diff --git a/.cspell/frigate-dictionary.txt b/.cspell/frigate-dictionary.txt
index f2bcf417af..6f2b2cc528 100644
--- a/.cspell/frigate-dictionary.txt
+++ b/.cspell/frigate-dictionary.txt
@@ -8,6 +8,7 @@ amdgpu
analyzeduration
Annke
apexcharts
+Aqara
arange
argmax
argmin
@@ -64,6 +65,7 @@ dsize
dtype
ECONNRESET
edgetpu
+Eufy
facenet
fastapi
faststart
@@ -82,6 +84,7 @@ frontdoor
fstype
fullchain
fullscreen
+gatekeep
genai
generativeai
genpts
@@ -162,6 +165,7 @@ mpegts
mqtt
mse
msenc
+muxing
namedtuples
nbytes
nchw
@@ -197,6 +201,8 @@ OWASP
paddleocr
paho
passwordless
+PCMA
+PCMU
popleft
posthog
postprocess
@@ -222,13 +228,16 @@ radeontop
rawvideo
rcond
RDONLY
+realmonitor
rebranded
+recvonly
referer
reindex
Reolink
restream
restreamed
restreaming
+RJSF
rkmpp
rknn
rkrga
@@ -238,8 +247,11 @@ rocminfo
rootfs
rtmp
RTSP
+rtsps
+rtspx
ruamel
scroller
+sendonly
setproctitle
setpts
shms
@@ -250,6 +262,7 @@ SNDMORE
socs
sqliteq
sqlitevecq
+Srtp
ssdlite
statm
stimeout
diff --git a/.github/DISCUSSION_TEMPLATE/beta-support.yml b/.github/DISCUSSION_TEMPLATE/beta-support.yml
index e342127a02..478d3e541d 100644
--- a/.github/DISCUSSION_TEMPLATE/beta-support.yml
+++ b/.github/DISCUSSION_TEMPLATE/beta-support.yml
@@ -10,7 +10,11 @@ body:
Before submitting, read the [beta documentation][docs].
- [docs]: https://deploy-preview-19787--frigate-docs.netlify.app/
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
+ [docs]: https://docs-dev.frigate.video/
+ [discussions]: https://github.com/blakeblackshear/frigate/discussions
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
@@ -22,8 +26,8 @@ body:
id: version
attributes:
label: Beta Version
- description: Visible on the System page in the Web UI. Please include the full version including the build identifier (eg. 0.17.0-beta1)
- placeholder: "0.17.0-beta1"
+ description: Visible on the System Metrics page in the Web UI. Please include the full version including the build identifier (eg. 0.18.0-beta1, 0.18.0-8b72c7a, etc.)
+ placeholder: "0.18.0-beta1"
validations:
required: true
- type: dropdown
@@ -71,11 +75,12 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
- Proxmox via Docker
- - Proxmox via TTeck Script
+ - Proxmox via installation script
+ - Proxomox via VM
- Windows WSL2
validations:
required: true
diff --git a/.github/DISCUSSION_TEMPLATE/camera-support.yml b/.github/DISCUSSION_TEMPLATE/camera-support.yml
index 521d65ded5..213eaab614 100644
--- a/.github/DISCUSSION_TEMPLATE/camera-support.yml
+++ b/.github/DISCUSSION_TEMPLATE/camera-support.yml
@@ -8,9 +8,12 @@ body:
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
@@ -87,11 +90,12 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
- Proxmox via Docker
- - Proxmox via TTeck Script
+ - Proxmox via installation script
+ - Proxomox via VM
- Windows WSL2
validations:
required: true
diff --git a/.github/DISCUSSION_TEMPLATE/config-support.yml b/.github/DISCUSSION_TEMPLATE/config-support.yml
index 575f7f640e..2aa1dfc25e 100644
--- a/.github/DISCUSSION_TEMPLATE/config-support.yml
+++ b/.github/DISCUSSION_TEMPLATE/config-support.yml
@@ -8,9 +8,12 @@ body:
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
@@ -73,11 +76,12 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
- Proxmox via Docker
- - Proxmox via TTeck Script
+ - Proxmox via installation script
+ - Proxomox via VM
- Windows WSL2
validations:
required: true
diff --git a/.github/DISCUSSION_TEMPLATE/detector-support.yml b/.github/DISCUSSION_TEMPLATE/detector-support.yml
index fb994500f4..9b6423d79e 100644
--- a/.github/DISCUSSION_TEMPLATE/detector-support.yml
+++ b/.github/DISCUSSION_TEMPLATE/detector-support.yml
@@ -8,9 +8,12 @@ body:
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
@@ -53,11 +56,12 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
- Proxmox via Docker
- - Proxmox via TTeck Script
+ - Proxmox via installation script
+ - Proxomox via VM
- Windows WSL2
validations:
required: true
diff --git a/.github/DISCUSSION_TEMPLATE/general-support.yml b/.github/DISCUSSION_TEMPLATE/general-support.yml
index 0b9f225b6b..7ccf4299b1 100644
--- a/.github/DISCUSSION_TEMPLATE/general-support.yml
+++ b/.github/DISCUSSION_TEMPLATE/general-support.yml
@@ -8,9 +8,12 @@ body:
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
@@ -73,11 +76,12 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
- Proxmox via Docker
- - Proxmox via TTeck Script
+ - Proxmox via installation script
+ - Proxmox via VM
- Windows WSL2
validations:
required: true
diff --git a/.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml b/.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml
index 861156696a..ad35e166d8 100644
--- a/.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml
+++ b/.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml
@@ -8,9 +8,12 @@ body:
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
@@ -69,11 +72,12 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
- Proxmox via Docker
- - Proxmox via TTeck Script
+ - Proxmox via installation script
+ - Proxomox via VM
- Windows WSL2
validations:
required: true
diff --git a/.github/DISCUSSION_TEMPLATE/question.yml b/.github/DISCUSSION_TEMPLATE/question.yml
index 6a4789c9c5..f4eee5caed 100644
--- a/.github/DISCUSSION_TEMPLATE/question.yml
+++ b/.github/DISCUSSION_TEMPLATE/question.yml
@@ -10,9 +10,12 @@ body:
**If you are looking for support, start a new discussion and use a support category.**
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: textarea
id: description
attributes:
diff --git a/.github/DISCUSSION_TEMPLATE/report-a-bug.yml b/.github/DISCUSSION_TEMPLATE/report-a-bug.yml
index de870ac0f7..ec28ceb66b 100644
--- a/.github/DISCUSSION_TEMPLATE/report-a-bug.yml
+++ b/.github/DISCUSSION_TEMPLATE/report-a-bug.yml
@@ -6,17 +6,20 @@ body:
value: |
Use this form to submit a reproducible bug in Frigate or Frigate's UI.
- **⚠️ If you are running a beta version (0.17.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
+ **⚠️ If you are running a beta version (0.18.0-beta or similar), please use the [Beta Support template](https://github.com/blakeblackshear/frigate/discussions/new?category=beta-support) instead.**
Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
**If you are unsure if your issue is actually a bug or not, please submit a support request first.**
+ By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
[prs]: https://www.github.com/blakeblackshear/frigate/pulls
[docs]: https://docs.frigate.video
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
[ai]: https://docs.frigate.video
+ [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
- type: checkboxes
attributes:
label: Checklist
@@ -116,9 +119,13 @@ body:
attributes:
label: Install method
options:
- - Home Assistant Add-on
+ - Home Assistant App
- Docker Compose
- Docker CLI
+ - Proxmox via Docker
+ - Proxmox via installation script
+ - Proxomox via VM
+ - Windows WSL2
validations:
required: true
- type: dropdown
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
index 57f76d3086..a9b84979d9 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -7,6 +7,13 @@ assignees: ''
---
+
+
**Describe what you are trying to accomplish and why in non technical terms**
I want to be able to ... so that I can ...
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
deleted file mode 100644
index f053abe3f2..0000000000
--- a/.github/copilot-instructions.md
+++ /dev/null
@@ -1,385 +0,0 @@
-# GitHub Copilot Instructions for Frigate NVR
-
-This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
-
-## Project Overview
-
-Frigate NVR is a realtime object detection system for IP cameras that uses:
-
-- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
-- **Frontend**: React with TypeScript, Vite, TailwindCSS
-- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
-- **Focus**: Minimal resource usage with maximum performance
-
-## Code Review Guidelines
-
-When reviewing code, do NOT comment on:
-
-- Missing imports - Static analysis tooling catches these
-- Code formatting - Ruff (Python) and Prettier (TypeScript/React) handle formatting
-- Minor style inconsistencies already enforced by linters
-
-## Python Backend Standards
-
-### Python Requirements
-
-- **Compatibility**: Python 3.13+
-- **Language Features**: Use modern Python features:
- - Pattern matching
- - Type hints (comprehensive typing preferred)
- - f-strings (preferred over `%` or `.format()`)
- - Dataclasses
- - Async/await patterns
-
-### Code Quality Standards
-
-- **Formatting**: Ruff (configured in `pyproject.toml`)
-- **Linting**: Ruff with rules defined in project config
-- **Type Checking**: Use type hints consistently
-- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
-- **Language**: American English for all code, comments, and documentation
-
-### Logging Standards
-
-- **Logger Pattern**: Use module-level logger
-
- ```python
- import logging
-
- logger = logging.getLogger(__name__)
- ```
-
-- **Format Guidelines**:
- - No periods at end of log messages
- - No sensitive data (keys, tokens, passwords)
- - Use lazy logging: `logger.debug("Message with %s", variable)`
-- **Log Levels**:
- - `debug`: Development and troubleshooting information
- - `info`: Important runtime events (startup, shutdown, state changes)
- - `warning`: Recoverable issues that should be addressed
- - `error`: Errors that affect functionality but don't crash the app
- - `exception`: Use in except blocks to include traceback
-
-### Error Handling
-
-- **Exception Types**: Choose most specific exception available
-- **Try/Catch Best Practices**:
- - Only wrap code that can throw exceptions
- - Keep try blocks minimal - process data after the try/except
- - Avoid bare exceptions except in background tasks
-
- Bad pattern:
-
- ```python
- try:
- data = await device.get_data() # Can throw
- # ❌ Don't process data inside try block
- processed = data.get("value", 0) * 100
- result = processed
- except DeviceError:
- logger.error("Failed to get data")
- ```
-
- Good pattern:
-
- ```python
- try:
- data = await device.get_data() # Can throw
- except DeviceError:
- logger.error("Failed to get data")
- return
-
- # ✅ Process data outside try block
- processed = data.get("value", 0) * 100
- result = processed
- ```
-
-### Async Programming
-
-- **External I/O**: All external I/O operations must be async
-- **Best Practices**:
- - Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
- - Avoid awaiting in loops - use `asyncio.gather()` instead
- - No blocking calls in async functions
- - Use `asyncio.create_task()` for background operations
-- **Thread Safety**: Use proper synchronization for shared state
-
-### Documentation Standards
-
-- **Module Docstrings**: Concise descriptions at top of files
- ```python
- """Utilities for motion detection and analysis."""
- ```
-- **Function Docstrings**: Required for public functions and methods
-
- ```python
- async def process_frame(frame: ndarray, config: Config) -> Detection:
- """Process a video frame for object detection.
-
- Args:
- frame: The video frame as numpy array
- config: Detection configuration
-
- Returns:
- Detection results with bounding boxes
- """
- ```
-
-- **Comment Style**:
- - Explain the "why" not just the "what"
- - Keep lines under 88 characters when possible
- - Use clear, descriptive comments
-
-### File Organization
-
-- **API Endpoints**: `frigate/api/` - FastAPI route handlers
-- **Configuration**: `frigate/config/` - Configuration parsing and validation
-- **Detectors**: `frigate/detectors/` - Object detection backends
-- **Events**: `frigate/events/` - Event management and storage
-- **Utilities**: `frigate/util/` - Shared utility functions
-
-## Frontend (React/TypeScript) Standards
-
-### Internationalization (i18n)
-
-- **CRITICAL**: Never write user-facing strings directly in components
-- **Always use react-i18next**: Import and use the `t()` function
-
- ```tsx
- import { useTranslation } from "react-i18next";
-
- function MyComponent() {
- const { t } = useTranslation(["views/live"]);
- return
{t("camera_not_found")}
;
- }
- ```
-
-- **Translation Files**: Add English strings to the appropriate json files in `web/public/locales/en`
-- **Namespaces**: Organize translations by feature/view (e.g., `views/live`, `common`, `views/system`)
-
-### Code Quality
-
-- **Linting**: ESLint (see `web/.eslintrc.cjs`)
-- **Formatting**: Prettier with Tailwind CSS plugin
-- **Type Safety**: TypeScript strict mode enabled
-- **Testing**: Vitest for unit tests
-
-### Component Patterns
-
-- **UI Components**: Use Radix UI primitives (in `web/src/components/ui/`)
-- **Styling**: TailwindCSS with `cn()` utility for class merging
-- **State Management**: React hooks (useState, useEffect, useCallback, useMemo)
-- **Data Fetching**: Custom hooks with proper loading and error states
-
-### ESLint Rules
-
-Key rules enforced:
-
-- `react-hooks/rules-of-hooks`: error
-- `react-hooks/exhaustive-deps`: error
-- `no-console`: error (use proper logging or remove)
-- `@typescript-eslint/no-explicit-any`: warn (always use proper types instead of `any`)
-- Unused variables must be prefixed with `_`
-- Comma dangles required for multiline objects/arrays
-
-### File Organization
-
-- **Pages**: `web/src/pages/` - Route components
-- **Views**: `web/src/views/` - Complex view components
-- **Components**: `web/src/components/` - Reusable components
-- **Hooks**: `web/src/hooks/` - Custom React hooks
-- **API**: `web/src/api/` - API client functions
-- **Types**: `web/src/types/` - TypeScript type definitions
-
-## Testing Requirements
-
-### Backend Testing
-
-- **Framework**: Python unittest
-- **Run Command**: `python3 -u -m unittest`
-- **Location**: `frigate/test/`
-- **Coverage**: Aim for comprehensive test coverage of core functionality
-- **Pattern**: Use `TestCase` classes with descriptive test method names
- ```python
- class TestMotionDetection(unittest.TestCase):
- def test_detects_motion_above_threshold(self):
- # Test implementation
- ```
-
-### Test Best Practices
-
-- Always have a way to test your work and confirm your changes
-- Write tests for bug fixes to prevent regressions
-- Test edge cases and error conditions
-- Mock external dependencies (cameras, APIs, hardware)
-- Use fixtures for test data
-
-## Development Commands
-
-### Python Backend
-
-```bash
-# Run all tests
-python3 -u -m unittest
-
-# Run specific test file
-python3 -u -m unittest frigate.test.test_ffmpeg_presets
-
-# Check formatting (Ruff)
-ruff format --check frigate/
-
-# Apply formatting
-ruff format frigate/
-
-# Run linter
-ruff check frigate/
-```
-
-### Frontend (from web/ directory)
-
-```bash
-# Start dev server (AI agents should never run this directly unless asked)
-npm run dev
-
-# Build for production
-npm run build
-
-# Run linter
-npm run lint
-
-# Fix linting issues
-npm run lint:fix
-
-# Format code
-npm run prettier:write
-```
-
-### Docker Development
-
-AI agents should never run these commands directly unless instructed.
-
-```bash
-# Build local image
-make local
-
-# Build debug image
-make debug
-```
-
-## Common Patterns
-
-### API Endpoint Pattern
-
-```python
-from fastapi import APIRouter, Request
-from frigate.api.defs.tags import Tags
-
-router = APIRouter(tags=[Tags.Events])
-
-@router.get("/events")
-async def get_events(request: Request, limit: int = 100):
- """Retrieve events from the database."""
- # Implementation
-```
-
-### Configuration Access
-
-```python
-# Access Frigate configuration
-config: FrigateConfig = request.app.frigate_config
-camera_config = config.cameras["front_door"]
-```
-
-### Database Queries
-
-```python
-from frigate.models import Event
-
-# Use Peewee ORM for database access
-events = (
- Event.select()
- .where(Event.camera == camera_name)
- .order_by(Event.start_time.desc())
- .limit(limit)
-)
-```
-
-## Common Anti-Patterns to Avoid
-
-### ❌ Avoid These
-
-```python
-# Blocking operations in async functions
-data = requests.get(url) # ❌ Use async HTTP client
-time.sleep(5) # ❌ Use asyncio.sleep()
-
-# Hardcoded strings in React components
-
Camera not found
# ❌ Use t("camera_not_found")
-
-# Missing error handling
-data = await api.get_data() # ❌ No exception handling
-
-# Bare exceptions in regular code
-try:
- value = await sensor.read()
-except Exception: # ❌ Too broad
- logger.error("Failed")
-```
-
-### ✅ Use These Instead
-
-```python
-# Async operations
-import aiohttp
-async with aiohttp.ClientSession() as session:
- async with session.get(url) as response:
- data = await response.json()
-
-await asyncio.sleep(5) # ✅ Non-blocking
-
-# Translatable strings in React
-const { t } = useTranslation();
-
{t("camera_not_found")}
# ✅ Translatable
-
-# Proper error handling
-try:
- data = await api.get_data()
-except ApiException as err:
- logger.error("API error: %s", err)
- raise
-
-# Specific exceptions
-try:
- value = await sensor.read()
-except SensorException as err: # ✅ Specific
- logger.exception("Failed to read sensor")
-```
-
-## Project-Specific Conventions
-
-### Configuration Files
-
-- Main config: `config/config.yml`
-
-### Directory Structure
-
-- Backend code: `frigate/`
-- Frontend code: `web/`
-- Docker files: `docker/`
-- Documentation: `docs/`
-- Database migrations: `migrations/`
-
-### Code Style Conformance
-
-Always conform new and refactored code to the existing coding style in the project:
-
-- Follow established patterns in similar files
-- Match indentation and formatting of surrounding code
-- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
-- Maintain the same level of verbosity in comments and docstrings
-
-## Additional Resources
-
-- Documentation: https://docs.frigate.video
-- Main Repository: https://github.com/blakeblackshear/frigate
-- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
new file mode 120000
index 0000000000..47dc3e3d86
--- /dev/null
+++ b/.github/copilot-instructions.md
@@ -0,0 +1 @@
+AGENTS.md
\ No newline at end of file
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 3204244a6c..841f97fb79 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,17 +1,18 @@
+_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) and the [AI policy](https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md) before submitting a PR. Every PR must be read and submitted by a person, and PRs that appear to be unreviewed AI output will be closed without review._
+
## Proposed change
+
-
## Type of change
- [ ] Dependency upgrade
@@ -25,6 +26,45 @@
- This PR fixes or closes issue: fixes #
- This PR is related to issue:
+- Link to discussion with maintainers (**required** for any large or "planned" features):
+
+## For new features
+
+
+
+- [ ] There is an existing feature request or discussion with community interest for this change.
+ - Link:
+
+## AI disclosure
+
+
+
+- [ ] No AI tools were used in this PR.
+- [ ] AI tools were used in this PR. Details below:
+
+**AI tool(s) used** (e.g., Claude, Copilot, ChatGPT, Cursor):
+
+**How AI was used** (e.g., code generation, code review, debugging, documentation):
+
+**Extent of AI involvement** (e.g., generated entire implementation, assisted with specific functions, suggested fixes):
+
+**Human oversight**: Describe what manual review, testing, and validation you performed on the AI-generated portions.
## Checklist
@@ -35,5 +75,6 @@
- [ ] The code change is tested and works locally.
- [ ] Local tests pass. **Your PR cannot be merged unless tests pass**
- [ ] There is no commented out code in this PR.
+- [ ] I can explain every line of code in this PR if asked.
- [ ] UI changes including text have used i18n keys and have been added to the `en` locale.
- [ ] The code has been formatted using Ruff (`ruff format frigate`)
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 54df536d64..41080be5d9 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -32,7 +32,7 @@ jobs:
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push amd64 standard build
- uses: docker/build-push-action@v5
+ uses: docker/build-push-action@v7
with:
context: .
file: docker/main/Dockerfile
@@ -56,7 +56,7 @@ jobs:
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push arm64 standard build
- uses: docker/build-push-action@v5
+ uses: docker/build-push-action@v7
with:
context: .
file: docker/main/Dockerfile
@@ -67,7 +67,7 @@ jobs:
${{ steps.setup.outputs.image-name }}-standard-arm64
cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
- name: Build and push RPi build
- uses: docker/bake-action@v6
+ uses: docker/bake-action@v7
with:
source: .
push: true
@@ -96,7 +96,7 @@ jobs:
BASE_IMAGE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
SLIM_BASE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
TRT_BASE: nvcr.io/nvidia/tensorrt:23.12-py3-igpu
- uses: docker/bake-action@v6
+ uses: docker/bake-action@v7
with:
source: .
push: true
@@ -124,7 +124,7 @@ jobs:
- name: Build and push TensorRT (x86 GPU)
env:
COMPUTE_LEVEL: "50 60 70 80 90"
- uses: docker/bake-action@v6
+ uses: docker/bake-action@v7
with:
source: .
push: true
@@ -137,7 +137,7 @@ jobs:
- name: AMD/ROCm general build
env:
HSA_OVERRIDE: 0
- uses: docker/bake-action@v6
+ uses: docker/bake-action@v7
with:
source: .
push: true
@@ -163,7 +163,7 @@ jobs:
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push Rockchip build
- uses: docker/bake-action@v6
+ uses: docker/bake-action@v7
with:
source: .
push: true
@@ -188,7 +188,7 @@ jobs:
with:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push Synaptics build
- uses: docker/bake-action@v6
+ uses: docker/bake-action@v7
with:
source: .
push: true
diff --git a/.github/workflows/pr_template_check.yml b/.github/workflows/pr_template_check.yml
new file mode 100644
index 0000000000..c82b202ef5
--- /dev/null
+++ b/.github/workflows/pr_template_check.yml
@@ -0,0 +1,120 @@
+name: PR template check
+
+on:
+ pull_request_target:
+ types: [opened, edited]
+
+permissions:
+ pull-requests: write
+
+jobs:
+ check_template:
+ name: Validate PR description
+ runs-on: ubuntu-latest
+ steps:
+ - name: Check PR description against template
+ uses: actions/github-script@v9
+ with:
+ script: |
+ const maintainers = ['blakeblackshear', 'NickM-27', 'hawkeye217', 'dependabot[bot]', 'weblate'];
+ const author = context.payload.pull_request.user.login;
+
+ if (maintainers.includes(author)) {
+ console.log(`Skipping template check for maintainer: ${author}`);
+ return;
+ }
+
+ const body = context.payload.pull_request.body || '';
+ const errors = [];
+
+ // Check that key template sections exist
+ const requiredSections = [
+ '## Proposed change',
+ '## Type of change',
+ '## AI disclosure',
+ '## Checklist',
+ ];
+
+ for (const section of requiredSections) {
+ if (!body.includes(section)) {
+ errors.push(`Missing section: **${section}**`);
+ }
+ }
+
+ // Check that "Proposed change" has content beyond the default HTML comment
+ const proposedChangeMatch = body.match(
+ /## Proposed change\s*(?:\s*)?([\s\S]*?)(?=\n## )/
+ );
+ const proposedContent = proposedChangeMatch
+ ? proposedChangeMatch[1].trim()
+ : '';
+ if (!proposedContent) {
+ errors.push(
+ 'The **Proposed change** section is empty. Please describe what this PR does.'
+ );
+ }
+
+ // Check that at least one "Type of change" checkbox is checked
+ const typeSection = body.match(
+ /## Type of change\s*([\s\S]*?)(?=\n## )/
+ );
+ if (typeSection && !/- \[x\]/i.test(typeSection[1])) {
+ errors.push(
+ 'No **Type of change** selected. Please check at least one option.'
+ );
+ }
+
+ // Check that at least one AI disclosure checkbox is checked
+ const aiSection = body.match(
+ /## AI disclosure\s*([\s\S]*?)(?=\n## )/
+ );
+ if (aiSection && !/- \[x\]/i.test(aiSection[1])) {
+ errors.push(
+ 'No **AI disclosure** option selected. Please indicate whether AI tools were used.'
+ );
+ }
+
+ // Check that at least one checklist item is checked
+ const checklistSection = body.match(
+ /## Checklist\s*([\s\S]*?)$/
+ );
+ if (checklistSection && !/- \[x\]/i.test(checklistSection[1])) {
+ errors.push(
+ 'No **Checklist** items checked. Please review and check the items that apply.'
+ );
+ }
+
+ if (errors.length === 0) {
+ console.log('PR description passes template validation.');
+ return;
+ }
+
+ const prNumber = context.payload.pull_request.number;
+ const message = [
+ '## PR template validation failed',
+ '',
+ 'This PR was automatically closed because the description does not follow the [pull request template](https://github.com/blakeblackshear/frigate/blob/dev/.github/pull_request_template.md).',
+ '',
+ '**Issues found:**',
+ ...errors.map((e) => `- ${e}`),
+ '',
+ 'Please update your PR description to include all required sections from the template, then reopen this PR.',
+ '',
+ '> If you used an AI tool to generate this PR, please see our [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) for details.',
+ ].join('\n');
+
+ await github.rest.issues.createComment({
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ issue_number: prNumber,
+ body: message,
+ });
+
+ await github.rest.pulls.update({
+ owner: context.repo.owner,
+ repo: context.repo.repo,
+ pull_number: prNumber,
+ state: 'closed',
+ });
+
+ core.setFailed('PR description does not follow the template.');
diff --git a/.github/workflows/pull_request.yml b/.github/workflows/pull_request.yml
index c4d8aa7a03..d2e279966e 100644
--- a/.github/workflows/pull_request.yml
+++ b/.github/workflows/pull_request.yml
@@ -27,6 +27,9 @@ jobs:
- name: Lint
run: npm run lint
working-directory: ./web
+ - name: Check i18n keys
+ run: npm run i18n:extract:ci
+ working-directory: ./web
web_test:
name: Web - Test
@@ -47,6 +50,37 @@ jobs:
# run: npm run test
# working-directory: ./web
+ web_e2e:
+ name: Web - E2E Tests
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v6
+ with:
+ persist-credentials: false
+ - uses: actions/setup-node@v6
+ with:
+ node-version: 20.x
+ - run: npm install
+ working-directory: ./web
+ - name: Install Playwright Chromium
+ run: npx playwright install chromium --with-deps
+ working-directory: ./web
+ - name: Build web for E2E
+ run: npm run e2e:build
+ working-directory: ./web
+ - name: Run E2E tests
+ run: npm run e2e
+ working-directory: ./web
+ - name: Upload test artifacts
+ uses: actions/upload-artifact@v7
+ if: failure()
+ with:
+ name: playwright-report
+ path: |
+ web/test-results/
+ web/playwright-report/
+ retention-days: 7
+
python_checks:
runs-on: ubuntu-latest
name: Python Checks
@@ -91,5 +125,7 @@ jobs:
run: devcontainer up --workspace-folder .
- name: Run mypy in devcontainer
run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m mypy --config-file frigate/mypy.ini frigate"
+ - name: Check API spec is up to date
+ run: devcontainer exec --workspace-folder . bash -lc "python3 generate_api_auth_spec.py --check"
- name: Run unit tests in devcontainer
run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m unittest"
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
index 011f70afd3..39512f24a3 100644
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -18,9 +18,9 @@ jobs:
close-issue-message: ""
days-before-stale: 30
days-before-close: 3
- exempt-draft-pr: true
- exempt-issue-labels: "pinned,security"
- exempt-pr-labels: "pinned,security,dependencies"
+ exempt-draft-pr: false
+ exempt-issue-labels: "planned,security"
+ exempt-pr-labels: "planned,security,dependencies"
operations-per-run: 120
- name: Print outputs
env:
diff --git a/.gitignore b/.gitignore
index 660a378b01..70ec5ae76f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,8 @@ __pycache__
.mypy_cache
*.swp
debug
+.claude/*
+.mcp.json
.vscode/*
!.vscode/launch.json
config/*
@@ -10,6 +12,7 @@ config/*
models
*.mp4
*.db
+*.db-*
*.csv
frigate/version.py
web/build
@@ -19,4 +22,9 @@ web/.env
core
!/web/**/*.ts
.idea/*
-.ipynb_checkpoints
\ No newline at end of file
+.ipynb_checkpoints
+
+# Auto-generated Docker Compose Generator config files
+docs/src/components/DockerComposeGenerator/config/devices.ts
+docs/src/components/DockerComposeGenerator/config/hardware.ts
+docs/src/components/DockerComposeGenerator/config/ports.ts
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 5c858267d8..2d7b6c8fb5 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -6,6 +6,23 @@
"type": "debugpy",
"request": "launch",
"module": "frigate"
+ },
+ {
+ "type": "editor-browser",
+ "request": "launch",
+ "name": "Vite: Launch in integrated browser",
+ "url": "http://localhost:5173"
+ },
+ {
+ "type": "editor-browser",
+ "request": "launch",
+ "name": "Nginx: Launch in integrated browser",
+ "url": "http://localhost:5000"
+ },
+ {
+ "type": "editor-browser",
+ "request": "attach",
+ "name": "Attach to integrated browser"
}
]
}
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000000..41d4b6460b
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,450 @@
+# Agent Instructions for Frigate NVR
+
+This document provides coding guidelines and best practices for contributing to Frigate NVR, a complete and local NVR designed for Home Assistant with AI object detection.
+
+## Project Overview
+
+Frigate NVR is a realtime object detection system for IP cameras that uses:
+
+- **Backend**: Python 3.13+ with FastAPI, OpenCV, TensorFlow/ONNX
+- **Frontend**: React with TypeScript, Vite, TailwindCSS
+- **Architecture**: Multiprocessing design with ZMQ and MQTT communication
+- **Focus**: Minimal resource usage with maximum performance
+
+## Code Review Guidelines
+
+When reviewing code, do NOT comment on:
+
+- Missing imports - Static analysis tooling catches these
+- Code formatting - Ruff (Python) and Prettier (TypeScript/React) handle formatting
+- Minor style inconsistencies already enforced by linters
+
+## Python Backend Standards
+
+### Python Requirements
+
+- **Compatibility**: Python 3.13+
+- **Language Features**: Use modern Python features:
+ - Pattern matching
+ - Type hints (comprehensive typing preferred)
+ - f-strings (preferred over `%` or `.format()`)
+ - Dataclasses
+ - Async/await patterns
+
+### Code Quality Standards
+
+- **Formatting**: Ruff (configured in `pyproject.toml`)
+- **Linting**: Ruff with rules defined in project config
+- **Type Checking**: Use type hints consistently
+- **Testing**: unittest framework - use `python3 -u -m unittest` to run tests
+- **Language**: American English for all code, comments, and documentation
+- **Punctuation**: Do not use em dashes in documentation, comments, or strings; reword with standard punctuation (commas, colons, parentheses, or separate sentences)
+
+### Logging Standards
+
+- **Logger Pattern**: Use module-level logger
+
+ ```python
+ import logging
+
+ logger = logging.getLogger(__name__)
+ ```
+
+- **Format Guidelines**:
+ - No periods at end of log messages
+ - No sensitive data (keys, tokens, passwords)
+ - Use lazy logging: `logger.debug("Message with %s", variable)`
+- **Log Levels**:
+ - `debug`: Development and troubleshooting information
+ - `info`: Important runtime events (startup, shutdown, state changes)
+ - `warning`: Recoverable issues that should be addressed
+ - `error`: Errors that affect functionality but don't crash the app
+ - `exception`: Use in except blocks to include traceback
+
+### Error Handling
+
+- **Exception Types**: Choose most specific exception available
+- **Try/Catch Best Practices**:
+ - Only wrap code that can throw exceptions
+ - Keep try blocks minimal - process data after the try/except
+ - Avoid bare exceptions except in background tasks
+
+ Bad pattern:
+
+ ```python
+ try:
+ data = await device.get_data() # Can throw
+ # ❌ Don't process data inside try block
+ processed = data.get("value", 0) * 100
+ result = processed
+ except DeviceError:
+ logger.error("Failed to get data")
+ ```
+
+ Good pattern:
+
+ ```python
+ try:
+ data = await device.get_data() # Can throw
+ except DeviceError:
+ logger.error("Failed to get data")
+ return
+
+ # ✅ Process data outside try block
+ processed = data.get("value", 0) * 100
+ result = processed
+ ```
+
+### Async Programming
+
+- **External I/O**: All external I/O operations must be async
+- **Best Practices**:
+ - Avoid sleeping in loops - use `asyncio.sleep()` not `time.sleep()`
+ - Avoid awaiting in loops - use `asyncio.gather()` instead
+ - No blocking calls in async functions
+ - Use `asyncio.create_task()` for background operations
+- **Thread Safety**: Use proper synchronization for shared state
+
+### Documentation Standards
+
+- **Module Docstrings**: Concise descriptions at top of files
+ ```python
+ """Utilities for motion detection and analysis."""
+ ```
+- **Function Docstrings**: Required for public functions and methods
+
+ ```python
+ async def process_frame(frame: ndarray, config: Config) -> Detection:
+ """Process a video frame for object detection.
+
+ Args:
+ frame: The video frame as numpy array
+ config: Detection configuration
+
+ Returns:
+ Detection results with bounding boxes
+ """
+ ```
+
+- **Comment Style**:
+ - Explain the "why" not just the "what"
+ - Keep lines under 88 characters when possible
+ - Use clear, descriptive comments
+
+### File Organization
+
+- **API Endpoints**: `frigate/api/` - FastAPI route handlers
+- **Configuration**: `frigate/config/` - Configuration parsing and validation
+- **Detectors**: `frigate/detectors/` - Object detection backends
+- **Events**: `frigate/events/` - Event management and storage
+- **Utilities**: `frigate/util/` - Shared utility functions
+
+## Frontend (React/TypeScript) Standards
+
+### Internationalization (i18n)
+
+- **CRITICAL**: Never write user-facing strings directly in components
+- **Always use react-i18next**: Import and use the `t()` function
+
+ ```tsx
+ import { useTranslation } from "react-i18next";
+
+ function MyComponent() {
+ const { t } = useTranslation(["views/live"]);
+ return
{t("camera_not_found")}
;
+ }
+ ```
+
+- **Translation Files**: Add English strings to the appropriate json files in `web/public/locales/en`
+- **Namespaces**: Organize translations by feature/view (e.g., `views/live`, `common`, `views/system`)
+
+### Code Quality
+
+- **Linting**: ESLint (see `web/.eslintrc.cjs`)
+- **Formatting**: Prettier with Tailwind CSS plugin
+- **Type Safety**: TypeScript strict mode enabled
+
+### Component Patterns
+
+- **UI Components**: Use Radix UI primitives (in `web/src/components/ui/`)
+- **Styling**: TailwindCSS with `cn()` utility for class merging
+- **State Management**: React hooks (useState, useEffect, useCallback, useMemo)
+- **Data Fetching**: Custom hooks with proper loading and error states
+
+### ESLint Rules
+
+Key rules enforced:
+
+- `react-hooks/rules-of-hooks`: error
+- `react-hooks/exhaustive-deps`: error
+- `no-console`: error (use proper logging or remove)
+- `@typescript-eslint/no-explicit-any`: warn (always use proper types instead of `any`)
+- Unused variables must be prefixed with `_`
+- Comma dangles required for multiline objects/arrays
+
+### File Organization
+
+- **Pages**: `web/src/pages/` - Route components
+- **Views**: `web/src/views/` - Complex view components
+- **Components**: `web/src/components/` - Reusable components
+- **Hooks**: `web/src/hooks/` - Custom React hooks
+- **API**: `web/src/api/` - API client functions
+- **Types**: `web/src/types/` - TypeScript type definitions
+
+## Testing Requirements
+
+### Backend Testing
+
+- **Framework**: Python unittest
+- **Run Command**: `python3 -u -m unittest`
+- **Location**: `frigate/test/`
+- **Coverage**: Aim for comprehensive test coverage of core functionality
+- **Pattern**: Use `TestCase` classes with descriptive test method names
+ ```python
+ class TestMotionDetection(unittest.TestCase):
+ def test_detects_motion_above_threshold(self):
+ # Test implementation
+ ```
+
+### Test Best Practices
+
+- Always have a way to test your work and confirm your changes
+- Write tests for bug fixes to prevent regressions
+- Test edge cases and error conditions
+- Mock external dependencies (cameras, APIs, hardware)
+- Use fixtures for test data
+
+## Development Commands
+
+### Python Backend
+
+```bash
+# Run all tests
+python3 -u -m unittest
+
+# Run specific test file
+python3 -u -m unittest frigate.test.test_ffmpeg_presets
+
+# Check formatting (Ruff)
+ruff format --check frigate/
+
+# Apply formatting
+ruff format frigate/
+
+# Run linter
+ruff check frigate/
+
+# Type check
+python3 -u -m mypy --config-file frigate/mypy.ini frigate
+
+# Regenerate the OpenAPI spec after adding, changing, or removing an API
+# endpoint or its auth dependency — outputs docs/static/frigate-api.yaml,
+# annotated with each endpoint's auth requirement (admin / any / camera /
+# public). NEVER edit that file by hand. CI runs the --check variant and fails
+# if it is out of date. (from repo root)
+python3 generate_api_auth_spec.py
+python3 generate_api_auth_spec.py --check
+```
+
+### Frontend (from web/ directory)
+
+```bash
+# Start dev server (AI agents should never run this directly unless asked)
+npm run dev
+
+# Build for production
+npm run build
+
+# Run linter
+npm run lint
+
+# Fix linting issues
+npm run lint:fix
+
+# Format code
+npm run prettier:write
+
+# E2E: first-time setup
+npm install
+npx playwright install chromium
+
+# E2E: build the app and run all tests
+npm run e2e:build && npm run e2e
+
+# E2E: interactive UI for debugging
+npm run e2e:ui
+
+# E2E: run a specific spec
+npx playwright test --config e2e/playwright.config.ts e2e/specs/live.spec.ts
+
+# E2E: filter by name, or run only desktop/mobile
+npx playwright test --config e2e/playwright.config.ts --grep="severity tab"
+npx playwright test --config e2e/playwright.config.ts --project=desktop
+
+# E2E: regenerate mock data after backend model changes (from repo root)
+PYTHONPATH=. python3 web/e2e/fixtures/mock-data/generate-mock-data.py
+
+# Regenerate config translations from Pydantic models — outputs to
+# web/public/locales/en/config/{global,cameras}.json. NEVER edit those
+# JSON files by hand; change the Pydantic field title/description and
+# re-run this script. (from repo root)
+python3 generate_config_translations.py
+
+# Extract i18n keys from source into the locale files after adding
+# new t() calls. Use the :ci variant to verify the locale files are
+# in sync with source (fails if extraction would change anything).
+npm run i18n:extract
+npm run i18n:extract:ci
+```
+
+### Docker Development
+
+AI agents should never run these commands directly unless instructed.
+
+```bash
+# Build local image
+make local
+
+# Build debug image
+make debug
+```
+
+## Common Patterns
+
+### API Endpoint Pattern
+
+```python
+from fastapi import APIRouter, Request
+from frigate.api.defs.tags import Tags
+
+router = APIRouter(tags=[Tags.Events])
+
+@router.get("/events")
+async def get_events(request: Request, limit: int = 100):
+ """Retrieve events from the database."""
+ # Implementation
+```
+
+After adding, changing, or removing an endpoint (or its auth dependency), regenerate the OpenAPI spec with `python3 generate_api_auth_spec.py` so `docs/static/frigate-api.yaml` stays in sync and the endpoint's auth requirement is documented. CI enforces this via the `--check` variant; never edit that file by hand.
+
+### Configuration Access
+
+```python
+# Access Frigate configuration
+config: FrigateConfig = request.app.frigate_config
+camera_config = config.cameras["front_door"]
+```
+
+### Database Queries
+
+```python
+from frigate.models import Event
+
+# Use Peewee ORM for database access
+events = (
+ Event.select()
+ .where(Event.camera == camera_name)
+ .order_by(Event.start_time.desc())
+ .limit(limit)
+)
+```
+
+## Common Anti-Patterns to Avoid
+
+### ❌ Avoid These
+
+```python
+# Blocking operations in async functions
+data = requests.get(url) # ❌ Use async HTTP client
+time.sleep(5) # ❌ Use asyncio.sleep()
+
+# Hardcoded strings in React components
+
Camera not found
# ❌ Use t("camera_not_found")
+
+# Missing error handling
+data = await api.get_data() # ❌ No exception handling
+
+# Bare exceptions in regular code
+try:
+ value = await sensor.read()
+except Exception: # ❌ Too broad
+ logger.error("Failed")
+
+# Returning exceptions in JSON responses
+except ValueError as e:
+ return JSONResponse(
+ content={"success": False, "message": str(e)},
+ )
+```
+
+### ✅ Use These Instead
+
+```python
+# Async operations
+import aiohttp
+async with aiohttp.ClientSession() as session:
+ async with session.get(url) as response:
+ data = await response.json()
+
+await asyncio.sleep(5) # ✅ Non-blocking
+
+# Translatable strings in React
+const { t } = useTranslation();
+
{t("camera_not_found")}
# ✅ Translatable
+
+# Proper error handling
+try:
+ data = await api.get_data()
+except ApiException as err:
+ logger.error("API error: %s", err)
+ raise
+
+# Specific exceptions
+try:
+ value = await sensor.read()
+except SensorException as err: # ✅ Specific
+ logger.exception("Failed to read sensor")
+
+# Safe error responses
+except ValueError:
+ logger.exception("Invalid parameters for API request")
+ return JSONResponse(
+ content={
+ "success": False,
+ "message": "Invalid request parameters",
+ },
+ )
+```
+
+## WebSocket Broadcasts
+
+Outbound WebSocket broadcasts go through a per-recipient classifier in `frigate/comms/ws.py` that enforces camera-level access. **The classifier is fail-closed: any topic it doesn't recognize is dropped for every client.** New outbound topics must be classified there or they'll silently disappear.
+
+## Project-Specific Conventions
+
+### Configuration Files
+
+- Main config: `config/config.yml`
+
+### Directory Structure
+
+- Backend code: `frigate/`
+- Frontend code: `web/`
+- Docker files: `docker/`
+- Documentation: `docs/`
+- Database migrations: `migrations/`
+
+### Code Style Conformance
+
+Always conform new and refactored code to the existing coding style in the project:
+
+- Follow established patterns in similar files
+- Match indentation and formatting of surrounding code
+- Use consistent naming conventions (snake_case for Python, camelCase for TypeScript)
+- Maintain the same level of verbosity in comments and docstrings
+
+## Additional Resources
+
+- Documentation: https://docs.frigate.video
+- Main Repository: https://github.com/blakeblackshear/frigate
+- Home Assistant Integration: https://github.com/blakeblackshear/frigate-hass-integration
diff --git a/AI_POLICY.md b/AI_POLICY.md
new file mode 100644
index 0000000000..de6dc602f1
--- /dev/null
+++ b/AI_POLICY.md
@@ -0,0 +1,126 @@
+# Frigate AI Policy
+
+## TL;DR
+
+- **Use AI tools if they help you.** We do too. This is about what you post, not which tools you use to write it.
+- **A person has to read it and send it.** Don't wire a bot or an agent up to post on your behalf.
+- **Write your posts yourself.** Your own words, the template filled in, and you answering maintainers rather than your assistant.
+- **Don't paste an AI's guess at the cause as though it were a diagnosis.** Tell us what you actually observed.
+- **Read your code before you submit it.** Disclose that AI was used, and be ready to explain every line.
+- **If we misjudge something you wrote, just say so.** We'll take you at your word.
+
+The rest of this document explains each of these, and why.
+
+## Scope
+
+AI tools are a reality of modern development and we're not opposed to their use. You are responsible for anything you submit, however it was produced, and we are responsible for anything we merge and release. We hold a high bar for both.
+
+This policy applies everywhere this project is discussed: issues, discussions, pull requests, code reviews, and commit comments.
+
+## Why this exists
+
+Frigate is built and supported by a small group of maintainers and a community of volunteers who read every post and review every pull request. Nobody here is paid to do it, and time spent reading a post is time not spent fixing bugs or building features.
+
+We're not opposed to AI tools. We use them too. But content generated by an AI and submitted without review costs a real person real time, and usually gives them less to work with than a few honest sentences would have. That is the problem this policy addresses.
+
+## A person has to be in the loop
+
+Every issue, discussion, comment, and pull request here must be read and submitted by a person. Using an AI tool to help you write is fine. Wiring one up to post on your behalf is not.
+
+Specifically, do not:
+
+- Connect a bot or agent to GitHub that opens issues, discussions, or pull requests without you reading them first
+- Post output from a tool you have not read
+- Use tooling to file bulk or drive-by contributions across the repository
+
+We will close anything we believe was posted without a person reading it, and we may mark it as spam. Posts that skip the templates are the most common sign of this.
+
+## Issues, discussions, and comments
+
+We do not mind if you use AI tools to help you write. Do not have tools post unreviewed content on your behalf. We may hide any comment we believe to be unreviewed AI output.
+
+Keep posts to what is needed to communicate your point. A long, confidently written, AI-padded post is harder to help with than a short direct one, not easier, and it is usually obvious.
+
+**Describe your actual problem in your own words.** Tell us what you did, what you expected, and what actually happened. That is the information we need, and only you have it.
+
+**Do not paste an AI's guess at the cause as though it were a diagnosis.** It is frequently wrong in ways that send everyone down the wrong path, and it buries the details that would have led to the real answer. We would rather see what you observed than what a model inferred.
+
+**Fill in the template completely.** The templates ask for logs, config, version, and hardware because those are the things needed to help you. An AI cannot supply them for you, and a post missing them cannot be acted on.
+
+**Answer maintainers yourself.** If we ask you a question, we are asking _you_, not your AI assistant. These are the spaces where we build trust and understanding with the community, and that only works if we're talking to each other. Using AI to fix your grammar or clarity is fine, but the substance has to be yours.
+
+This applies to pull request descriptions and review replies as much as it does to bug reports and discussions.
+
+### Quoting AI output
+
+If you want to include something an AI told you, it must be:
+
+- In a quote block, using `>`
+- Disclosed as AI output, saying which tool it came from
+- Accompanied by your own comment explaining why you think it is relevant
+
+Keep the excerpt short. Do not paste long transcripts.
+
+### Non-native English speakers
+
+AI is genuinely useful for participating in a project that operates in English, and we would rather hear from you through a translation tool than not hear from you at all. Using AI to improve the grammar or clarity of something you wrote yourself is fine.
+
+If you are translating your posts, make sure the translation says what you meant. Including your original text in a `` block helps us verify the translation if something reads oddly, and keeps the thread readable.
+
+## Code contributions
+
+We need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
+
+Because of the long-term maintenance burden every merged change creates, we require a human in the loop who understands the work the AI produced. Pull requests that appear to be unreviewed AI output will be closed without review.
+
+### Requirements when AI is used
+
+If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
+
+1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest, this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
+2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
+3. **Be prepared to explain every line of code you submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
+4. **Check for an existing pull request addressing the same change.** If one exists, comment there and work with its author instead of opening a duplicate.
+5. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
+
+### Established contributors
+
+Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption, it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
+
+### What this means in practice
+
+We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
+
+Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term, often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
+
+One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated, where the author can't explain the design, debug issues independently, or engage substantively in design discussions, doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
+
+## Our use of AI
+
+The Frigate documentation site has an "Ask AI" search that answers questions from the docs, and we may use AI tooling to help with triage and project management. Like any automated tooling, it is not always right.
+
+If an AI tool leaves a comment on your contribution, treat it the way you would any other comment. If you think it is wrong, say so, and a brief explanation is enough. Maintainers always have the final say.
+
+## Enforcement
+
+Contributions and posts that do not follow this policy will be closed. Depending on the situation, maintainers may also:
+
+- Hide or delete comments that appear to be unreviewed AI output
+- Mark automated content as spam
+- Close an issue, discussion, or pull request without further review
+- Lock a conversation
+- Temporarily or permanently block an account from participating in the project
+
+Repeated violations may result in being blocked from contributing to Frigate.
+
+### When we get it wrong
+
+There is no reliable way to detect this, and we're not going to pretend otherwise. Whether something reads as unreviewed AI output is a judgment call, usually made quickly, by a volunteer with limited time and no way to know for certain. These calls are subjective and we won't always get them right.
+
+If it happens to you, just say so. A short reply telling us you wrote it yourself is enough, and we'll take you at your word and pick the conversation back up. We would much rather occasionally reopen something we misjudged than treat everyone who posts here as a suspect.
+
+We'd ask for some understanding in return. These calls get made quickly because the volume is real, and time spent second-guessing them is time not spent helping the person in the next thread.
+
+## Attribution
+
+Portions of this policy are adapted from the [Open Home Foundation AI Policy](https://developers.home-assistant.io/docs/ai_policy/).
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 120000
index 0000000000..47dc3e3d86
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1 @@
+AGENTS.md
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000000..c94423d687
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,135 @@
+# Contributing to Frigate
+
+Thank you for your interest in contributing to Frigate. This document covers the expectations and guidelines for contributions. Please read it before submitting a pull request.
+
+All participation in this project, including pull requests, issues, and discussions, is covered by our [AI policy](AI_POLICY.md).
+
+## Before you start
+
+### Bugfixes
+
+If you've found a bug and want to fix it, go for it. Link to the relevant issue in your PR if one exists, or describe the bug in the PR description.
+
+### New features
+
+A pull request is more than just code — it's a request for the maintainers to review, integrate, and support the change long-term. We're selective about what we take on, and prioritize changes that align with the project's direction and can be responsibly maintained in the long term.
+
+**Large or highly-requested features** raise the bar even higher. Popularity signals demand, but it doesn't pre-approve any particular implementation. The bigger the change, the higher the long-term cost, and the more important it is that we're aligned on scope and approach before any code is written. A large PR that lands without prior discussion is unlikely to be merged as-is, no matter how well it's implemented.
+
+Before writing code for a new feature:
+
+1. **Check for existing discussion.** Search [feature requests](https://github.com/blakeblackshear/frigate/issues) and [discussions](https://github.com/blakeblackshear/frigate/discussions) to see if it's been proposed or discussed. Feature requests tagged with "planned" are on our radar — we plan to get to them, but we don't maintain a public roadmap or timeline. Check in with us first if you have interest in contributing to one.
+2. **Start a discussion or feature request first.** This helps ensure your idea aligns with Frigate's direction before you invest time building it. Community interest in a feature request helps us gauge demand, though a great idea is a great idea even without a crowd behind it.
+
+## AI usage policy
+
+AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting, and we need to hear from you rather than from your AI assistant.
+
+**Read the [AI policy](AI_POLICY.md) before you open a pull request.** It is short, and it applies to everything you post here. The parts that most often catch people out:
+
+- A person has to be in the loop. Don't wire a bot or agent up to open pull requests, issues, or discussions on your behalf.
+- Disclose how AI was used. The PR template asks for this. Be honest, it won't automatically disqualify your PR.
+- Review and test everything you submit, and be prepared to explain every line when asked.
+- Don't use AI to write your PR description or your replies to maintainers.
+
+Pull requests that appear to be unreviewed AI output will be closed without review.
+
+## Pull request guidelines
+
+### Before submitting
+
+- **Search for existing PRs** to avoid duplicating effort.
+- **Test your changes locally.** Your PR cannot be merged unless tests pass.
+- **Format your code.** Run `ruff format frigate` for Python and `npm run prettier:write` from the `web/` directory for frontend changes.
+- **Run the linter.** Run `ruff check frigate` for Python and `npm run lint` from `web/` for frontend.
+- **One concern per PR.** Don't combine unrelated changes. A bugfix and a new feature should be separate PRs.
+
+### What we look for in review
+
+- **Does it work?** Tested locally, tests pass, no regressions.
+- **Is it maintainable?** Clear code, appropriate complexity, good separation of concerns.
+- **Does it fit?** Consistent with Frigate's architecture and design philosophy.
+- **Is it scoped well?** Solves the stated problem without unnecessary additions.
+
+### After submitting
+
+- Be responsive to review feedback. We may ask for changes.
+- Expect honest, direct feedback. We try to be respectful but we also try to be efficient.
+- If your PR goes stale, rebase it on the latest `dev` branch.
+
+## Coding standards
+
+### Python (backend)
+
+- **Python** — use modern language features (type hints, pattern matching, f-strings, dataclasses)
+- **Formatting**: Ruff (configured in `pyproject.toml`)
+- **Linting**: Ruff
+- **Testing**: `python3 -u -m unittest`
+- **Logging**: Use module-level `logger = logging.getLogger(__name__)` with lazy formatting
+- **Async**: All external I/O must be async. No blocking calls in async functions.
+- **Error handling**: Use specific exception types. Keep try blocks minimal.
+- **Language**: American English for all code, comments, and documentation
+
+### TypeScript/React (frontend)
+
+- **Linting**: ESLint (`npm run lint` from `web/`)
+- **Formatting**: Prettier (`npm run prettier:write` from `web/`)
+- **Type safety**: TypeScript strict mode. Avoid `any`.
+- **i18n**: All user-facing strings must use `react-i18next`. Never hardcode display text in components. Add English strings to the appropriate files in `web/public/locales/en/`.
+- **Components**: Use Radix UI/shadcn primitives and TailwindCSS with the `cn()` utility.
+
+### Development commands
+
+```bash
+# Python
+python3 -u -m unittest # Run all tests
+python3 -u -m unittest frigate.test.test_ffmpeg_presets # Run specific test
+ruff format frigate # Format
+ruff check frigate # Lint
+
+# Frontend (from web/ directory)
+npm run build # Build
+npm run lint # Lint
+npm run lint:fix # Lint + fix
+npm run prettier:write # Format
+```
+
+## Project structure
+
+```
+frigate/ # Python backend
+ api/ # FastAPI route handlers
+ config/ # Configuration parsing and validation
+ detectors/ # Object detection backends
+ events/ # Event management and storage
+ test/ # Backend tests
+ util/ # Shared utilities
+web/ # React/TypeScript frontend
+ src/
+ api/ # API client functions
+ components/ # Reusable components
+ hooks/ # Custom React hooks
+ pages/ # Route components
+ types/ # TypeScript type definitions
+ views/ # Complex view components
+docker/ # Docker build files
+docs/ # Documentation site
+migrations/ # Database migrations
+```
+
+## Translations
+
+Frigate uses [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) for managing language translations. If you'd like to help translate Frigate into your language:
+
+1. Visit the [Frigate project on Weblate](https://hosted.weblate.org/projects/frigate-nvr/).
+2. Create an account or log in.
+3. Browse the available languages and select the one you'd like to contribute to, or request a new language.
+4. Translate strings directly in the Weblate interface — no code changes or pull requests needed.
+
+Translation contributions through Weblate are automatically synced to the repository. Please do not submit pull requests for translation changes — use Weblate instead so that translations are properly tracked and coordinated.
+
+## Resources
+
+- [Documentation](https://docs.frigate.video)
+- [Discussions, Support, and Bug Reports](https://github.com/blakeblackshear/frigate/discussions)
+- [Feature Requests](https://github.com/blakeblackshear/frigate/issues)
diff --git a/Makefile b/Makefile
index 42adb6bacc..3800399ea1 100644
--- a/Makefile
+++ b/Makefile
@@ -1,7 +1,7 @@
default_target: local
COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.17.2
+VERSION = 0.18.0
IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
BOARDS= #Initialized empty
@@ -49,7 +49,8 @@ push: push-boards
--push
run: local
- docker run --rm --publish=5000:5000 --volume=${PWD}/config:/config frigate:latest
+ docker run --rm --publish=5000:5000 --publish=8971:8971 \
+ --volume=${PWD}/config:/config frigate:latest
run_tests: local
docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \
diff --git a/audio-labelmap.txt b/audio-labelmap.txt
index 4a38b5f639..a303c3b7a8 100644
--- a/audio-labelmap.txt
+++ b/audio-labelmap.txt
@@ -24,7 +24,7 @@ yell
sigh
singing
choir
-sodeling
+yodeling
chant
mantra
child_singing
diff --git a/docker-compose.yml b/docker-compose.yml
index db63297d5e..1563057bb9 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -14,6 +14,8 @@ services:
dockerfile: docker/main/Dockerfile
# Use target devcontainer-trt for TensorRT dev
target: devcontainer
+ cache_from:
+ - ghcr.io/blakeblackshear/frigate:cache-amd64
## Uncomment this block for nvidia gpu support
# deploy:
# resources:
diff --git a/docker/main/Dockerfile b/docker/main/Dockerfile
index 055a1458f8..bf17146e30 100644
--- a/docker/main/Dockerfile
+++ b/docker/main/Dockerfile
@@ -52,10 +52,18 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
--mount=type=cache,target=/root/.ccache \
/deps/build_sqlite_vec.sh
+# Build intel-media-driver from source against bookworm's system libva so it
+# works with Debian 12's glibc/libstdc++ (pre-built noble/trixie packages
+# require glibc 2.38 which is not available on bookworm).
+FROM base AS intel-media-driver
+ARG DEBIAN_FRONTEND
+RUN --mount=type=bind,source=docker/main/build_intel_media_driver.sh,target=/deps/build_intel_media_driver.sh \
+ /deps/build_intel_media_driver.sh
+
FROM scratch AS go2rtc
ARG TARGETARCH
WORKDIR /rootfs/usr/local/go2rtc/bin
-ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.10/go2rtc_linux_${TARGETARCH}" go2rtc
+ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.14/go2rtc_linux_${TARGETARCH}" go2rtc
FROM wget AS tempio
ARG TARGETARCH
@@ -73,10 +81,10 @@ RUN --mount=type=bind,source=docker/main/install_tempio.sh,target=/deps/install_
FROM base_host AS ov-converter
ARG DEBIAN_FRONTEND
-# Install OpenVino Runtime and Dev library
+# Install OpenVINO for model conversion
COPY docker/main/requirements-ov.txt /requirements-ov.txt
RUN apt-get -qq update \
- && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
+ && apt-get -qq install -y wget python3 python3-distutils \
&& wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
&& sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
&& python3 get-pip.py "pip" \
@@ -200,6 +208,7 @@ RUN --mount=type=bind,source=docker/main/install_hailort.sh,target=/deps/install
FROM scratch AS deps-rootfs
COPY --from=nginx /usr/local/nginx/ /usr/local/nginx/
COPY --from=sqlite-vec /usr/local/lib/ /usr/local/lib/
+COPY --from=intel-media-driver /rootfs/ /
COPY --from=go2rtc /rootfs/ /
COPY --from=libusb-build /usr/local/lib /usr/local/lib
COPY --from=tempio /rootfs/ /
@@ -256,8 +265,8 @@ ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PA
RUN --mount=type=bind,source=docker/main/install_deps.sh,target=/deps/install_deps.sh \
/deps/install_deps.sh
-ENV DEFAULT_FFMPEG_VERSION="7.0"
-ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0"
+ENV DEFAULT_FFMPEG_VERSION="8.0"
+ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:7.0:5.0"
RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
&& sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
@@ -266,6 +275,12 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
pip3 install -U /deps/wheels/*.whl
+# Install Axera Engine
+RUN pip3 install https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3-frigate/axengine-0.1.3-py3-none-any.whl
+
+ENV PATH="${PATH}:/usr/bin/axcl"
+ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/lib/axcl"
+
# Install MemryX runtime (requires libgomp (OpenMP) in the final docker image)
RUN --mount=type=bind,source=docker/main/install_memryx.sh,target=/deps/install_memryx.sh \
bash -c "bash /deps/install_memryx.sh"
diff --git a/docker/main/build_intel_media_driver.sh b/docker/main/build_intel_media_driver.sh
new file mode 100755
index 0000000000..acc9caf09d
--- /dev/null
+++ b/docker/main/build_intel_media_driver.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+set -euxo pipefail
+
+# Intel media driver is x86_64-only. Create empty rootfs on other arches so
+# the downstream COPY --from has a valid source.
+if [ "$(uname -m)" != "x86_64" ]; then
+ mkdir -p /rootfs
+ exit 0
+fi
+
+MEDIA_DRIVER_VERSION="intel-media-25.2.6"
+GMMLIB_VERSION="intel-gmmlib-22.7.2"
+
+apt-get -qq update
+apt-get -qq install -y wget gnupg ca-certificates cmake g++ make pkg-config
+
+# Use Intel's jammy repo for newer libva-dev (2.22) which provides the
+# VVC/VVC-decode headers required by media-driver 25.x
+wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
+echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" > /etc/apt/sources.list.d/intel-gpu-jammy.list
+apt-get -qq update
+apt-get -qq install -y libva-dev
+
+# Build gmmlib (required by media-driver)
+wget -qO gmmlib.tar.gz "https://github.com/intel/gmmlib/archive/refs/tags/${GMMLIB_VERSION}.tar.gz"
+mkdir /tmp/gmmlib
+tar -xf gmmlib.tar.gz -C /tmp/gmmlib --strip-components 1
+cmake -S /tmp/gmmlib -B /tmp/gmmlib/build -DCMAKE_BUILD_TYPE=Release
+make -C /tmp/gmmlib/build -j"$(nproc)"
+make -C /tmp/gmmlib/build install
+
+# Build intel-media-driver
+wget -qO media-driver.tar.gz "https://github.com/intel/media-driver/archive/refs/tags/${MEDIA_DRIVER_VERSION}.tar.gz"
+mkdir /tmp/media-driver
+tar -xf media-driver.tar.gz -C /tmp/media-driver --strip-components 1
+cmake -S /tmp/media-driver -B /tmp/media-driver/build \
+ -DCMAKE_BUILD_TYPE=Release \
+ -DENABLE_KERNELS=ON \
+ -DENABLE_NONFREE_KERNELS=ON \
+ -DCMAKE_INSTALL_PREFIX=/usr \
+ -DCMAKE_INSTALL_LIBDIR=/usr/lib/x86_64-linux-gnu \
+ -DCMAKE_C_FLAGS="-Wno-error" \
+ -DCMAKE_CXX_FLAGS="-Wno-error"
+make -C /tmp/media-driver/build -j"$(nproc)"
+
+# Install driver to rootfs for COPY --from
+make -C /tmp/media-driver/build install DESTDIR=/rootfs
diff --git a/docker/main/build_nginx.sh b/docker/main/build_nginx.sh
index 6066826651..708a4cb45c 100755
--- a/docker/main/build_nginx.sh
+++ b/docker/main/build_nginx.sh
@@ -73,6 +73,7 @@ cd /tmp/nginx
--with-file-aio \
--with-http_sub_module \
--with-http_ssl_module \
+ --with-http_v2_module \
--with-http_auth_request_module \
--with-http_realip_module \
--with-threads \
diff --git a/docker/main/build_ov_model.py b/docker/main/build_ov_model.py
index 2888d87a85..f585078f3d 100644
--- a/docker/main/build_ov_model.py
+++ b/docker/main/build_ov_model.py
@@ -1,11 +1,106 @@
-import openvino as ov
-from openvino.tools import mo
+"""Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
-ov_model = mo.convert_model(
- "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
- compress_to_fp16=True,
- transformations_config="/usr/local/lib/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
- tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
- reverse_input_channels=True,
+Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
+frontend translates the Object Detection API pre and post processors literally,
+producing per-class NonMaxSuppression, NonZero ops and map loops with data
+dependent shapes that the GPU plugin handles very badly. Both are cut out the
+way ssd_v2_support.json used to do it: the preprocessor is an identity at the
+native 300x300 input, and the postprocessor becomes a single fused
+DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
+OpenVINO detector expects, with the input flipped to BGR to match the legacy
+reverse_input_channels behavior.
+"""
+
+import numpy as np
+import openvino as ov
+from openvino import opset8 as ops
+from openvino.preprocess import PrePostProcessor
+
+MODEL_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
+OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
+INPUT_SHAPE = [1, 300, 300, 3]
+
+# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
+# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
+BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
+
+model = ov.convert_model(
+ f"{MODEL_DIR}/frozen_inference_graph.pb",
+ input=[("image_tensor:0", INPUT_SHAPE)],
)
-ov.save_model(ov_model, "/models/ssdlite_mobilenet_v2.xml")
+
+nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
+parameter = model.get_parameters()[0]
+
+preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
+box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
+class_scores = nodes["Postprocessor/convert_scores"].output(0)
+anchors_output = nodes["Postprocessor/Reshape"].output(0)
+
+# The anchors only depend on the static input shape, so fold them into a
+# constant and drop the generator subgraph with the rest of the postprocessor.
+probe = ov.Core().compile_model(
+ ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
+)
+probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
+anchors, resized = (out.copy() for out in probe([probe_input]).values())
+
+assert np.allclose(resized, probe_input, atol=1e-3), (
+ "preprocessor is not an identity at 300x300, it cannot be bypassed"
+)
+
+image = ops.convert(parameter, "f32")
+
+for consumer in list(preprocessor.output(0).get_target_inputs()):
+ consumer.replace_source_output(image.output(0))
+
+# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
+priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
+variances = np.tile(BOX_VARIANCES, len(anchors))
+proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
+
+# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
+box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
+class_preds = ops.reshape(class_scores, [1, -1], False)
+
+detections = ops.detection_output(
+ box_logits,
+ class_preds,
+ proposals,
+ {
+ "background_label_id": 0,
+ "top_k": 100,
+ "keep_top_k": [100],
+ "nms_threshold": 0.6,
+ "confidence_threshold": 0.3,
+ "code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
+ "share_location": True,
+ "variance_encoded_in_target": False,
+ "normalized": True,
+ "clip_before_nms": False,
+ "clip_after_nms": True,
+ "decrease_label_id": False,
+ },
+)
+detections.output(0).get_tensor().set_names({"detection_out"})
+
+model = ov.Model([detections], [parameter], "ssdlite_mobilenet_v2")
+
+ppp = PrePostProcessor(model)
+ppp.input().tensor().set_layout(ov.Layout("NHWC"))
+ppp.input().preprocess().reverse_channels()
+model = ppp.build()
+
+# Fail the build rather than silently ship the dynamically shaped graph again.
+op_types = [op.get_type_name() for op in model.get_ordered_ops()]
+assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
+
+for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
+ assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
+
+output_shape = model.outputs[0].get_partial_shape()
+assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
+ f"unexpected detector output shape {output_shape}"
+)
+
+ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)
diff --git a/docker/main/build_sqlite_vec.sh b/docker/main/build_sqlite_vec.sh
index b41f3383d9..8036c6f522 100755
--- a/docker/main/build_sqlite_vec.sh
+++ b/docker/main/build_sqlite_vec.sh
@@ -2,7 +2,7 @@
set -euxo pipefail
-SQLITE_VEC_VERSION="0.1.3"
+SQLITE_VEC_VERSION="0.1.9"
source /etc/os-release
diff --git a/docker/main/install_deps.sh b/docker/main/install_deps.sh
index 330caff9f5..e197ce1b68 100755
--- a/docker/main/install_deps.sh
+++ b/docker/main/install_deps.sh
@@ -55,6 +55,10 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
rm -rf ffmpeg.tar.xz
+ mkdir -p /usr/lib/ffmpeg/8.0
+ wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linux64-gpl-8.1.tar.xz"
+ tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/8.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
+ rm -rf ffmpeg.tar.xz
fi
# ffmpeg -> arm64
@@ -67,6 +71,10 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linuxarm64-gpl-7.0.tar.xz"
tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
rm -f ffmpeg.tar.xz
+ mkdir -p /usr/lib/ffmpeg/8.0
+ wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-06-02-14-20/ffmpeg-n8.1.1-9-g58d4114d36-linuxarm64-gpl-8.1.tar.xz"
+ tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/8.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
+ rm -f ffmpeg.tar.xz
fi
# arch specific packages
@@ -87,46 +95,60 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
# intel packages use zst compression so we need to update dpkg
apt-get install -y dpkg
- # use intel apt intel packages
+ # use intel apt repo for libmfx1 (legacy QSV, pre-Gen12)
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
apt-get -qq update
- apt-get -qq install --no-install-recommends --no-install-suggests -y \
- intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2
+ # intel-media-va-driver-non-free is built from source in the
+ # intel-media-driver Dockerfile stage for Battlemage (Xe2) support
+ apt-get -qq install --no-install-recommends --no-install-suggests -y \
+ libmfx1
+ rm -f /usr/share/keyrings/intel-graphics.gpg
+ rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
+
+ # upgrade libva2, oneVPL runtime, and libvpl2 from trixie for Battlemage support
+ echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.d/trixie.list
+ apt-get -qq update
+ apt-get -qq install -y -t trixie libva2 libva-drm2 libzstd1
+ apt-get -qq install -y -t trixie libmfx-gen1.2 libvpl2
+ rm -f /etc/apt/sources.list.d/trixie.list
+ apt-get -qq update
apt-get -qq install -y ocl-icd-libopencl1
# install libtbb12 for NPU support
apt-get -qq install -y libtbb12
- rm -f /usr/share/keyrings/intel-graphics.gpg
- rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
-
- # install legacy and standard intel icd and level-zero-gpu
+ # install legacy and standard intel compute packages
# see https://github.com/intel/compute-runtime/blob/master/LEGACY_PLATFORMS.md for more info
# needed core package
- wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/libigdgmm12_22.5.5_amd64.deb
- dpkg -i libigdgmm12_22.5.5_amd64.deb
- rm libigdgmm12_22.5.5_amd64.deb
+ wget https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/libigdgmm12_22.9.0_amd64.deb
+ dpkg -i libigdgmm12_22.9.0_amd64.deb
+ rm libigdgmm12_22.9.0_amd64.deb
- # legacy packages
+ # legacy compute-runtime packages
wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-level-zero-gpu-legacy1_1.5.30872.36_amd64.deb
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-opencl_1.0.17537.24_amd64.deb
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-core_1.0.17537.24_amd64.deb
- # standard packages
- wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/intel-opencl-icd_24.52.32224.5_amd64.deb
- wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/intel-level-zero-gpu_1.6.32224.5_amd64.deb
- wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-opencl-2_2.5.6+18417_amd64.deb
- wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-core-2_2.5.6+18417_amd64.deb
+ # standard compute-runtime packages
+ wget https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/intel-opencl-icd_26.14.37833.4-0_amd64.deb
+ wget https://github.com/intel/compute-runtime/releases/download/26.14.37833.4/libze-intel-gpu1_26.14.37833.4-0_amd64.deb
+ wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.32.7/intel-igc-opencl-2_2.32.7+21184_amd64.deb
+ wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.32.7/intel-igc-core-2_2.32.7+21184_amd64.deb
# npu packages
- wget https://github.com/oneapi-src/level-zero/releases/download/v1.21.9/level-zero_1.21.9+u22.04_amd64.deb
- wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-driver-compiler-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
- wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-fw-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
- wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-level-zero-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
+ wget https://github.com/oneapi-src/level-zero/releases/download/v1.28.2/level-zero_1.28.2+u22.04_amd64.deb
+ wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-driver-compiler-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
+ wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-fw-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
+ wget https://github.com/intel/linux-npu-driver/releases/download/v1.19.0/intel-level-zero-npu_1.19.0.20250707-16111289554_ubuntu22.04_amd64.deb
dpkg -i *.deb
rm *.deb
+ apt-get -qq install -f -y
+
+ # Battlemage uses the xe kernel driver, but the VA-API driver is still iHD.
+ # The oneVPL runtime may look for a driver named after the kernel module.
+ ln -sf /usr/lib/x86_64-linux-gnu/dri/iHD_drv_video.so /usr/lib/x86_64-linux-gnu/dri/xe_drv_video.so
fi
if [[ "${TARGETARCH}" == "arm64" ]]; then
diff --git a/docker/main/requirements-dev.txt b/docker/main/requirements-dev.txt
index ac9d357583..df5818fe0d 100644
--- a/docker/main/requirements-dev.txt
+++ b/docker/main/requirements-dev.txt
@@ -1,4 +1,4 @@
-ruff
+ruff == 0.15.20
# types
types-peewee == 3.17.*
diff --git a/docker/main/requirements-ov.txt b/docker/main/requirements-ov.txt
index 6fd1ca55d9..2df7890dd5 100644
--- a/docker/main/requirements-ov.txt
+++ b/docker/main/requirements-ov.txt
@@ -1,3 +1,2 @@
numpy
-tensorflow
-openvino-dev>=2024.0.0
\ No newline at end of file
+openvino >= 2026.2.0
diff --git a/docker/main/requirements-wheels.txt b/docker/main/requirements-wheels.txt
index f81fefea47..5ed9664fc2 100644
--- a/docker/main/requirements-wheels.txt
+++ b/docker/main/requirements-wheels.txt
@@ -11,7 +11,7 @@ joserfc == 1.2.*
cryptography == 44.0.*
pathvalidate == 3.3.*
markupsafe == 3.0.*
-python-multipart == 0.0.20
+python-multipart == 0.0.26
# Classification Model Training
tensorflow == 2.19.* ; platform_machine == 'aarch64'
tensorflow-cpu == 2.19.* ; platform_machine == 'x86_64'
@@ -42,7 +42,7 @@ opencv-python-headless == 4.11.0.*
opencv-contrib-python == 4.11.0.*
scipy == 1.16.*
# OpenVino & ONNX
-openvino == 2025.3.*
+openvino == 2025.4.*
onnxruntime == 1.22.*
# Embeddings
transformers == 4.45.*
@@ -79,7 +79,5 @@ sherpa-onnx==1.12.*
faster-whisper==1.1.*
librosa==0.11.*
soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*
# Memory profiling
memray == 1.15.*
diff --git a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/certsync/run b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/certsync/run
index 4ce1c133f5..b834c09bbf 100755
--- a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/certsync/run
+++ b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/certsync/run
@@ -10,7 +10,8 @@ echo "[INFO] Starting certsync..."
lefile="/etc/letsencrypt/live/frigate/fullchain.pem"
-tls_enabled=`python3 /usr/local/nginx/get_listen_settings.py | jq -r .tls.enabled`
+tls_enabled=`python3 /usr/local/nginx/get_nginx_settings.py | jq -r .tls.enabled`
+listen_external_port=`python3 /usr/local/nginx/get_nginx_settings.py | jq -r .listen.external_port`
while true
do
@@ -34,7 +35,7 @@ do
;;
esac
- liveprint=`echo | openssl s_client -showcerts -connect 127.0.0.1:8971 2>&1 | openssl x509 -fingerprint 2>&1 | grep -i fingerprint || echo 'failed'`
+ liveprint=`echo | openssl s_client -showcerts -connect 127.0.0.1:$listen_external_port 2>&1 | openssl x509 -fingerprint 2>&1 | grep -i fingerprint || echo 'failed'`
case "$liveprint" in
*Fingerprint*)
@@ -55,4 +56,4 @@ do
done
-exit 0
\ No newline at end of file
+exit 0
diff --git a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/nginx/run b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/nginx/run
index 8bd9b5250f..a3c7b32484 100755
--- a/docker/main/rootfs/etc/s6-overlay/s6-rc.d/nginx/run
+++ b/docker/main/rootfs/etc/s6-overlay/s6-rc.d/nginx/run
@@ -80,14 +80,14 @@ if [ ! \( -f "$letsencrypt_path/privkey.pem" -a -f "$letsencrypt_path/fullchain.
fi
# build templates for optional FRIGATE_BASE_PATH environment variable
-python3 /usr/local/nginx/get_base_path.py | \
+python3 /usr/local/nginx/get_nginx_settings.py | \
tempio -template /usr/local/nginx/templates/base_path.gotmpl \
- -out /usr/local/nginx/conf/base_path.conf
+ -out /usr/local/nginx/conf/base_path.conf
-# build templates for optional TLS support
-python3 /usr/local/nginx/get_listen_settings.py | \
- tempio -template /usr/local/nginx/templates/listen.gotmpl \
- -out /usr/local/nginx/conf/listen.conf
+# build templates for additional network settings
+python3 /usr/local/nginx/get_nginx_settings.py | \
+ tempio -template /usr/local/nginx/templates/listen.gotmpl \
+ -out /usr/local/nginx/conf/listen.conf
# Replace the bash process with the NGINX process, redirecting stderr to stdout
exec 2>&1
diff --git a/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py b/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py
index 0f492cc5c5..9f4d08f2e3 100644
--- a/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py
+++ b/docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py
@@ -5,11 +5,7 @@ from typing import Any
from ruamel.yaml import YAML
sys.path.insert(0, "/opt/frigate")
-from frigate.const import (
- DEFAULT_FFMPEG_VERSION,
- INCLUDED_FFMPEG_VERSIONS,
-)
-from frigate.util.config import find_config_file
+from frigate.util.config import find_config_file, resolve_ffmpeg_path
sys.path.remove("/opt/frigate")
@@ -29,9 +25,4 @@ except FileNotFoundError:
config: dict[str, Any] = {}
path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
- print(f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg")
-elif path in INCLUDED_FFMPEG_VERSIONS:
- print(f"/usr/lib/ffmpeg/{path}/bin/ffmpeg")
-else:
- print(f"{path}/bin/ffmpeg")
+print(resolve_ffmpeg_path(path, "ffmpeg"))
diff --git a/docker/main/rootfs/usr/local/go2rtc/create_config.py b/docker/main/rootfs/usr/local/go2rtc/create_config.py
index dfd8b722ca..70cb744f13 100644
--- a/docker/main/rootfs/usr/local/go2rtc/create_config.py
+++ b/docker/main/rootfs/usr/local/go2rtc/create_config.py
@@ -9,14 +9,13 @@ from typing import Any
from ruamel.yaml import YAML
sys.path.insert(0, "/opt/frigate")
+from frigate.config.env import substitute_frigate_vars
from frigate.const import (
BIRDSEYE_PIPE,
- DEFAULT_FFMPEG_VERSION,
- INCLUDED_FFMPEG_VERSIONS,
LIBAVFORMAT_VERSION_MAJOR,
)
from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
-from frigate.util.config import find_config_file
+from frigate.util.config import find_config_file, resolve_ffmpeg_path
from frigate.util.services import (
is_go2rtc_arbitrary_exec_allowed,
is_restricted_go2rtc_source,
@@ -82,23 +81,18 @@ if go2rtc_config["webrtc"].get("candidates") is None:
go2rtc_config["webrtc"]["candidates"] = default_candidates
if go2rtc_config.get("rtsp", {}).get("username") is not None:
- go2rtc_config["rtsp"]["username"] = go2rtc_config["rtsp"]["username"].format(
- **FRIGATE_ENV_VARS
+ go2rtc_config["rtsp"]["username"] = substitute_frigate_vars(
+ go2rtc_config["rtsp"]["username"]
)
if go2rtc_config.get("rtsp", {}).get("password") is not None:
- go2rtc_config["rtsp"]["password"] = go2rtc_config["rtsp"]["password"].format(
- **FRIGATE_ENV_VARS
+ go2rtc_config["rtsp"]["password"] = substitute_frigate_vars(
+ go2rtc_config["rtsp"]["password"]
)
# ensure ffmpeg path is set correctly
path = config.get("ffmpeg", {}).get("path", "default")
-if path == "default":
- ffmpeg_path = f"/usr/lib/ffmpeg/{DEFAULT_FFMPEG_VERSION}/bin/ffmpeg"
-elif path in INCLUDED_FFMPEG_VERSIONS:
- ffmpeg_path = f"/usr/lib/ffmpeg/{path}/bin/ffmpeg"
-else:
- ffmpeg_path = f"{path}/bin/ffmpeg"
+ffmpeg_path = resolve_ffmpeg_path(path, "ffmpeg")
if go2rtc_config.get("ffmpeg") is None:
go2rtc_config["ffmpeg"] = {"bin": ffmpeg_path}
diff --git a/docker/main/rootfs/usr/local/nginx/conf/nginx.conf b/docker/main/rootfs/usr/local/nginx/conf/nginx.conf
index f6b0928eb9..cad314ae35 100644
--- a/docker/main/rootfs/usr/local/nginx/conf/nginx.conf
+++ b/docker/main/rootfs/usr/local/nginx/conf/nginx.conf
@@ -63,6 +63,9 @@ http {
server {
include listen.conf;
+ # enable HTTP/2 for TLS connections to eliminate browser 6-connection limit
+ http2 on;
+
# vod settings
vod_base_url '';
vod_segments_base_url '';
@@ -147,7 +150,9 @@ http {
include auth_request.conf;
types {
video/mp4 mp4;
- image/jpeg jpg;
+ image/jpeg jpg jpeg;
+ image/png png;
+ image/webp webp;
}
expires 7d;
@@ -224,16 +229,6 @@ http {
include proxy.conf;
}
- # frontend uses this to fetch the version
- location /api/go2rtc/api {
- include auth_request.conf;
- limit_except GET {
- deny all;
- }
- proxy_pass http://go2rtc/api;
- include proxy.conf;
- }
-
# integration uses this to add webrtc candidate
location /api/go2rtc/webrtc {
include auth_request.conf;
@@ -281,6 +276,13 @@ http {
include proxy.conf;
}
+ location /api/logout {
+ auth_request off;
+ rewrite ^/api(/.*)$ $1 break;
+ proxy_pass http://frigate_api;
+ include proxy.conf;
+ }
+
# Allow unauthenticated access to the first_time_login endpoint
# so the login page can load help text before authentication.
location /api/auth/first_time_login {
diff --git a/docker/main/rootfs/usr/local/nginx/get_base_path.py b/docker/main/rootfs/usr/local/nginx/get_base_path.py
deleted file mode 100644
index 2e78a7de9e..0000000000
--- a/docker/main/rootfs/usr/local/nginx/get_base_path.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""Prints the base path as json to stdout."""
-
-import json
-import os
-from typing import Any
-
-base_path = os.environ.get("FRIGATE_BASE_PATH", "")
-
-result: dict[str, Any] = {"base_path": base_path}
-
-print(json.dumps(result))
diff --git a/docker/main/rootfs/usr/local/nginx/get_listen_settings.py b/docker/main/rootfs/usr/local/nginx/get_listen_settings.py
deleted file mode 100644
index d879db56e4..0000000000
--- a/docker/main/rootfs/usr/local/nginx/get_listen_settings.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Prints the tls config as json to stdout."""
-
-import json
-import sys
-from typing import Any
-
-from ruamel.yaml import YAML
-
-sys.path.insert(0, "/opt/frigate")
-from frigate.util.config import find_config_file
-
-sys.path.remove("/opt/frigate")
-
-yaml = YAML()
-
-config_file = find_config_file()
-
-try:
- with open(config_file) as f:
- raw_config = f.read()
-
- if config_file.endswith((".yaml", ".yml")):
- config: dict[str, Any] = yaml.load(raw_config)
- elif config_file.endswith(".json"):
- config: dict[str, Any] = json.loads(raw_config)
-except FileNotFoundError:
- config: dict[str, Any] = {}
-
-tls_config: dict[str, any] = config.get("tls", {"enabled": True})
-networking_config = config.get("networking", {})
-ipv6_config = networking_config.get("ipv6", {"enabled": False})
-
-output = {"tls": tls_config, "ipv6": ipv6_config}
-
-print(json.dumps(output))
diff --git a/docker/main/rootfs/usr/local/nginx/get_nginx_settings.py b/docker/main/rootfs/usr/local/nginx/get_nginx_settings.py
new file mode 100644
index 0000000000..79cda36860
--- /dev/null
+++ b/docker/main/rootfs/usr/local/nginx/get_nginx_settings.py
@@ -0,0 +1,62 @@
+"""Prints the nginx settings as json to stdout."""
+
+import json
+import os
+import sys
+from typing import Any
+
+from ruamel.yaml import YAML
+
+sys.path.insert(0, "/opt/frigate")
+from frigate.util.config import find_config_file
+
+sys.path.remove("/opt/frigate")
+
+yaml = YAML()
+
+config_file = find_config_file()
+
+try:
+ with open(config_file) as f:
+ raw_config = f.read()
+
+ if config_file.endswith((".yaml", ".yml")):
+ config: dict[str, Any] = yaml.load(raw_config)
+ elif config_file.endswith(".json"):
+ config: dict[str, Any] = json.loads(raw_config)
+except FileNotFoundError:
+ config: dict[str, Any] = {}
+
+tls_config: dict[str, Any] = config.get("tls", {})
+tls_config.setdefault("enabled", True)
+
+networking_config: dict[str, Any] = config.get("networking", {})
+ipv6_config: dict[str, Any] = networking_config.get("ipv6", {})
+ipv6_config.setdefault("enabled", False)
+
+listen_config: dict[str, Any] = networking_config.get("listen", {})
+listen_config.setdefault("internal", 5000)
+listen_config.setdefault("external", 8971)
+
+# handle case where internal port is a string with ip:port
+internal_port = listen_config["internal"]
+if type(internal_port) is str:
+ internal_port = int(internal_port.split(":")[-1])
+listen_config["internal_port"] = internal_port
+
+# handle case where external port is a string with ip:port
+external_port = listen_config["external"]
+if type(external_port) is str:
+ external_port = int(external_port.split(":")[-1])
+listen_config["external_port"] = external_port
+
+base_path = os.environ.get("FRIGATE_BASE_PATH", "")
+
+result: dict[str, Any] = {
+ "tls": tls_config,
+ "ipv6": ipv6_config,
+ "listen": listen_config,
+ "base_path": base_path,
+}
+
+print(json.dumps(result))
diff --git a/docker/main/rootfs/usr/local/nginx/templates/base_path.gotmpl b/docker/main/rootfs/usr/local/nginx/templates/base_path.gotmpl
index ace4443ee5..ca945ba1fe 100644
--- a/docker/main/rootfs/usr/local/nginx/templates/base_path.gotmpl
+++ b/docker/main/rootfs/usr/local/nginx/templates/base_path.gotmpl
@@ -7,7 +7,7 @@ location ^~ {{ .base_path }}/ {
# remove base_url from the path before passing upstream
rewrite ^{{ .base_path }}/(.*) /$1 break;
- proxy_pass $scheme://127.0.0.1:8971;
+ proxy_pass $scheme://127.0.0.1:{{ .listen.external_port }};
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
diff --git a/docker/main/rootfs/usr/local/nginx/templates/listen.gotmpl b/docker/main/rootfs/usr/local/nginx/templates/listen.gotmpl
index 066f872cb9..628784b609 100644
--- a/docker/main/rootfs/usr/local/nginx/templates/listen.gotmpl
+++ b/docker/main/rootfs/usr/local/nginx/templates/listen.gotmpl
@@ -1,45 +1,36 @@
-
# Internal (IPv4 always; IPv6 optional)
-listen 5000;
-{{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:5000;{{ end }}{{ end }}
-
+listen {{ .listen.internal }};
+{{ if .ipv6.enabled }}listen [::]:{{ .listen.internal_port }};{{ end }}
# intended for external traffic, protected by auth
-{{ if .tls }}
- {{ if .tls.enabled }}
- # external HTTPS (IPv4 always; IPv6 optional)
- listen 8971 ssl;
- {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971 ssl;{{ end }}{{ end }}
+{{ if .tls.enabled }}
+ # external HTTPS (IPv4 always; IPv6 optional)
+ listen {{ .listen.external }} ssl;
+ {{ if .ipv6.enabled }}listen [::]:{{ .listen.external_port }} ssl;{{ end }}
- ssl_certificate /etc/letsencrypt/live/frigate/fullchain.pem;
- ssl_certificate_key /etc/letsencrypt/live/frigate/privkey.pem;
+ ssl_certificate /etc/letsencrypt/live/frigate/fullchain.pem;
+ ssl_certificate_key /etc/letsencrypt/live/frigate/privkey.pem;
- # generated 2024-06-01, Mozilla Guideline v5.7, nginx 1.25.3, OpenSSL 1.1.1w, modern configuration, no OCSP
- # https://ssl-config.mozilla.org/#server=nginx&version=1.25.3&config=modern&openssl=1.1.1w&ocsp=false&guideline=5.7
- ssl_session_timeout 1d;
- ssl_session_cache shared:MozSSL:10m; # about 40000 sessions
- ssl_session_tickets off;
+ # generated 2024-06-01, Mozilla Guideline v5.7, nginx 1.25.3, OpenSSL 1.1.1w, modern configuration, no OCSP
+ # https://ssl-config.mozilla.org/#server=nginx&version=1.25.3&config=modern&openssl=1.1.1w&ocsp=false&guideline=5.7
+ ssl_session_timeout 1d;
+ ssl_session_cache shared:MozSSL:10m; # about 40000 sessions
+ ssl_session_tickets off;
- # modern configuration
- ssl_protocols TLSv1.3;
- ssl_prefer_server_ciphers off;
+ # modern configuration
+ ssl_protocols TLSv1.3;
+ ssl_prefer_server_ciphers off;
- # HSTS (ngx_http_headers_module is required) (63072000 seconds)
- add_header Strict-Transport-Security "max-age=63072000" always;
+ # HSTS (ngx_http_headers_module is required) (63072000 seconds)
+ add_header Strict-Transport-Security "max-age=63072000" always;
- # ACME challenge location
- location /.well-known/acme-challenge/ {
- default_type "text/plain";
- root /etc/letsencrypt/www;
- }
- {{ else }}
- # external HTTP (IPv4 always; IPv6 optional)
- listen 8971;
- {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971;{{ end }}{{ end }}
- {{ end }}
+ # ACME challenge location
+ location /.well-known/acme-challenge/ {
+ default_type "text/plain";
+ root /etc/letsencrypt/www;
+ }
{{ else }}
- # (No tls section) default to HTTP (IPv4 always; IPv6 optional)
- listen 8971;
- {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971;{{ end }}{{ end }}
+ # (No tls) default to HTTP (IPv4 always; IPv6 optional)
+ listen {{ .listen.external }};
+ {{ if .ipv6.enabled }}listen [::]:{{ .listen.external_port }};{{ end }}
{{ end }}
-
diff --git a/docker/rockchip/conv2rknn.py b/docker/rockchip/conv2rknn.py
index 4880d98684..700f35689f 100644
--- a/docker/rockchip/conv2rknn.py
+++ b/docker/rockchip/conv2rknn.py
@@ -11,10 +11,10 @@ except FileNotFoundError:
pass
try:
- with open("/config/conv2rknn.yaml", "r") as config_file:
+ with open("/config/conv2rknn.yaml") as config_file:
configuration = yaml.safe_load(config_file)
except FileNotFoundError:
- raise Exception("Please place a config file at /config/conv2rknn.yaml")
+ raise Exception("Please place a config file at /config/conv2rknn.yaml") from None
if configuration["config"] != None:
rknn_config = configuration["config"]
@@ -31,7 +31,7 @@ if "soc" not in configuration:
with open("/proc/device-tree/compatible") as file:
soc = file.read().split(",")[-1].strip("\x00")
except FileNotFoundError:
- raise Exception("Make sure to run docker in privileged mode.")
+ raise Exception("Make sure to run docker in privileged mode.") from None
configuration["soc"] = [
soc,
diff --git a/docker/rocm/Dockerfile b/docker/rocm/Dockerfile
index 9edcd60585..653ed1e4ee 100644
--- a/docker/rocm/Dockerfile
+++ b/docker/rocm/Dockerfile
@@ -13,7 +13,7 @@ ARG ROCM
RUN apt update -qq && \
apt install -y wget gpg && \
- wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_all.deb && \
+ wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.2.3/ubuntu/jammy/amdgpu-install_7.2.3.70203-1_all.deb && \
apt install -y ./rocm.deb && \
apt update && \
apt install -qq -y rocm
@@ -32,11 +32,14 @@ RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
FROM deps AS deps-prelim
COPY docker/rocm/debian-backports.sources /etc/apt/sources.list.d/debian-backports.sources
-RUN apt-get update && \
+# install_deps.sh upgraded libstdc++6 from trixie for Battlemage; the matching
+# -dev package must also come from trixie or apt refuses to satisfy it.
+RUN echo "deb http://deb.debian.org/debian trixie main" > /etc/apt/sources.list.d/trixie.list && \
+ apt-get update && \
apt-get install -y libnuma1 && \
apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers && \
- # Install C++ standard library headers for HIPRTC kernel compilation fallback
- apt-get install -qq -y libstdc++-12-dev && \
+ apt-get install -qq -y -t trixie libstdc++-14-dev && \
+ rm -f /etc/apt/sources.list.d/trixie.list && \
rm -rf /var/lib/apt/lists/*
WORKDIR /opt/frigate
@@ -56,13 +59,17 @@ FROM scratch AS rocm-dist
ARG ROCM
+# Copy HIP headers required for MIOpen JIT (BuildHip) / HIPRTC at runtime
+COPY --from=rocm /opt/rocm-${ROCM}/include/ /opt/rocm-${ROCM}/include/
COPY --from=rocm /opt/rocm-$ROCM/bin/rocminfo /opt/rocm-$ROCM/bin/migraphx-driver /opt/rocm-$ROCM/bin/
-# Copy MIOpen database files for gfx10xx and gfx11xx only (RDNA2/RDNA3)
+# Copy MIOpen database files for gfx10xx, gfx11xx, and gfx12xx only (RDNA2/RDNA3/RDNA4)
COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx10* /opt/rocm-$ROCM/share/miopen/db/
COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx11* /opt/rocm-$ROCM/share/miopen/db/
-# Copy rocBLAS library files for gfx10xx and gfx11xx only
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx12* /opt/rocm-$ROCM/share/miopen/db/
+# Copy rocBLAS library files for gfx10xx, gfx11xx, and gfx12xx only
COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx10* /opt/rocm-$ROCM/lib/rocblas/library/
COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx11* /opt/rocm-$ROCM/lib/rocblas/library/
+COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx12* /opt/rocm-$ROCM/lib/rocblas/library/
COPY --from=rocm /opt/rocm-dist/ /
#######################################################################
@@ -71,6 +78,10 @@ ENV MIGRAPHX_DISABLE_MIOPEN_FUSION=1
ENV MIGRAPHX_DISABLE_SCHEDULE_PASS=1
ENV MIGRAPHX_DISABLE_REDUCE_FUSION=1
ENV MIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS=1
+ENV MIOPEN_CUSTOM_CACHE_DIR=/config/model_cache/migraphx
+ENV MIOPEN_USER_DB_PATH=/config/model_cache/migraphx
+ENV AMD_COMGR_CACHE=1
+ENV AMD_COMGR_CACHE_DIR=/config/model_cache/migraphx
COPY --from=rocm-dist / /
diff --git a/docker/rocm/requirements-wheels-rocm.txt b/docker/rocm/requirements-wheels-rocm.txt
index b6a202f93f..f60b550c3f 100644
--- a/docker/rocm/requirements-wheels-rocm.txt
+++ b/docker/rocm/requirements-wheels-rocm.txt
@@ -1 +1 @@
-onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.1.0/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
\ No newline at end of file
+onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.2.3-1/onnxruntime_migraphx-1.24.4-cp311-cp311-linux_x86_64.whl
\ No newline at end of file
diff --git a/docker/rocm/rocm.hcl b/docker/rocm/rocm.hcl
index 6595066c50..224118818e 100644
--- a/docker/rocm/rocm.hcl
+++ b/docker/rocm/rocm.hcl
@@ -1,5 +1,5 @@
variable "ROCM" {
- default = "7.1.1"
+ default = "7.2.3"
}
variable "HSA_OVERRIDE_GFX_VERSION" {
default = ""
diff --git a/docker/tensorrt/requirements-amd64.txt b/docker/tensorrt/requirements-amd64.txt
index 63c68b5832..597680c00c 100644
--- a/docker/tensorrt/requirements-amd64.txt
+++ b/docker/tensorrt/requirements-amd64.txt
@@ -1,18 +1,18 @@
-# NVidia TensorRT Support (amd64 only)
+# Nvidia ONNX Runtime GPU Support
--extra-index-url 'https://pypi.nvidia.com'
cython==3.0.*; platform_machine == 'x86_64'
-nvidia_cuda_cupti_cu12==12.5.82; platform_machine == 'x86_64'
-nvidia-cublas-cu12==12.5.3.*; platform_machine == 'x86_64'
-nvidia-cudnn-cu12==9.3.0.*; platform_machine == 'x86_64'
-nvidia-cufft-cu12==11.2.3.*; platform_machine == 'x86_64'
-nvidia-curand-cu12==10.3.6.*; platform_machine == 'x86_64'
-nvidia_cuda_nvcc_cu12==12.5.82; platform_machine == 'x86_64'
-nvidia-cuda-nvrtc-cu12==12.5.82; platform_machine == 'x86_64'
-nvidia_cuda_runtime_cu12==12.5.82; platform_machine == 'x86_64'
-nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
-nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
-nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
-nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
+nvidia-cuda-cupti-cu12==12.8.90; platform_machine == 'x86_64'
+nvidia-cublas-cu12==12.8.4.1; platform_machine == 'x86_64'
+nvidia-cudnn-cu12==9.8.0.87; platform_machine == 'x86_64'
+nvidia-cufft-cu12==11.3.3.83; platform_machine == 'x86_64'
+nvidia-curand-cu12==10.3.9.90; platform_machine == 'x86_64'
+nvidia-cuda-nvcc-cu12==12.8.93; platform_machine == 'x86_64'
+nvidia-cuda-nvrtc-cu12==12.8.93; platform_machine == 'x86_64'
+nvidia-cuda-runtime-cu12==12.8.90; platform_machine == 'x86_64'
+nvidia-cusolver-cu12==11.7.3.90; platform_machine == 'x86_64'
+nvidia-cusparse-cu12==12.5.8.93; platform_machine == 'x86_64'
+nvidia-nccl-cu12==2.26.2.post1; platform_machine == 'x86_64'
+nvidia-nvjitlink-cu12==12.8.93; platform_machine == 'x86_64'
onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
+onnxruntime-gpu==1.24.*; platform_machine == 'x86_64'
protobuf==3.20.3; platform_machine == 'x86_64'
diff --git a/docs/.gitignore b/docs/.gitignore
index b2d6de3062..6e46bafc0f 100644
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -7,6 +7,7 @@
# Generated files
.docusaurus
.cache-loader
+docs/integrations/api/
# Misc
.DS_Store
diff --git a/docs/data/object_detectors_models.yaml b/docs/data/object_detectors_models.yaml
new file mode 100644
index 0000000000..9d9aa40a1d
--- /dev/null
+++ b/docs/data/object_detectors_models.yaml
@@ -0,0 +1,1271 @@
+edgeTPU:
+ title: EdgeTPU
+ models:
+ - key: mobiledet
+ label: Mobiledet
+ recommended: true
+ download: A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+ ui: Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
+ yaml: |-
+ detectors:
+ coral:
+ type: edgetpu
+ device: usb
+ - key: yolov9
+ label: YOLOv9
+ recommended: false
+ download: "[Download the model](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite), bind mount the file into the container, and provide the path with `model.path`. Note that the linked model requires a 17-label [labelmap file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) that includes only 17 COCO classes."
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`. Then on the same page, in the **Custom Model** tab, configure the model settings:
+
+ | Field | Value |
+ | ---------------------------------------- | ----------------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite` |
+ | **Label map for custom object detector** | `/config/labels-coco17.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize of the model) |
+ | **Object detection model input height** | `320` (should match the imgsize of the model) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nhwc` (Frigate's default value) |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ coral:
+ type: edgetpu
+ device: usb
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize of the model, typically 320
+ height: 320 # <--- should match the imgsize of the model, typically 320
+ path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
+ labelmap_path: /config/labels-coco17.txt
+hailo8l:
+ title: Hailo-8/Hailo-8L
+ models:
+ - key: yolo
+ label: YOLO
+ recommended: true
+ download: If no custom model path or URL is provided, the Hailo detector automatically downloads the default model (YOLOv6n) from the Hailo Model Zoo on first startup based on the detected hardware. Once cached under `/config/model_cache/hailo`, the model works fully offline.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, configure the model settings:
+
+ | Field | Value |
+ | ---------------------------------------- | ----------------------- |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `rgb` |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` |
+ | **Object Detection Model Type** | `yolo-generic` |
+
+ The detector automatically selects the default model based on your hardware. Optionally, specify a local model path or URL to override.
+ yaml: |-
+ detectors:
+ hailo:
+ type: hailo8l
+ device: PCIe
+
+ model:
+ width: 320
+ height: 320
+ input_tensor: nhwc
+ input_pixel_format: rgb
+ input_dtype: int
+ model_type: yolo-generic
+ labelmap_path: /labelmap/coco-80.txt
+
+ # The detector automatically selects the default model based on your hardware:
+ # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
+ # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
+ #
+ # Optionally, you can specify a local model path to override the default.
+ # If a local path is provided and the file exists, it will be used instead of downloading.
+ # Example:
+ # path: /config/model_cache/hailo/yolov6n.hef
+ #
+ # You can also override using a custom URL:
+ # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
+ # just make sure to give it the write configuration based on the model
+ - key: ssd
+ label: SSD MobileNet v1
+ recommended: false
+ download: For SSD-based models, provide either a model path or URL to your compiled SSD model. The integration will first check the local path before downloading if necessary. The model file is cached under `/config/model_cache/hailo`.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, configure the model settings:
+
+ | Field | Value |
+ | --------------------------------------- | ------ |
+ | **Object detection model input width** | `300` |
+ | **Object detection model input height** | `300` |
+ | **Model Input Pixel Color Format** | `rgb` |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `ssd` |
+
+ Specify the local model path or URL for SSD MobileNet v1.
+ yaml: |-
+ detectors:
+ hailo:
+ type: hailo8l
+ device: PCIe
+
+ model:
+ width: 300
+ height: 300
+ input_tensor: nhwc
+ input_pixel_format: rgb
+ model_type: ssd
+ # Specify the local model path (if available) or URL for SSD MobileNet v1.
+ # Example with a local path:
+ # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
+ #
+ # Or override using a custom URL:
+ # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
+openvino:
+ title: OpenVINO
+ models:
+ - key: yolov9
+ label: YOLOv9
+ recommended: true
+ download: |-
+ YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
+ FROM python:3.11 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+ WORKDIR /yolov9
+ ADD https://github.com/WongKinYiu/yolov9.git .
+ RUN uv pip install --system -r requirements.txt
+ RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
+ ARG MODEL_SIZE
+ ARG IMG_SIZE
+ ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
+ RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
+ RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
+ FROM scratch
+ ARG MODEL_SIZE
+ ARG IMG_SIZE
+ COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolo.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `320` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: GPU # or NPU
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize set during model export
+ height: 320 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/yolo.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: ssd
+ label: SSDLite MobileNet v2
+ recommended: false
+ download: An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------ |
+ | **Custom object detector model path** | `/openvino-model/ssdlite_mobilenet_v2.xml` |
+ | **Label map for custom object detector** | `/openvino-model/coco_91cl_bkgr.txt` |
+ | **Object detection model input width** | `300` |
+ | **Object detection model input height** | `300` |
+ | **Model Input Pixel Color Format** | `bgr` |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `ssd` (Frigate's default value) |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: GPU # Or NPU
+
+ model:
+ width: 300
+ height: 300
+ input_tensor: nhwc
+ input_pixel_format: bgr
+ path: /openvino-model/ssdlite_mobilenet_v2.xml
+ labelmap_path: /openvino-model/coco_91cl_bkgr.txt
+ - key: yolo-legacy
+ label: YOLO (v3, v4, v7)
+ recommended: false
+ download: |-
+ To export as ONNX:
+
+ ```sh
+ git clone https://github.com/NateMeyer/tensorrt_demos
+ cd tensorrt_demos/yolo
+ ./download_yolo.sh
+ python3 yolo_to_onnx.py -m yolov7-320
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolo.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `320` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: GPU # or NPU
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize set during model export
+ height: 320 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/yolo.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolonas
+ label: YOLO-NAS
+ recommended: false
+ download: |-
+ You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) which can be run directly in [Google Colab](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+
+ :::warning
+
+ The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+
+ :::
+
+ The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------------- |
+ | **Custom object detector model path** | `/config/yolo_nas_s.onnx` |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match whatever was set in notebook) |
+ | **Object detection model input height** | `320` (should match whatever was set in notebook) |
+ | **Model Input Pixel Color Format** | `bgr` |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolonas` |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: GPU
+
+ model:
+ model_type: yolonas
+ width: 320 # <--- should match whatever was set in notebook
+ height: 320 # <--- should match whatever was set in notebook
+ input_tensor: nchw
+ input_pixel_format: bgr
+ path: /config/yolo_nas_s.onnx
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolox
+ label: YOLOX
+ recommended: false
+ download: YOLOx models can be downloaded [from the YOLOx repo](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime).
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ------------------------------------- | -------------------------------- |
+ | **Custom object detector model path** | `/config/yolox.onnx` (use the filename you generated above) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nhwc` (Frigate's default value) |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolox` |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: GPU
+
+ model:
+ model_type: yolox
+ path: /config/model_cache/yolox.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: rfdetr
+ label: RF-DETR
+ recommended: false
+ download: |-
+ RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=Nano --rm --output . -f- <<'EOF'
+ FROM python:3.12 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+ WORKDIR /rfdetr
+ RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnx==1.19.1 transformers==4.57.6 onnxscript
+ ARG MODEL_SIZE
+ RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
+ FROM scratch
+ ARG MODEL_SIZE
+ COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | --------------------------------------- | --------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/rfdetr.onnx` (use the filename you generated above) |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `rfdetr` |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: GPU
+
+ model:
+ model_type: rfdetr
+ width: 320
+ height: 320
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/rfdetr.onnx # use the filename you generated above
+ - key: dfine
+ label: D-FINE / DEIMv2
+ recommended: false
+ download: |-
+ #### D-FINE
+
+ D-FINE can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=s` in the first line to `s`, `m`, or `l` size.
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=s --output . -f- <<'EOF'
+ FROM python:3.11 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
+ WORKDIR /dfine
+ RUN git clone https://github.com/Peterande/D-FINE.git .
+ RUN uv pip install --system -r requirements.txt
+ RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
+ # Create output directory and download checkpoint
+ RUN mkdir -p output
+ ARG MODEL_SIZE
+ RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
+ # Modify line 58 of export_onnx.py to change batch size to 1
+ RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
+ RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
+ FROM scratch
+ ARG MODEL_SIZE
+ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
+ EOF
+ ```
+
+ #### DEIMv2
+
+ [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
+
+ - **HGNetv2** (smaller/faster): `atto`, `femto`, `pico`, `n`
+ - **DINOv3** (larger/more accurate): `s`, `m`, `l`, `x`
+
+ Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
+
+ ```sh
+ docker build . --rm --build-arg BACKBONE=hgnetv2 --build-arg MODEL_SIZE=n --output . -f- <<'EOF'
+ FROM python:3.11-slim AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y git libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
+ WORKDIR /deimv2
+ RUN git clone https://github.com/Intellindust-AI-Lab/DEIMv2.git .
+ # Install CPU-only PyTorch first to avoid pulling CUDA variant
+ RUN uv pip install --no-cache --system torch torchvision --index-url https://download.pytorch.org/whl/cpu
+ RUN uv pip install --no-cache --system -r requirements.txt
+ RUN uv pip install --no-cache --system onnx safetensors huggingface_hub
+ RUN mkdir -p output
+ ARG BACKBONE
+ ARG MODEL_SIZE
+ # Download from Hugging Face and convert safetensors to pth
+ RUN python3 -c "\
+ from huggingface_hub import hf_hub_download; \
+ from safetensors.torch import load_file; \
+ import torch; \
+ backbone = '${BACKBONE}'.replace('hgnetv2','HGNetv2').replace('dinov3','DINOv3'); \
+ size = '${MODEL_SIZE}'.upper(); \
+ st = load_file(hf_hub_download('Intellindust/DEIMv2_' + backbone + '_' + size + '_COCO', 'model.safetensors')); \
+ torch.save({'model': st}, 'output/deimv2.pth')"
+ RUN sed -i "s/data = torch.rand(2/data = torch.rand(1/" tools/deployment/export_onnx.py
+ # HuggingFace safetensors omits frozen constants that the model constructor initializes
+ RUN sed -i "s/cfg.model.load_state_dict(state)/cfg.model.load_state_dict(state, strict=False)/" tools/deployment/export_onnx.py
+ RUN python3 tools/deployment/export_onnx.py -c configs/deimv2/deimv2_${BACKBONE}_${MODEL_SIZE}_coco.yml -r output/deimv2.pth
+ FROM scratch
+ ARG BACKBONE
+ ARG MODEL_SIZE
+ COPY --from=build /deimv2/output/deimv2.onnx /deimv2_${BACKBONE}_${MODEL_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `CPU`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ---------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/dfine-s.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `640` |
+ | **Object detection model input height** | `640` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `dfine` |
+ yaml: |-
+ detectors:
+ ov:
+ type: openvino
+ device: CPU
+
+ model:
+ model_type: dfine
+ width: 640
+ height: 640
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/dfine-s.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+appleSilicon:
+ title: Apple Silicon
+ models:
+ - key: yolov9
+ label: YOLOv9
+ recommended: true
+ download: |-
+ YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
+ FROM python:3.11 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+ WORKDIR /yolov9
+ ADD https://github.com/WongKinYiu/yolov9.git .
+ RUN uv pip install --system -r requirements.txt
+ RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
+ ARG MODEL_SIZE
+ ARG IMG_SIZE
+ ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
+ RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
+ RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
+ FROM scratch
+ ARG MODEL_SIZE
+ ARG IMG_SIZE
+ COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolo.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `320` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ apple-silicon:
+ type: zmq
+ endpoint: tcp://host.docker.internal:5555
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize set during model export
+ height: 320 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/yolo.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolo-legacy
+ label: YOLO (v3, v4, v7)
+ recommended: false
+ download: |-
+ To export as ONNX:
+
+ ```sh
+ git clone https://github.com/NateMeyer/tensorrt_demos
+ cd tensorrt_demos/yolo
+ ./download_yolo.sh
+ python3 yolo_to_onnx.py -m yolov7-320
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolo.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `320` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ apple-silicon:
+ type: zmq
+ endpoint: tcp://host.docker.internal:5555
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize set during model export
+ height: 320 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/yolo.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+onnx:
+ title: ONNX
+ models:
+ - key: yolov9
+ label: YOLOv9
+ recommended: true
+ download: |-
+ YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
+ FROM python:3.11 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+ WORKDIR /yolov9
+ ADD https://github.com/WongKinYiu/yolov9.git .
+ RUN uv pip install --system -r requirements.txt
+ RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
+ ARG MODEL_SIZE
+ ARG IMG_SIZE
+ ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
+ RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
+ RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
+ FROM scratch
+ ARG MODEL_SIZE
+ ARG IMG_SIZE
+ COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolo.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `320` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ onnx:
+ type: onnx
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize set during model export
+ height: 320 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/yolo.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: rfdetr
+ label: RF-DETR
+ recommended: false
+ download: |-
+ RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=Nano --rm --output . -f- <<'EOF'
+ FROM python:3.12 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+ WORKDIR /rfdetr
+ RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnx==1.19.1 transformers==4.57.6 onnxscript
+ ARG MODEL_SIZE
+ RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
+ FROM scratch
+ ARG MODEL_SIZE
+ COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | --------------------------------------- | --------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/rfdetr.onnx` (use the filename you generated above) |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `rfdetr` |
+ yaml: |-
+ detectors:
+ onnx:
+ type: onnx
+
+ model:
+ model_type: rfdetr
+ width: 320
+ height: 320
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/rfdetr.onnx # use the filename you generated above
+ - key: yolonas
+ label: YOLO-NAS
+ recommended: false
+ download: |-
+ You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) which can be run directly in [Google Colab](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
+
+ :::warning
+
+ The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+
+ :::
+
+ The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------------- |
+ | **Custom object detector model path** | `/config/yolo_nas_s.onnx` |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match whatever was set in notebook) |
+ | **Object detection model input height** | `320` (should match whatever was set in notebook) |
+ | **Model Input Pixel Color Format** | `bgr` |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolonas` |
+ yaml: |-
+ detectors:
+ onnx:
+ type: onnx
+
+ model:
+ model_type: yolonas
+ width: 320 # <--- should match whatever was set in notebook
+ height: 320 # <--- should match whatever was set in notebook
+ input_pixel_format: bgr
+ input_tensor: nchw
+ path: /config/yolo_nas_s.onnx
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolox
+ label: YOLOX
+ recommended: false
+ download: YOLOx models can be downloaded [from the YOLOx repo](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime).
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolox_tiny.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `416` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `416` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float_denorm` |
+ | **Object Detection Model Type** | `yolox` |
+ yaml: |-
+ detectors:
+ onnx:
+ type: onnx
+
+ model:
+ model_type: yolox
+ width: 416 # <--- should match the imgsize set during model export
+ height: 416 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float_denorm
+ path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: dfine
+ label: D-FINE / DEIMv2
+ recommended: false
+ download: |-
+ #### Downloading D-FINE Model
+
+ D-FINE can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=s` in the first line to `s`, `m`, or `l` size.
+
+ ```sh
+ docker build . --build-arg MODEL_SIZE=s --output . -f- <<'EOF'
+ FROM python:3.11 AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
+ WORKDIR /dfine
+ RUN git clone https://github.com/Peterande/D-FINE.git .
+ RUN uv pip install --system -r requirements.txt
+ RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
+ # Create output directory and download checkpoint
+ RUN mkdir -p output
+ ARG MODEL_SIZE
+ RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
+ # Modify line 58 of export_onnx.py to change batch size to 1
+ RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
+ RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
+ FROM scratch
+ ARG MODEL_SIZE
+ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
+ EOF
+ ```
+
+ #### Downloading DEIMv2 Model
+
+ [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
+
+ - **HGNetv2** (smaller/faster): `atto`, `femto`, `pico`, `n`
+ - **DINOv3** (larger/more accurate): `s`, `m`, `l`, `x`
+
+ Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
+
+ ```sh
+ docker build . --rm --build-arg BACKBONE=hgnetv2 --build-arg MODEL_SIZE=n --output . -f- <<'EOF'
+ FROM python:3.11-slim AS build
+ RUN apt-get update && apt-get install --no-install-recommends -y git libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
+ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
+ WORKDIR /deimv2
+ RUN git clone https://github.com/Intellindust-AI-Lab/DEIMv2.git .
+ # Install CPU-only PyTorch first to avoid pulling CUDA variant
+ RUN uv pip install --no-cache --system torch torchvision --index-url https://download.pytorch.org/whl/cpu
+ RUN uv pip install --no-cache --system -r requirements.txt
+ RUN uv pip install --no-cache --system onnx safetensors huggingface_hub
+ RUN mkdir -p output
+ ARG BACKBONE
+ ARG MODEL_SIZE
+ # Download from Hugging Face and convert safetensors to pth
+ RUN python3 -c "\
+ from huggingface_hub import hf_hub_download; \
+ from safetensors.torch import load_file; \
+ import torch; \
+ backbone = '${BACKBONE}'.replace('hgnetv2','HGNetv2').replace('dinov3','DINOv3'); \
+ size = '${MODEL_SIZE}'.upper(); \
+ st = load_file(hf_hub_download('Intellindust/DEIMv2_' + backbone + '_' + size + '_COCO', 'model.safetensors')); \
+ torch.save({'model': st}, 'output/deimv2.pth')"
+ RUN sed -i "s/data = torch.rand(2/data = torch.rand(1/" tools/deployment/export_onnx.py
+ # HuggingFace safetensors omits frozen constants that the model constructor initializes
+ RUN sed -i "s/cfg.model.load_state_dict(state)/cfg.model.load_state_dict(state, strict=False)/" tools/deployment/export_onnx.py
+ RUN python3 tools/deployment/export_onnx.py -c configs/deimv2/deimv2_${BACKBONE}_${MODEL_SIZE}_coco.yml -r output/deimv2.pth
+ FROM scratch
+ ARG BACKBONE
+ ARG MODEL_SIZE
+ COPY --from=build /deimv2/output/deimv2.onnx /deimv2_${BACKBONE}_${MODEL_SIZE}.onnx
+ EOF
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/dfine_m_obj2coco.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `640` |
+ | **Object detection model input height** | `640` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `dfine` |
+ yaml: |-
+ detectors:
+ onnx:
+ type: onnx
+
+ model:
+ model_type: dfine
+ width: 640
+ height: 640
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolo-legacy
+ label: YOLO (v3, v4, v7)
+ recommended: false
+ download: |-
+ To export as ONNX:
+
+ ```sh
+ git clone https://github.com/NateMeyer/tensorrt_demos
+ cd tensorrt_demos/yolo
+ ./download_yolo.sh
+ python3 yolo_to_onnx.py -m yolov7-320
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------------- |
+ | **Custom object detector model path** | `/config/model_cache/yolo.onnx` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (should match the imgsize set during model export) |
+ | **Object detection model input height** | `320` (should match the imgsize set during model export) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ onnx:
+ type: onnx
+
+ model:
+ model_type: yolo-generic
+ width: 320 # <--- should match the imgsize set during model export
+ height: 320 # <--- should match the imgsize set during model export
+ input_tensor: nchw
+ input_dtype: float
+ path: /config/model_cache/yolo.onnx # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+cpu:
+ title: CPU
+ models:
+ - key: ssd
+ label: MobileNet v2
+ recommended: true
+ download: A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **CPU** from the detector type dropdown and click **Add**. Configure the number of threads and click **Add** again to add additional CPU detectors as needed (one per camera is recommended).
+
+ | Field | Value |
+ | ----------------- | ----- |
+ | **Detector type** | `cpu` |
+ | **Num threads** | `3` |
+ yaml: |-
+ detectors:
+ cpu1:
+ type: cpu
+ num_threads: 3
+deepstack:
+ title: DeepStack / CodeProject.AI
+ models:
+ - key: yolo
+ label: YOLO
+ recommended: true
+ download: This detector runs object detection over the network against a CodeProject.AI or DeepStack server, so no model is downloaded into Frigate itself. Visit the [CodeProject.AI official website](https://www.codeproject.com/Articles/5322557/CodeProject-AI-Server-AI-the-easy-way) to download and install the AI server on your preferred device (e.g. Raspberry Pi, Nvidia Jetson, or other compatible hardware) before configuring the detector.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **DeepStack** from the detector type dropdown and click **Add**. Set the API URL to point to your CodeProject.AI server (e.g., `http://:/v1/vision/detection`).
+
+ | Field | Value |
+ | ------------- | ---------------------------------------------------------------------- |
+ | **API URL** | `http://:/v1/vision/detection` |
+ | **API Timeout** | `0.1` (seconds) |
+ yaml: |-
+ detectors:
+ deepstack:
+ api_url: http://:/v1/vision/detection
+ type: deepstack
+ api_timeout: 0.1 # seconds
+memryx:
+ title: MemryX
+ models:
+ - key: yolonas
+ label: YOLO-NAS
+ recommended: true
+ download: |-
+ The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded automatically and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
+
+ **Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
+
+ The input size for **YOLO-NAS** can be set to either **320x320** (default) or **640x640**.
+
+ - The default size of **320x320** is optimized for lower CPU usage and faster inference times.
+
+ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------------- |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (can be set to `640` for higher resolution) |
+ | **Object detection model input height** | `320` (can be set to `640` for higher resolution) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolonas` |
+ yaml: |-
+ detectors:
+ memx0:
+ type: memryx
+ device: PCIe:0
+
+ model:
+ model_type: yolonas
+ width: 320 # (Can be set to 640 for higher resolution)
+ height: 320 # (Can be set to 640 for higher resolution)
+ input_tensor: nchw
+ input_dtype: float
+ labelmap_path: /labelmap/coco-80.txt
+ # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+ # path: /config/yolonas.zip
+ # The .zip file must contain:
+ # ├── yolonas.dfp (a file ending with .dfp)
+ # └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
+ - key: yolov9
+ label: YOLOv9
+ recommended: false
+ download: |-
+ The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
+
+ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------------- |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (can be set to `640` for higher resolution) |
+ | **Object detection model input height** | `320` (can be set to `640` for higher resolution) |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ memx0:
+ type: memryx
+ device: PCIe:0
+
+ model:
+ model_type: yolo-generic
+ width: 320 # (Can be set to 640 for higher resolution)
+ height: 320 # (Can be set to 640 for higher resolution)
+ input_tensor: nchw
+ input_dtype: float
+ labelmap_path: /labelmap/coco-80.txt
+ # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+ # path: /config/yolov9.zip
+ # The .zip file must contain:
+ # ├── yolov9.dfp (a file ending with .dfp)
+ - key: yolox
+ label: YOLOX
+ recommended: false
+ download: |-
+ The model is sourced from the [OpenCV Model Zoo](https://github.com/opencv/opencv_zoo) and precompiled to DFP.
+
+ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ----------------------- |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `640` |
+ | **Object detection model input height** | `640` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float_denorm` |
+ | **Object Detection Model Type** | `yolox` |
+ yaml: |-
+ detectors:
+ memx0:
+ type: memryx
+ device: PCIe:0
+
+ model:
+ model_type: yolox
+ width: 640
+ height: 640
+ input_tensor: nchw
+ input_dtype: float_denorm
+ labelmap_path: /labelmap/coco-80.txt
+ # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+ # path: /config/yolox.zip
+ # The .zip file must contain:
+ # ├── yolox.dfp (a file ending with .dfp)
+ - key: ssd
+ label: SSDLite MobileNet v2
+ recommended: false
+ download: |-
+ The model is sourced from the [OpenMMLab Model Zoo](https://mmdeploy-oss.openmmlab.com/model/mmdet-det/ssdlite-e8679f.onnx) and has been converted to DFP.
+
+ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ----------------------- |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `float` |
+ | **Object Detection Model Type** | `ssd` |
+ yaml: |-
+ detectors:
+ memx0:
+ type: memryx
+ device: PCIe:0
+
+ model:
+ model_type: ssd
+ width: 320
+ height: 320
+ input_tensor: nchw
+ input_dtype: float
+ labelmap_path: /labelmap/coco-80.txt
+ # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
+ # path: /config/ssdlite_mobilenet.zip
+ # The .zip file must contain:
+ # ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
+ # └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
+tensorrt:
+ title: TensorRT
+ models:
+ - key: yolo-legacy
+ label: YOLO (v3, v4, v7)
+ recommended: true
+ download: |-
+ The model used for TensorRT must be preprocessed on the same hardware platform that it will run on, so Frigate generates the `.trt` model file on-device at startup. Processed models are stored in the `/config/model_cache` folder.
+
+ By default no models are generated. Set the `YOLO_MODELS` environment variable in Docker to one or more comma-separated model names (from the available `yolov3`/`yolov4`/`yolov7` models) and each one will be generated on startup if the corresponding `{model}.trt` file is not already present in `model_cache` (delete it to force regeneration). On Jetson devices with DLAs (Xavier or Orin), append `-dla` to a model name to generate a DLA model. If your GPU does not support FP16 operations, pass `USE_FP16=False` to disable it.
+
+ An example `docker-compose.yml` fragment that converts the `yolov7-320` and `yolov7x-640` models:
+
+ ```yml
+ frigate:
+ environment:
+ - YOLO_MODELS=yolov7-320,yolov7x-640
+ - USE_FP16=false
+ ```
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **TensorRT** from the detector type dropdown and click **Add**, then set the device to `0` (the default GPU index). Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ------------------------------------------------------------ |
+ | **Custom object detector model path** | `/config/model_cache/tensorrt/yolov7-320.trt` (use the filename you generated above) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` (MUST match the chosen model, e.g., yolov7-320 -> 320) |
+ | **Object detection model input height** | `320` (MUST match the chosen model, e.g., yolov7-320 -> 320) |
+ | **Model Input Pixel Color Format** | `rgb` |
+ | **Model Input Tensor Shape** | `nchw` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `ssd` (Frigate's default value) |
+ yaml: |-
+ detectors:
+ tensorrt:
+ type: tensorrt
+ device: 0 #This is the default, select the first GPU
+
+ model:
+ path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
+ labelmap_path: /labelmap/coco-80.txt
+ input_tensor: nchw
+ input_pixel_format: rgb
+ width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
+ height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
+synaptics:
+ title: Synaptics
+ models:
+ - key: ssd
+ label: SSD MobileNet
+ recommended: true
+ download: A synap model is provided in the container at `/synaptics/mobilenet.synap` and is used by this detector type by default. The model comes from the [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **Synaptics** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ---------------------------- |
+ | **Custom object detector model path** | `/synaptics/mobilenet.synap` |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `224` |
+ | **Object detection model input height** | `224` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `ssd` (Frigate's default value) |
+ yaml: |-
+ detectors: # required
+ synap_npu: # required
+ type: synaptics # required
+
+ model: # required
+ path: /synaptics/mobilenet.synap # required
+ width: 224 # required
+ height: 224 # required
+ input_tensor: nhwc # default value (optional. If you change the model, it is required)
+ labelmap_path: /labelmap/coco-80.txt # required
+rknn:
+ title: RKNN
+ models:
+ - key: yolov9
+ label: YOLOv9
+ recommended: true
+ download: |-
+ If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
+
+ You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | -------------------------------------------------- |
+ | **Custom object detector model path** | `frigate-fp16-yolov9-t` (or other yolov9 variants) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ model: # required
+ # name of model (will be automatically downloaded) or path to your own .rknn model file
+ # possible values are:
+ # - frigate-fp16-yolov9-t
+ # - frigate-fp16-yolov9-s
+ # - frigate-fp16-yolov9-m
+ # - frigate-fp16-yolov9-c
+ # - frigate-fp16-yolov9-e
+ # your yolo_model.rknn
+ path: frigate-fp16-yolov9-t
+ model_type: yolo-generic
+ width: 320
+ height: 320
+ input_tensor: nhwc
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolonas
+ label: YOLO-NAS
+ recommended: false
+ download: |-
+ If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
+
+ You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
+
+ **Note:** The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ----------------------------------------------------------------------- |
+ | **Custom object detector model path** | `deci-fp16-yolonas_s` (or `deci-fp16-yolonas_m`, `deci-fp16-yolonas_l`) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `bgr` |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolonas` |
+ yaml: |-
+ model: # required
+ # name of model (will be automatically downloaded) or path to your own .rknn model file
+ # possible values are:
+ # - deci-fp16-yolonas_s
+ # - deci-fp16-yolonas_m
+ # - deci-fp16-yolonas_l
+ # your yolonas_model.rknn
+ path: deci-fp16-yolonas_s
+ model_type: yolonas
+ width: 320
+ height: 320
+ input_pixel_format: bgr
+ input_tensor: nhwc
+ labelmap_path: /labelmap/coco-80.txt
+ - key: yolox
+ label: YOLOx
+ recommended: false
+ download: |-
+ If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
+
+ You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ---------------------------------------------- |
+ | **Custom object detector model path** | `rock-i8-yolox_nano` (or other yolox variants) |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `416` |
+ | **Object detection model input height** | `416` |
+ | **Model Input Pixel Color Format** | `rgb` (Frigate's default value) |
+ | **Model Input Tensor Shape** | `nhwc` |
+ | **Model Input D Type** | `int` (Frigate's default value) |
+ | **Object Detection Model Type** | `yolox` |
+ yaml: |-
+ model: # required
+ # name of model (will be automatically downloaded) or path to your own .rknn model file
+ # possible values are:
+ # - rock-i8-yolox_nano
+ # - rock-i8-yolox_tiny
+ # - rock-fp16-yolox_nano
+ # - rock-fp16-yolox_tiny
+ # your yolox_model.rknn
+ path: rock-i8-yolox_nano
+ model_type: yolox
+ width: 416
+ height: 416
+ input_tensor: nhwc
+ labelmap_path: /labelmap/coco-80.txt
+axengine:
+ title: AXEngine
+ models:
+ - key: yolov9
+ label: YOLOv9
+ recommended: true
+ download: A yolov9 axmodel is provided in the container at `/axmodels` and is used by this detector type by default. The AXEngine detector downloads its default model from HuggingFace on first startup; once cached, the model works fully offline.
+ ui: |-
+ Navigate to **Settings > System > Detectors and model** and select **AXEngine NPU** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
+
+ | Field | Value |
+ | ---------------------------------------- | ----------------------- |
+ | **Custom object detector model path** | `frigate-yolov9-tiny` |
+ | **Label map for custom object detector** | `/labelmap/coco-80.txt` |
+ | **Object detection model input width** | `320` |
+ | **Object detection model input height** | `320` |
+ | **Model Input Pixel Color Format** | `bgr` |
+ | **Model Input Tensor Shape** | `nhwc` (Frigate's default value) |
+ | **Model Input D Type** | `int` |
+ | **Object Detection Model Type** | `yolo-generic` |
+ yaml: |-
+ detectors:
+ axengine:
+ type: axengine
+
+ model:
+ path: frigate-yolov9-tiny
+ model_type: yolo-generic
+ width: 320
+ height: 320
+ input_dtype: int
+ input_pixel_format: bgr
+ labelmap_path: /labelmap/coco-80.txt
diff --git a/docs/docs/configuration/reference.md b/docs/docs/configuration/advanced/reference.md
similarity index 83%
rename from docs/docs/configuration/reference.md
rename to docs/docs/configuration/advanced/reference.md
index edcea57a14..802f3d7856 100644
--- a/docs/docs/configuration/reference.md
+++ b/docs/docs/configuration/advanced/reference.md
@@ -11,6 +11,8 @@ It is not recommended to copy this full configuration file. Only specify values
:::
+Sections marked `# NOTE: Can be overridden at the camera level` can be set globally and then adjusted per camera. See [Global and Camera-Level Configuration](../config_overrides.md) for how that works.
+
```yaml
mqtt:
# Optional: Enable mqtt server (default: shown below)
@@ -75,11 +77,19 @@ tls:
# Optional: Enable TLS for port 8971 (default: shown below)
enabled: True
-# Optional: IPv6 configuration
+# Optional: Networking configuration
networking:
# Optional: Enable IPv6 on 5000, and 8971 if tls is configured (default: shown below)
ipv6:
enabled: False
+ # Optional: Override ports Frigate uses for listening (defaults: shown below)
+ # An IP address may also be provided to bind to a specific interface, e.g. ip:port
+ # NOTE: This setting is for advanced users and may break some integrations. The majority
+ # of users should change ports in the docker compose file
+ # or use the docker run `--publish` option to select a different port.
+ listen:
+ internal: 5000
+ external: 8971
# Optional: Proxy configuration
proxy:
@@ -139,6 +149,13 @@ auth:
# NOTE: changing this value will not automatically update password hashes, you
# will need to change each user password for it to apply
hash_iterations: 600000
+ # Optional: Map roles to the list of cameras each role can access (default: none)
+ # NOTE: An empty list grants the role access to all cameras. Roles defined here can be
+ # referenced by proxy header role mapping or assigned to native users.
+ roles:
+ my_custom_role:
+ - front_door
+ - back_yard
# Optional: model modifications
# NOTE: The default values are for the EdgeTPU detector.
@@ -156,10 +173,14 @@ model:
# Valid values are rgb, bgr, or yuv. (default: shown below)
input_pixel_format: rgb
# Required: Object detection model input tensor format
- # Valid values are nhwc or nchw (default: shown below)
+ # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
input_tensor: nhwc
- # Required: Object detection model type, currently only used with the OpenVINO detector
- # Valid values are ssd, yolox, yolonas (default: shown below)
+ # Optional: Data type of the model input tensor
+ # Valid values are float, float_denorm, or int (default: shown below)
+ input_dtype: int
+ # Required: Object detection model architecture, used by detectors that support more
+ # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
+ # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
model_type: ssd
# Required: Label name modifications. These are merged into the standard labelmap.
labelmap:
@@ -188,11 +209,12 @@ audio:
# - 500 - medium sensitivity
# - 1000 - low sensitivity
min_volume: 500
+ # Optional: Number of threads to use for audio detection (default: shown below)
+ num_threads: 2
# Optional: Types of audio to listen for (default: shown below)
listen:
- bark
- fire_alarm
- - scream
- speech
- yell
# Optional: Filters to configure detection.
@@ -249,7 +271,7 @@ birdseye:
# More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
ffmpeg:
# Optional: ffmpeg binary path (default: shown below)
- # can also be set to `7.0` or `5.0` to specify one of the included versions
+ # can also be set to `8.0` or `5.0` to specify one of the included versions
# or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
path: "default"
# Optional: global ffmpeg args (default: shown below)
@@ -320,7 +342,7 @@ detect:
# especially when using separate streams for detect and record.
# Use this setting to make the timeline bounding boxes more closely align
# with the recording. The value can be positive or negative.
- # TIP: Imagine there is an tracked object clip with a person walking from left to right.
+ # TIP: Imagine there is a tracked object clip with a person walking from left to right.
# If the tracked object lifecycle bounding box is consistently to the left of the person
# then the value should be decreased. Similarly, if a person is walking from
# left to right and the bounding box is consistently ahead of the person
@@ -339,7 +361,15 @@ objects:
# Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
# Checks based on the bottom center of the bounding box of the object.
# NOTE: This mask is COMBINED with the object type specific mask below
- mask: 0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278
+ mask:
+ # Object filter mask name (required)
+ mask1:
+ # Optional: A friendly name for the mask
+ friendly_name: "Object filter mask area"
+ # Optional: Whether this mask is active (default: true)
+ enabled: true
+ # Required: Coordinates polygon for the mask
+ coordinates: "0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278"
# Optional: filters to reduce false positives for specific object types
filters:
person:
@@ -359,7 +389,15 @@ objects:
threshold: 0.7
# Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
# Checks based on the bottom center of the bounding box of the object
- mask: 0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278
+ mask:
+ # Object filter mask name (required)
+ mask1:
+ # Optional: A friendly name for the mask
+ friendly_name: "Object filter mask area"
+ # Optional: Whether this mask is active (default: true)
+ enabled: true
+ # Required: Coordinates polygon for the mask
+ coordinates: "0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278"
# Optional: Configuration for AI generated tracked object descriptions
genai:
# Optional: Enable AI object description generation (default: shown below)
@@ -433,8 +471,8 @@ review:
detections: False
# Optional: Activity Context Prompt to give context to the GenAI what activity is and is not suspicious.
# It is important to be direct and detailed. See documentation for the default prompt structure.
- activity_context_prompt: """Define what is and is not suspicious
-"""
+ activity_context_prompt: |
+ Define what is and is not suspicious
# Optional: Image source for GenAI (default: preview)
# Options: "preview" (uses cached preview frames at ~180p) or "recordings" (extracts frames from recordings at 480p)
# Using "recordings" provides better image quality but uses more tokens per image.
@@ -445,6 +483,8 @@ review:
- Animals in the garden
# Optional: Preferred response language (default: English)
preferred_language: English
+ # Optional: Save thumbnails sent to the GenAI provider for review/debugging purposes (default: shown below)
+ debug_save_thumbnails: False
# Optional: Motion configuration
# NOTE: Can be overridden at the camera level
@@ -458,12 +498,16 @@ motion:
# Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
# The value should be between 1 and 255.
threshold: 30
- # Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection
- # needs to recalibrate. (default: shown below)
+ # Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection needs
+ # to recalibrate and motion checks stop for that frame. Recordings are unaffected. (default: shown below)
# Increasing this value will make motion detection more likely to consider lightning or ir mode changes as valid motion.
- # Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching
- # a doorbell camera.
+ # Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera.
lightning_threshold: 0.8
+ # Optional: Fraction of the frame that must change in a single update before motion boxes are completely
+ # ignored. Values range between 0.0 and 1.0. When exceeded, no motion boxes are reported and **no motion
+ # recording** is created for that frame. Leave unset (null) to disable this feature. Use with care on PTZ
+ # cameras or other situations where you require guaranteed frame capture.
+ skip_motion_threshold: None
# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: shown below)
# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
# make motion detection more sensitive to smaller moving objects.
@@ -472,6 +516,8 @@ motion:
# - 30 - medium sensitivity
# - 50 - low sensitivity
contour_area: 10
+ # Optional: Alpha blending factor used in frame differencing for motion calculation (default: shown below)
+ delta_alpha: 0.2
# Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
# Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
# Low values will cause things like moving shadows to be detected as motion for longer.
@@ -483,7 +529,15 @@ motion:
frame_height: 100
# Optional: motion mask
# NOTE: see docs for more detailed info on creating masks
- mask: 0.000,0.469,1.000,0.469,1.000,1.000,0.000,1.000
+ mask:
+ # Motion mask name (required)
+ mask1:
+ # Optional: A friendly name for the mask
+ friendly_name: "Motion mask area"
+ # Optional: Whether this mask is active (default: true)
+ enabled: true
+ # Required: Coordinates polygon for the mask
+ coordinates: "0.000,0.469,1.000,0.469,1.000,1.000,0.000,1.000"
# Optional: improve contrast (default: shown below)
# Enables dynamic contrast improvement. This should help improve night detections at the cost of making motion detection more sensitive
# for daytime.
@@ -512,8 +566,6 @@ record:
# Optional: Number of minutes to wait between cleanup runs (default: shown below)
# This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
expire_interval: 60
- # Optional: Two-way sync recordings database with disk on startup and once a day (default: shown below).
- sync_recordings: False
# Optional: Continuous retention settings
continuous:
# Optional: Number of days to retain recordings regardless of tracked objects or motion (default: shown below)
@@ -536,6 +588,10 @@ record:
# The -r (framerate) dictates how smooth the output video is.
# So the args would be -vf setpts=0.02*PTS -r 30 in that case.
timelapse_args: "-vf setpts=0.04*PTS -r 30"
+ # Optional: Global hardware acceleration settings for timelapse exports. (default: inherit)
+ hwaccel_args: auto
+ # Optional: Maximum number of export jobs to process at the same time (default: shown below)
+ max_concurrent: 3
# Optional: Recording Preview Settings
preview:
# Optional: Quality of recording preview (default: shown below).
@@ -582,13 +638,12 @@ record:
# never stored, so setting the mode to "all" here won't bring them back.
mode: motion
-# Optional: Configuration for the jpg snapshots written to the clips directory for each tracked object
+# Optional: Configuration for the snapshots written to the clips directory for each tracked object
+# Timestamp, bounding_box, crop and height settings are applied by default to API requests for snapshots.
# NOTE: Can be overridden at the camera level
snapshots:
- # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
+ # Optional: Enable writing snapshot images to /media/frigate/clips (default: shown below)
enabled: False
- # Optional: save a clean copy of the snapshot image (default: shown below)
- clean_copy: True
# Optional: print a timestamp on the snapshots (default: shown below)
timestamp: False
# Optional: draw bounding box on the snapshots (default: shown below)
@@ -606,8 +661,8 @@ snapshots:
# Optional: Per object retention days
objects:
person: 15
- # Optional: quality of the encoded jpeg, 0-100 (default: shown below)
- quality: 70
+ # Optional: quality of the encoded snapshot image, 0-100 (default: shown below)
+ quality: 60
# Optional: Configuration for semantic search capability
semantic_search:
@@ -679,28 +734,42 @@ lpr:
enhancement: 0
# Optional: Save plate images to /media/frigate/clips/lpr for debugging purposes (default: shown below)
debug_save_plates: False
- # Optional: List of regex replacement rules to normalize detected plates (default: shown below)
- replace_rules: {}
+ # Optional: List of regex replacement rules to normalize detected plates before matching (default: none)
+ replace_rules:
+ # Required: regex pattern to match in the detected plate
+ - pattern: "O"
+ # Required: string to replace the matched pattern with
+ replacement: "0"
-# Optional: Configuration for AI / LLM provider
+# Optional: Configuration for AI / LLM providers
# WARNING: Depending on the provider, this will send thumbnails over the internet
# to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
# the camera level to enhance privacy for indoor cameras.
+# NOTE: genai is a map of named providers. Each key is a name you choose for the provider,
+# and each role (chat, descriptions, embeddings) may be assigned to exactly one provider.
genai:
- # Required: Provider must be one of ollama, gemini, or openai
- provider: ollama
- # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
- base_url: http://localhost::11434
- # Required if gemini or openai
- api_key: "{FRIGATE_GENAI_API_KEY}"
- # Required: The model to use with the provider.
- model: gemini-1.5-flash
- # Optional additional args to pass to the GenAI Provider (default: None)
- provider_options:
- keep_alive: -1
- # Optional: Options to pass during inference calls (default: {})
- runtime_options:
- temperature: 0.7
+ # Required: name of the provider (chosen by you, used to reference it elsewhere)
+ my_provider:
+ # Required: Provider must be one of ollama, openai, azure_openai, gemini, or llamacpp
+ provider: ollama
+ # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
+ base_url: http://localhost::11434
+ # Required if gemini or openai
+ api_key: "{FRIGATE_GENAI_API_KEY}"
+ # Required: The model to use with the provider.
+ model: gemini-1.5-flash
+ # Optional: Roles this provider handles (default: shown below)
+ # Each role (chat, descriptions, embeddings) must be assigned to exactly one provider.
+ roles:
+ - chat
+ - descriptions
+ - embeddings
+ # Optional additional args to pass to the GenAI Provider (default: None)
+ provider_options:
+ keep_alive: -1
+ # Optional: Options to pass during inference calls (default: {})
+ runtime_options:
+ temperature: 0.7
# Optional: Configuration for audio transcription
# NOTE: only the enabled option can be overridden at the camera level
@@ -747,14 +816,15 @@ classification:
cameras:
camera_name:
# Required: Crop of image frame on this camera to run classification on
- crop: [0, 180, 220, 400]
+ # [x1, y1, x2, y2] as decimals between 0 and 1, relative to the detect resolution
+ crop: [0.0, 0.25, 0.3, 0.85]
# Optional: If classification should be run when motion is detected in the crop (default: shown below)
motion: False
# Optional: Interval to run classification on in seconds (default: shown below)
interval: None
# Optional: Restream configuration
-# Uses https://github.com/AlexxIT/go2rtc (v1.9.10)
+# Uses https://github.com/AlexxIT/go2rtc (v1.9.14)
# NOTE: The default go2rtc API port (1984) must be used,
# changing this port for the integrated go2rtc instance is not supported.
go2rtc:
@@ -805,8 +875,8 @@ cameras:
# Required: name of the camera
back:
# Optional: Enable/Disable the camera (default: shown below).
- # If disabled: config is used but no live stream and no capture etc.
- # Events/Recordings are still viewable.
+ # When False, ffmpeg is not started and the camera is hidden from the UI
+ # (except Camera Management). Re-enabling requires a Frigate restart.
enabled: True
# Optional: camera type used for some Frigate features (default: shown below)
# Options are "generic" and "lpr"
@@ -840,6 +910,11 @@ cameras:
# Optional: camera specific output args (default: inherit)
# output_args:
+ # Optional: camera specific hwaccel args for timelapse export (default: inherit)
+ # record:
+ # export:
+ # hwaccel_args:
+
# Optional: timeout for highest scoring image before allowing it
# to be replaced by a newer image. (default: shown below)
best_image_timeout: 60
@@ -855,6 +930,9 @@ cameras:
front_steps:
# Optional: A friendly name or descriptive text for the zones
friendly_name: ""
+ # Optional: Whether this zone is active (default: shown below)
+ # Disabled zones are completely ignored at runtime - no object tracking or debug drawing
+ enabled: True
# Required: List of x,y coordinates to define the polygon of the zone.
# NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
@@ -865,6 +943,9 @@ cameras:
inertia: 3
# Optional: Number of seconds that an object must loiter to be considered in the zone (default: shown below)
loitering_time: 0
+ # Optional: Minimum speed required for an object to be considered present in the zone (default: none)
+ # In real-world units if distances are set. Used for speed-based zone triggers.
+ speed_threshold: 2.5
# Optional: List of objects that can trigger this zone (default: all tracked objects)
objects:
- person
@@ -900,15 +981,20 @@ cameras:
# Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
# By default the cameras are sorted alphabetically.
order: 0
- # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
+ # Optional: Whether or not to show the camera on the default All Cameras live dashboard.
+ # The camera is still available everywhere else, including camera groups and settings
+ # (default: shown below)
dashboard: True
+ # Optional: Whether this camera is visible in review (the review page and its camera
+ # filter, motion review, and the history view) (default: shown below)
+ review: True
# Optional: connect to ONVIF camera
# to enable PTZ controls.
onvif:
# Required: host of the camera being connected to.
# NOTE: HTTP is assumed by default; HTTPS is supported if you specify the scheme, ex: "https://0.0.0.0".
- # NOTE: ONVIF user, and password can be specified with environment variables or docker secrets
+ # NOTE: ONVIF host, user, and password can be specified with environment variables or docker secrets
# that must begin with 'FRIGATE_'. e.g. host: '{FRIGATE_ONVIF_USERNAME}'
host: 0.0.0.0
# Optional: ONVIF port for device (default: shown below).
@@ -923,6 +1009,10 @@ cameras:
# Optional: Ignores time synchronization mismatches between the camera and the server during authentication.
# Using NTP on both ends is recommended and this should only be set to True in a "safe" environment due to the security risk it represents.
ignore_time_mismatch: False
+ # Optional: ONVIF media profile to use for PTZ control, matched by token or name. (default: shown below)
+ # If not set, the first profile with valid PTZ configuration is selected automatically.
+ # Use this when your camera has multiple ONVIF profiles and you need to select a specific one.
+ profile: None
# Optional: PTZ camera object autotracking. Keeps a moving object in
# the center of the frame by automatically moving the PTZ camera.
autotracking:
@@ -986,6 +1076,49 @@ cameras:
actions:
- notification
+ # Optional: Named config profiles with partial overrides that can be activated at runtime.
+ # NOTE: Profile names must be defined in the top-level 'profiles' section.
+ profiles:
+ # Required: name of the profile (must match a top-level profile definition)
+ away:
+ # Optional: Enable or disable the camera when this profile is active (default: not set, inherits base)
+ enabled: true
+ # Optional: Override audio settings
+ audio:
+ enabled: true
+ # Optional: Override birdseye settings
+ # birdseye:
+ # Optional: Override detect settings
+ detect:
+ enabled: true
+ # Optional: Override face_recognition settings
+ # face_recognition:
+ # Optional: Override lpr settings
+ # lpr:
+ # Optional: Override motion settings
+ # motion:
+ # Optional: Override notification settings
+ notifications:
+ enabled: true
+ # Optional: Override objects settings
+ objects:
+ track:
+ - person
+ - car
+ # Optional: Override record settings
+ record:
+ enabled: true
+ # Optional: Override review settings
+ review:
+ alerts:
+ labels:
+ - person
+ - car
+ # Optional: Override snapshot settings
+ # snapshots:
+ # Optional: Override or add zones (merged with base zones)
+ # zones:
+
# Optional
ui:
# Optional: Set a timezone to use in the UI (default: use browser local time)
@@ -993,22 +1126,6 @@ ui:
# Optional: Set the time format used.
# Options are browser, 12hour, or 24hour (default: shown below)
time_format: browser
- # Optional: Set the date style for a specified length.
- # Options are: full, long, medium, short
- # Examples:
- # short: 2/11/23
- # medium: Feb 11, 2023
- # full: Saturday, February 11, 2023
- # (default: shown below).
- date_style: short
- # Optional: Set the time style for a specified length.
- # Options are: full, long, medium, short
- # Examples:
- # short: 8:14 PM
- # medium: 8:15:22 PM
- # full: 8:15:22 PM Mountain Standard Time
- # (default: shown below).
- time_style: medium
# Optional: Set the unit system to either "imperial" or "metric" (default: metric)
# Used in the UI and in MQTT topics
unit_system: metric
@@ -1052,4 +1169,14 @@ camera_groups:
icon: LuCar
# Required: index of this group
order: 0
+
+# Optional: Profile definitions for named config overrides
+# NOTE: Profile names defined here can be referenced in camera profiles sections
+profiles:
+ # Required: name of the profile (machine name used internally)
+ home:
+ # Required: display name shown in the UI
+ friendly_name: Home
+ away:
+ friendly_name: Away
```
diff --git a/docs/docs/configuration/advanced.md b/docs/docs/configuration/advanced/system.md
similarity index 56%
rename from docs/docs/configuration/advanced.md
rename to docs/docs/configuration/advanced/system.md
index c04cec97cc..75415c7053 100644
--- a/docs/docs/configuration/advanced.md
+++ b/docs/docs/configuration/advanced/system.md
@@ -1,15 +1,31 @@
---
-id: advanced
-title: Advanced Options
-sidebar_label: Advanced Options
+id: system
+title: System
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
### Logging
#### Frigate `logger`
Change the default log level for troubleshooting purposes.
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------- | ------------------------------------------------------- |
+| **Logging level** | The default log level for all modules (default: `info`) |
+| **Per-process log level** | Override the log level for specific modules |
+
+
+
+
```yaml
logger:
# Optional: default log level (default: shown below)
@@ -19,6 +35,9 @@ logger:
frigate.mqtt: error
```
+
+
+
Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
Examples of available modules are:
@@ -48,7 +67,26 @@ This section can be used to set environment variables for those unable to modify
Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
-Example:
+:::note
+
+The `go2rtc` section is an exception. go2rtc runs as a separate process, so its stream definitions can only be substituted with variables that exist in the container's environment (set via Docker `-e`, the `environment:` section of `docker-compose.yml`, or Docker secrets). Variables defined in the `environment_vars` block above are not available to go2rtc streams. Home Assistant app users, who cannot set container environment variables, must instead put credentials directly in their go2rtc stream URLs.
+
+:::
+
+
+
+
+Navigate to to add or edit environment variables.
+
+| Field | Description |
+| ----------------- | --------------------------------------------------------- |
+| **Variable name** | The environment variable name (e.g., `FRIGATE_MQTT_USER`) |
+| **Value** | The value for the variable |
+
+Variables defined here can be referenced elsewhere in your configuration using the `{FRIGATE_VARIABLE_NAME}` syntax.
+
+
+
```yaml
environment_vars:
@@ -61,10 +99,27 @@ mqtt:
password: "{FRIGATE_MQTT_PASSWORD}"
```
+
+
+
#### TensorFlow Thread Configuration
If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
+
+
+
+Navigate to and add the following variables:
+
+| Variable | Description |
+| --------------------------------- | ---------------------------------------------- |
+| `TF_INTRA_OP_PARALLELISM_THREADS` | Threads within operations (`0` = use default) |
+| `TF_INTER_OP_PARALLELISM_THREADS` | Threads between operations (`0` = use default) |
+| `TF_DATASET_THREAD_POOL_SIZE` | Data pipeline threads (`0` = use default) |
+
+
+
+
```yaml
environment_vars:
TF_INTRA_OP_PARALLELISM_THREADS: "2" # Threads within operations (0 = use default)
@@ -72,19 +127,35 @@ environment_vars:
TF_DATASET_THREAD_POOL_SIZE: "2" # Data pipeline threads (0 = use default)
```
+
+
+
### `database`
Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
-If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database in the config if necessary.
+If you are storing your database on a network share (SMB, NFS, etc), you may get a `database is locked` error message on startup. You can customize the location of the database if necessary.
This may need to be in a custom location if network storage is used for the media folder.
+
+
+
+Navigate to .
+
+- Set **Database path** to the custom path for the Frigate database file (default: `/config/frigate.db`)
+
+
+
+
```yaml
database:
path: /path/to/frigate.db
```
+
+
+
### `model`
If using a custom model, the width and height will need to be specified.
@@ -103,6 +174,22 @@ Custom models may also require different input tensor formats. The colorspace co
| "nhwc" |
| "nchw" |
+
+
+
+Navigate to and open the **Custom Model** tab to configure the model path, dimensions, and input format.
+
+| Field | Description |
+| --------------------------------------------- | ------------------------------------ |
+| **Custom object detector model path** | Path to the custom model file |
+| **Object detection model input width** | Model input width (default: 320) |
+| **Object detection model input height** | Model input height (default: 320) |
+| **Advanced > Model Input Tensor Shape** | Input tensor shape: `nhwc` or `nchw` |
+| **Advanced > Model Input Pixel Color Format** | Pixel format: `rgb`, `bgr`, or `yuv` |
+
+
+
+
```yaml
# Optional: model config
model:
@@ -113,11 +200,14 @@ model:
input_pixel_format: "bgr"
```
+
+
+
#### `labelmap`
:::warning
-If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../configuration/review.md#restricting-alerts-to-specific-labels) for more info.
+If the labelmap is customized then the labels used for alerts will need to be adjusted as well. See [alert labels](../review.md#restricting-alerts-to-specific-labels) for more info.
:::
@@ -149,7 +239,69 @@ Some labels have special handling and modifications can disable functionality.
## Network Configuration
-Changes to Frigate's internal network configuration can be made by bind mounting nginx.conf into the container. For example:
+Frigate exposes a few networking options. IPv6 and the listen ports are set in the `networking` configuration (or from the Settings UI); more advanced changes require [customizing the bundled Nginx configuration](#customizing-the-nginx-configuration).
+
+### Enabling IPv6
+
+By default Frigate listens on IPv4 only. To also listen on IPv6 (on port `5000`, and on `8971` when TLS is configured), enable it in the `networking` configuration.
+
+
+
+
+Navigate to and enable **IPv6**.
+
+
+
+
+```yaml
+networking:
+ ipv6:
+ enabled: true
+```
+
+
+
+
+### Listen on different ports
+
+You can change the ports Nginx uses for listening. The internal port (unauthenticated) and external port (authenticated) can be changed independently. You can also specify an IP address using the format `ip:port` if you wish to bind the port to a specific interface. This may be useful for example to prevent exposing the internal port outside the container.
+
+
+
+
+Navigate to to configure the listen ports.
+
+| Field | Description |
+| ----------------- | --------------------------------------------------------- |
+| **Internal port** | The unauthenticated listen address/port (default: `5000`) |
+| **External port** | The authenticated listen address/port (default: `8971`) |
+
+
+
+
+```yaml
+networking:
+ listen:
+ internal: 127.0.0.1:5000
+ external: 8971
+```
+
+
+
+
+:::warning
+
+This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run` `--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
+
+The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
+
+Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
+
+:::
+
+### Customizing the Nginx configuration
+
+More advanced changes to Frigate's internal network configuration can be made by bind mounting your own `nginx.conf` into the container. For example:
```yaml
services:
@@ -161,36 +313,6 @@ services:
- /path/to/your/nginx.conf:/usr/local/nginx/conf/nginx.conf
```
-### Enabling IPv6
-
-IPv6 is disabled by default, to enable IPv6 listen.gotmpl needs to be bind mounted with IPv6 enabled. For example:
-
-```
-{{ if not .enabled }}
-# intended for external traffic, protected by auth
-listen 8971;
-{{ else }}
-# intended for external traffic, protected by auth
-listen 8971 ssl;
-
-# intended for internal traffic, not protected by auth
-listen 5000;
-```
-
-becomes
-
-```
-{{ if not .enabled }}
-# intended for external traffic, protected by auth
-listen [::]:8971 ipv6only=off;
-{{ else }}
-# intended for external traffic, protected by auth
-listen [::]:8971 ipv6only=off ssl;
-
-# intended for internal traffic, not protected by auth
-listen [::]:5000 ipv6only=off;
-```
-
## Base path
By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.
@@ -217,7 +339,7 @@ For example:
```
services:
frigate:
- image: blakeblackshear/frigate:latest
+ image: ghcr.io/blakeblackshear/frigate:stable
environment:
- FRIGATE_BASE_PATH=/frigate
```
@@ -242,7 +364,7 @@ To do this:
### Custom go2rtc version
-Frigate currently includes go2rtc v1.9.10, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.14, there may be certain cases where you want to run a different version of go2rtc.
To do this:
diff --git a/docs/docs/configuration/audio_detectors.md b/docs/docs/configuration/audio_detectors.md
index 9576679147..39ec2e9ff9 100644
--- a/docs/docs/configuration/audio_detectors.md
+++ b/docs/docs/configuration/audio_detectors.md
@@ -3,6 +3,10 @@ id: audio_detectors
title: Audio Detectors
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
Frigate provides a builtin audio detector which runs on the CPU. Compared to object detection in images, audio detection is a relatively lightweight operation so the only option is to run the detection on a CPU.
## Configuration
@@ -11,7 +15,17 @@ Audio events work by detecting a type of audio and creating an event, the event
### Enabling Audio Events
-Audio events can be enabled for all cameras or only for specific cameras.
+Audio events can be enabled globally or for specific cameras.
+
+
+
+
+**Global:** Navigate to and set **Enable audio detection** to on.
+
+**Per-camera:** Navigate to and set **Enable audio detection** to on for the desired camera.
+
+
+
```yaml
@@ -26,6 +40,9 @@ cameras:
enabled: True # <- enable audio events for the front_camera
```
+
+
+
If you are using multiple streams then you must set the `audio` role on the stream that is going to be used for audio detection, this can be any stream but the stream must have audio included.
:::note
@@ -34,6 +51,14 @@ The ffmpeg process for capturing audio will be a separate connection to the came
:::
+
+
+
+Navigate to and add an input with the `audio` role pointing to a stream that includes audio.
+
+
+
+
```yaml
cameras:
front_camera:
@@ -48,9 +73,12 @@ cameras:
- detect
```
+
+
+
### Configuring Minimum Volume
-The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are is displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
+The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that Frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The [Debug view](/usage/live#the-single-camera-view) in the Frigate UI has an Audio tab for cameras that have the `audio` role assigned where a graph and the current levels are displayed. The `min_volume` parameter should be set to the minimum the `RMS` level required to run audio detection.
:::tip
@@ -60,7 +88,18 @@ Volume is considered motion for recordings, this means when the `record -> retai
### Configuring Audio Events
-The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `scream`, `speech`, and `yell` are enabled but these can be customized.
+The included audio model has over [500 different types](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) of audio that can be detected, many of which are not practical. By default `bark`, `fire_alarm`, `speech`, and `yell` are enabled but these can be customized.
+
+
+
+
+Navigate to .
+
+- Set **Enable audio detection** to on
+- Set **Listen types** to include the audio types you want to detect
+
+
+
```yaml
audio:
@@ -68,20 +107,106 @@ audio:
listen:
- bark
- fire_alarm
- - scream
- speech
- yell
```
+
+
+
+### Common Audio Labels
+
+The labelmap includes hundreds of sound types. The labels below are the ones most users may find practical, grouped by what they're typically used for. Use the exact label string from the left column in your `listen` config, or search for the label in the Frigate UI directly.
+
+Some labels cover several related sounds: `yell` is triggered by shouting, yelling, children shouting, and screaming; `crying` covers baby cries, sobbing, and whimpering; and `speech` covers ordinary talking and conversation.
+
+**Safety and security**
+
+| Label | Detects |
+| ---------------- | ---------------------------------- |
+| `yell` | Shouting, yelling, screaming |
+| `fire_alarm` | Fire and smoke alarm sirens |
+| `smoke_detector` | Smoke detector beeps |
+| `alarm` | General alarm sounds |
+| `car_alarm` | Car alarms |
+| `siren` | Emergency vehicle and civil sirens |
+| `glass` | Glass clinking |
+| `shatter` | Breaking glass |
+| `breaking` | Something breaking |
+| `gunshot` | Gunshots |
+| `explosion` | Explosions |
+
+**People and activity**
+
+| Label | Detects |
+| ----------- | ------------------------ |
+| `speech` | Talking and conversation |
+| `laughter` | Laughing |
+| `crying` | Baby crying and sobbing |
+| `cough` | Coughing |
+| `footsteps` | Footsteps and walking |
+| `knock` | Knocking on a door |
+| `doorbell` | Doorbell |
+| `ding-dong` | Doorbell chime |
+
+**Pets and animals**
+
+| Label | Detects |
+| ---------- | ---------------- |
+| `bark` | Dog barking |
+| `dog` | Other dog sounds |
+| `howl` | Howling |
+| `growling` | Growling |
+| `meow` | Cat meowing |
+| `cat` | Other cat sounds |
+| `hiss` | Hissing |
+
+**Vehicles and driveway**
+
+| Label | Detects |
+| ----------------- | -------------------- |
+| `car` | Passing cars |
+| `honk` | Car horns |
+| `truck` | Trucks |
+| `reversing_beeps` | Vehicle backup beeps |
+| `motorcycle` | Motorcycles |
+| `engine_starting` | Engines starting |
+
+:::tip
+
+Frequently-heard labels like `speech` can generate a lot of events, and each event could save a snapshot and recording based on your configuration, so start with a focused set and expand from there. The defaults (`bark`, `fire_alarm`, `speech`, `yell`) plus a few of the safety labels above cover most needs. See the [full audio labelmap](https://github.com/blakeblackshear/frigate/blob/dev/audio-labelmap.txt) or the Frigate UI for every available type.
+
+:::
+
### Audio Transcription
-Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
+Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
+
+:::info
+
+Audio transcription requires a one-time internet connection to download the Whisper or Sherpa-ONNX model on first use. Once cached, transcription runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
#### Configuration
-To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
+To enable transcription, configure it globally and optionally disable for specific cameras. Audio detection must also be enabled as described above.
+
+
+
+
+**Global:** Navigate to .
+
+- Set **Enable audio transcription** to on
+- Set **Transcription device** to the desired device
+- Set **Model size** to the desired size
+
+**Per-camera:** Navigate to to enable or disable transcription for a specific camera.
+
+
+
```yaml
audio_transcription:
@@ -100,6 +225,9 @@ cameras:
enabled: False
```
+
+
+
:::note
Audio detection must be enabled and configured as described above in order to use audio transcription features.
@@ -128,7 +256,7 @@ The only field that is valid at the camera level is `enabled`.
#### Live transcription
-The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate//audio/transcription` will also be updated in real-time with transcribed text.
+The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing, or toggle it outside of the UI with the [`frigate//audio_transcription/set`](/integrations/mqtt#frigatecamera_nameaudio_transcriptionset) MQTT topic or the HTTP API. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate//audio/transcription` will also be updated in real-time with transcribed text.
Results can be error-prone due to a number of factors, including:
@@ -144,9 +272,9 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
#### Transcription and translation of `speech` audio events
-Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
+Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
-In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for in your config. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
+In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
The transcribed/translated speech will appear in the description box in the Tracked Object Details pane. If Semantic Search is enabled, embeddings are generated for the transcription text and are fully searchable using the description search type.
@@ -162,16 +290,16 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
1. Why doesn't Frigate automatically transcribe all `speech` events?
- Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
+ Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That's a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
- If you hear speech that’s actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
+ If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button (the microphone icon) in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
2. Why don't you save live transcription text and use that for `speech` events?
- There’s no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
+ There's no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
- Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.
+ Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That's why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.
diff --git a/docs/docs/configuration/authentication.md b/docs/docs/configuration/authentication.md
index 694c4badaf..94d46dba07 100644
--- a/docs/docs/configuration/authentication.md
+++ b/docs/docs/configuration/authentication.md
@@ -3,6 +3,10 @@ id: authentication
title: Authentication
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
# Authentication
Frigate stores user information in its database. Password hashes are generated using industry standard PBKDF2-SHA256 with 600,000 iterations. Upon successful login, a JWT token is issued with an expiration date and set as a cookie. The cookie is refreshed as needed automatically. This JWT token can also be passed in the Authorization header as a bearer token.
@@ -22,13 +26,26 @@ On startup, an admin user and password are generated and printed in the logs. It
## Resetting admin password
-In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup using the `reset_admin_password` setting in your config file.
+In the event that you are locked out of your instance, you can tell Frigate to reset the admin password and print it in the logs on next startup.
+
+
+
+
+Navigate to .
+
+- Set **Reset admin password** to on to reset the admin password and print it in the logs on next startup
+
+
+
```yaml
auth:
reset_admin_password: true
```
+
+
+
## Password guidance
Constructing secure passwords and managing them properly is important. Frigate requires a minimum length of 12 characters. For guidance on password standards see [NIST SP 800-63B](https://pages.nist.gov/800-63-3/sp800-63b.html). To learn what makes a password truly secure, read this [article](https://medium.com/peerio/how-to-build-a-billion-dollar-password-3d92568d9277).
@@ -47,7 +64,20 @@ Restarting Frigate will reset the rate limits.
If you are running Frigate behind a proxy, you will want to set `trusted_proxies` or these rate limits will apply to the upstream proxy IP address. This means that a brute force attack will rate limit login attempts from other devices and could temporarily lock you out of your instance. In order to ensure rate limits only apply to the actual IP address where the requests are coming from, you will need to list the upstream networks that you want to trust. These trusted proxies are checked against the `X-Forwarded-For` header when looking for the IP address where the request originated.
-If you are running a reverse proxy in the same Docker Compose file as Frigate, here is an example of how your auth config might look:
+If you are running a reverse proxy in the same Docker Compose file as Frigate, configure rate limiting and trusted proxies as follows:
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ----------------------- | ------------------------------------------------------------------------------------------------------------------------- |
+| **Failed login limits** | Rate limit string for login failures (e.g., `1/second;5/minute;20/hour`) |
+| **Trusted proxies** | List of upstream network CIDRs to trust for `X-Forwarded-For` (e.g., `172.18.0.0/16` for internal Docker Compose network) |
+
+
+
```yaml
auth:
@@ -56,9 +86,12 @@ auth:
- 172.18.0.0/16 # <---- this is the subnet for the internal Docker Compose network
```
+
+
+
## Session Length
-The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required — otherwise, the user will need to log in again.
+The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required. Otherwise, the user will need to log in again.
While the default provides a balance of security and convenience, you can customize this duration to suit your specific security requirements and user experience preferences. The session length is configured in seconds.
@@ -67,11 +100,24 @@ The default value of `86400` will expire the authentication session after 24 hou
- `0`: Setting the session length to 0 will require a user to log in every time they access the application or after a very short, immediate timeout.
- `604800`: Setting the session length to 604800 will require a user to log in if the token is not refreshed for 7 days.
+
+
+
+Navigate to .
+
+- Set **Session length** to the duration in seconds before the authentication session expires (default: 86400 / 24 hours)
+
+
+
+
```yaml
auth:
session_length: 86400
```
+
+
+
## JWT Token Secret
The JWT token secret needs to be kept secure. Anyone with this secret can generate valid JWT tokens to authenticate with Frigate. This should be a cryptographically random string of at least 64 characters.
@@ -95,11 +141,22 @@ Changing the secret will invalidate current tokens.
## Proxy configuration
-Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth.
+Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth. Frigate does not implement OIDC, SAML, or LDAP natively; as an NVR focused on recording and object detection, it relies on robust, battle-tested proxies to handle those protocols and passes the authenticated user and role through via headers (see below).
If you are leveraging the authentication of an upstream proxy, you likely want to disable Frigate's authentication as there is no correspondence between users in Frigate's database and users authenticated via the proxy. Optionally, if communication between the reverse proxy and Frigate is over an untrusted network, you should set an `auth_secret` in the `proxy` config and configure the proxy to send the secret value as a header named `X-Proxy-Secret`. Assuming this is an untrusted network, you will also want to [configure a real TLS certificate](tls.md) to ensure the traffic can't simply be sniffed to steal the secret.
-Here is an example of how to disable Frigate's authentication and also ensure the requests come only from your known proxy.
+To disable Frigate's authentication and ensure requests come only from your known proxy:
+
+
+
+
+1. Navigate to .
+ - Set **Enable authentication** to off
+2. Navigate to .
+ - Set **Proxy secret** to ``
+
+
+
```yaml
auth:
@@ -109,6 +166,9 @@ proxy:
auth_secret:
```
+
+
+
You can use the following code to generate a random secret.
```shell
@@ -119,6 +179,20 @@ python3 -c 'import secrets; print(secrets.token_hex(64))'
If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Groups` values. Header names are not case sensitive. Multiple values can be included in the role header. Frigate expects that the character separating the roles is a comma, but this can be specified using the `separator` config entry.
+
+
+
+Navigate to and configure the header mapping and separator settings.
+
+| Field | Description |
+| -------------------------------- | ---------------------------------------------------------------------------------------------------- |
+| **Separator character** | Character separating multiple roles in the role header (default: comma). Authentik uses a pipe `\|`. |
+| **Header mapping > User header** | Header name for the authenticated username (e.g., `x-forwarded-user`) |
+| **Header mapping > Role header** | Header name for the authenticated role/groups (e.g., `x-forwarded-groups`) |
+
+
+
+
```yaml
proxy:
...
@@ -128,19 +202,37 @@ proxy:
role: x-forwarded-groups
```
+
+
+
Frigate supports `admin`, `viewer`, and custom roles (see below). When using port `8971`, Frigate validates these headers and subsequent requests use the headers `remote-user` and `remote-role` for authorization.
A default role can be provided. Any value in the mapped `role` header will override the default.
+
+
+
+Navigate to and set the default role.
+
+| Field | Description |
+| ---------------- | ------------------------------------------------------------- |
+| **Default role** | Fallback role when no role header is present (e.g., `viewer`) |
+
+
+
+
```yaml
proxy:
...
default_role: viewer
```
+
+
+
## Role mapping
-In some environments, upstream identity providers (OIDC, SAML, LDAP, etc.) do not pass a Frigate-compatible role directly, but instead pass one or more group claims. To handle this, Frigate supports a `role_map` that translates upstream group names into Frigate’s internal roles (`admin`, `viewer`, or custom).
+In some environments, upstream identity providers (OIDC, SAML, LDAP, etc.) do not pass a Frigate-compatible role directly, but instead pass one or more group claims. To handle this, Frigate supports a `role_map` that translates upstream group names into Frigate's internal roles (`admin`, `viewer`, or custom). This is configurable via YAML in the configuration file:
```yaml
proxy:
@@ -170,12 +262,25 @@ In this example:
- Admin precedence: if the `admin` mapping matches, Frigate resolves the session to `admin` to avoid accidental downgrade when a user belongs to multiple groups (for example both `admin` and `viewer` groups).
+:::note
+
+If a user isn't getting the role you expect, enable debug logging to see exactly what headers Frigate is receiving from your proxy:
+
+```yaml
+logger:
+ default: info
+ logs:
+ frigate.api.auth: debug
+```
+
+:::
+
#### Port Considerations
**Authenticated Port (8971)**
- Header mapping is **fully supported**.
-- The `remote-role` header determines the user’s privileges:
+- The `remote-role` header determines the user's privileges:
- **admin** → Full access (user management, configuration changes).
- **viewer** → Read-only access.
- **Custom roles** → Read-only access limited to the cameras defined in `auth.roles[role]`.
@@ -232,6 +337,14 @@ The viewer role provides read-only access to all cameras in the UI and API. Cust
### Role Configuration Example
+
+
+
+Navigate to to define custom roles and assign which cameras each role can access.
+
+
+
+
```yaml {11-16}
cameras:
front_door:
@@ -251,13 +364,16 @@ auth:
- side_yard
```
+
+
+
If you want to provide access to all cameras to a specific user, just use the **viewer** role.
### Managing User Roles
1. Log in as an **admin** user via port `8971` (preferred), or unauthenticated via port `5000`.
2. Navigate to **Settings**.
-3. In the **Users** section, edit a user’s role by selecting from available roles (admin, viewer, or custom).
+3. In the **Users** section, edit a user's role by selecting from available roles (admin, viewer, or custom).
4. In the **Roles** section, add/edit/delete custom roles (select cameras via switches). Deleting a role auto-reassigns users to "viewer".
### Role Enforcement
@@ -277,7 +393,7 @@ To use role-based access control, you must connect to Frigate via the **authenti
1. Log in as an **admin** user via port `8971`.
2. Navigate to **Settings > Users**.
-3. Edit a user’s role by selecting **admin** or **viewer**.
+3. Edit a user's role by selecting **admin** or **viewer**.
## API Authentication Guide
diff --git a/docs/docs/configuration/autotracking.md b/docs/docs/configuration/autotracking.md
index 86179a2641..f49026e11c 100644
--- a/docs/docs/configuration/autotracking.md
+++ b/docs/docs/configuration/autotracking.md
@@ -3,6 +3,11 @@ id: autotracking
title: Camera Autotracking
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
+
An ONVIF-capable, PTZ (pan-tilt-zoom) camera that supports relative movement within the field of view (FOV) can be configured to automatically track moving objects and keep them in the center of the frame.
@@ -29,12 +34,44 @@ A growing list of cameras and brands that have been reported by users to work wi
First, set up a PTZ preset in your camera's firmware and give it a name. If you're unsure how to do this, consult the documentation for your camera manufacturer's firmware. Some tutorials for common brands: [Amcrest](https://www.youtube.com/watch?v=lJlE9-krmrM), [Reolink](https://www.youtube.com/watch?v=VAnxHUY5i5w), [Dahua](https://www.youtube.com/watch?v=7sNbc5U-k54).
-Edit your Frigate configuration file and enter the ONVIF parameters for your camera. Specify the object types to track, a required zone the object must enter to begin autotracking, and the camera preset name you configured in your camera's firmware to return to when tracking has ended. Optionally, specify a delay in seconds before Frigate returns the camera to the preset.
+Configure the ONVIF connection and autotracking parameters for your camera. Specify the object types to track, a required zone the object must enter to begin autotracking, and the camera preset name you configured in your camera's firmware to return to when tracking has ended. Optionally, specify a delay in seconds before Frigate returns the camera to the preset.
An [ONVIF connection](cameras.md) is required for autotracking to function. Also, a [motion mask](masks.md) over your camera's timestamp and any overlay text is recommended to ensure they are completely excluded from scene change calculations when the camera is moving.
Note that `autotracking` is disabled by default but can be enabled in the configuration or by MQTT.
+
+
+
+Navigate to for the desired camera.
+
+**ONVIF Connection**
+
+| Field | Description |
+| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **ONVIF host** | Host of the camera being connected to. HTTP is assumed by default; prefix with `https://` for HTTPS. |
+| **ONVIF port** | ONVIF port for device (default: 8000) |
+| **ONVIF username** | Username for login. Some devices require admin to access ONVIF. |
+| **ONVIF password** | Password for login |
+| **Disable TLS verify** | Skip TLS verification and disable digest auth for ONVIF (default: false) |
+| **ONVIF profile** | ONVIF media profile to use for PTZ control, matched by token or name. If not set, the first profile with valid PTZ configuration is selected automatically. |
+
+**Autotracking**
+
+| Field | Description |
+| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
+| **Enable Autotracking** | Enable or disable object autotracking (default: false) |
+| **Calibrate on start** | Calibrate the camera on startup by measuring PTZ motor speed (default: false) |
+| **Zoom mode** | Zoom mode during autotracking: `disabled`, `absolute`, or `relative` (default: disabled) |
+| **Zoom Factor** | Controls zoom behavior on tracked objects, between 0.1 and 0.75. Lower keeps more scene visible; higher zooms in more (default: 0.3) |
+| **Tracked objects** | List of object types to track (default: person) |
+| **Required Zones** | Zones an object must enter to begin autotracking |
+| **Return Preset** | Name of ONVIF preset in camera firmware to return to when tracking ends (default: home) |
+| **Return timeout** | Seconds to delay before returning to preset (default: 10) |
+
+
+
+
```yaml
cameras:
ptzcamera:
@@ -52,6 +89,10 @@ cameras:
password: admin
# Optional: Skip TLS verification from the ONVIF server (default: shown below)
tls_insecure: False
+ # Optional: ONVIF media profile to use for PTZ control, matched by token or name. (default: shown below)
+ # If not set, the first profile with valid PTZ configuration is selected automatically.
+ # Use this when your camera has multiple ONVIF profiles and you need to select a specific one.
+ profile: None
# Optional: PTZ camera object autotracking. Keeps a moving object in
# the center of the frame by automatically moving the PTZ camera.
autotracking:
@@ -88,13 +129,16 @@ cameras:
movement_weights: []
```
+
+
+
## Calibration
PTZ motors operate at different speeds. Performing a calibration will direct Frigate to measure this speed over a variety of movements and use those measurements to better predict the amount of movement necessary to keep autotracked objects in the center of the frame.
Calibration is optional, but will greatly assist Frigate in autotracking objects that move across the camera's field of view more quickly.
-To begin calibration, set the `calibrate_on_startup` for your camera to `True` and restart Frigate. Frigate will then make a series of small and large movements with your camera. Don't move the PTZ manually while calibration is in progress. Once complete, camera motion will stop and your config file will be automatically updated with a `movement_weights` parameter to be used in movement calculations. You should not modify this parameter manually.
+To begin calibration, set `calibrate_on_startup` for your camera to `True` and restart Frigate. Frigate will then make a series of small and large movements with your camera. Don't move the PTZ manually while calibration is in progress. Once complete, camera motion will stop and your config file will be automatically updated with a `movement_weights` parameter to be used in movement calculations. You should not modify this parameter manually.
After calibration has ended, your PTZ will be moved to the preset specified by `return_preset`.
@@ -118,13 +162,13 @@ Every PTZ camera is different, so autotracking may not perform ideally in every
The object tracker in Frigate estimates the motion of the PTZ so that tracked objects are preserved when the camera moves. In most cases 5 fps is sufficient, but if you plan to track faster moving objects, you may want to increase this slightly. Higher frame rates (> 10fps) will only slow down Frigate and the motion estimator and may lead to dropped frames, especially if you are using experimental zooming.
-A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's debug viewer for your camera to see a thicker colored box around the object currently being autotracked.
+A fast [detector](object_detectors.md) is recommended. CPU detectors will not perform well or won't work at all. You can watch Frigate's [debug viewer](/usage/live#the-single-camera-view) for your camera to see a thicker colored box around the object currently being autotracked.
A full-frame zone in `required_zones` is not recommended, especially if you've calibrated your camera and there are `movement_weights` defined in the configuration file. Frigate will continue to autotrack an object that has entered one of the `required_zones`, even if it moves outside of that zone.
-Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](index.md).
+Some users have found it helpful to adjust the zone `inertia` value. See the [configuration reference](advanced/reference.md).
## Zooming
@@ -144,30 +188,96 @@ In security and surveillance, it's common to use "spotter" cameras in combinatio
## Troubleshooting and FAQ
-### The autotracker loses track of my object. Why?
+### Camera Compatibility
+
+
+
+See the community-maintained list of [ONVIF PTZ camera recommendations](cameras.md#onvif-ptz-camera-recommendations) for cameras and brands reported to work (and not work) with autotracking. This is not an exhaustive list that is frequently updated, so other cameras not listed may also work well. Frigate's autotracking was developed with a Dahua SD1A404XB-GNR (now sold as the EmpireTech PTZ1A4M-4X-S2), and Dahua / EmpireTech PTZs are the most consistently reported as working well.
+
+When comparing models:
+
+- Verify ONVIF support first. See [Checking ONVIF camera support](#checking-onvif-camera-support) above.
+- Favor a camera with a fast PTZ motor. Cameras with slow motors may fail [calibration](#calibration) and will struggle to keep up with objects that move across the field of view quickly.
+
+
+
+
+
+No. Reolink cameras (including the TrackMix series) lack the ONVIF FOV RelativeMove firmware support that Frigate's autotracker requires, so autotracking will not work with any current Reolink PTZ. Their video streams and basic PTZ controls still work in Frigate. If you want object tracking on a Reolink PTZ, you will need to use the tracking feature built into the camera's firmware, which is proprietary and operates independently of Frigate.
+
+
+
+
+
+There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware, such as some Annke models. In rare cases the vendor may provide fixed firmware on request; for example, Annke has supplied firmware that resolves this for the CZ504 (see the [camera recommendations list](cameras.md#onvif-ptz-camera-recommendations)).
+
+
+
+
+
+Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
+
+
+
+### Calibration Issues
+
+
+
+This is often caused by the same reason as the "MOVING" status error above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
+
+
+
+
+
+Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner.
+
+Some things to try:
+
+- If your camera's firmware has a PTZ or motor speed setting, set it to the fastest available speed and calibrate again.
+- Run without calibration: remove the `movement_weights` line from your config, set `calibrate_on_startup` to `False`, and restart.
+
+If calibration consistently fails, this often means your camera's motor is too slow and autotracking will behave unpredictably or won't be able to keep up with moving objects.
+
+
+
+
+
+Frigate uses the `movement_weights` measured during calibration to predict how far the camera needs to move to keep an object centered, so inaccurate values produce movements that don't seem to make sense: overshooting, moving the opposite direction, or zooming in on an object's last known position and losing it entirely. This is almost always a calibration issue.
+
+- Remove the `movement_weights` entry from your config and restart Frigate to run without calibration. If tracking improves, try recalibrating.
+- Recalibrate several times. The `movement_weights` values should be close to each other after each run. If they vary significantly between runs, your camera may not be reporting its motor status reliably, and you may get better results without calibration.
+- If you are using zooming, a high `zoom_factor` can cause the camera to zoom in too far and lose the object. Try a lower value.
+
+Remember to recalibrate whenever you change your `return_preset`, change your camera's detect `fps`, or enable zooming after calibrating with it disabled.
+
+
+
+### Tracking Behavior
+
+
There are many reasons this could be the case. If you are using experimental zooming, your `zoom_factor` value might be too high, the object might be traveling too quickly, the scene might be too dark, there are not enough details in the scene (for example, a PTZ looking down on a driveway or other monotone background without a sufficient number of hard edges or corners), or the scene is otherwise less than optimal for Frigate to maintain tracking.
Your camera's shutter speed may also be set too low so that blurring occurs with motion. Check your camera's firmware to see if you can increase the shutter speed.
-Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it.
+Watching Frigate's debug view can help to determine a possible cause. The autotracked object will have a thicker colored box around it. If the camera consistently zooms in on the object and then loses it, see [Autotracking is erratic, moves the camera in the wrong direction, or zooms past my object. Why?](#autotracking-is-erratic-or-moves-the-camera-in-the-wrong-direction) above.
-### I'm seeing an error in the logs that my camera "is still in ONVIF 'MOVING' status." What does this mean?
+
-There are two possible known reasons for this (and perhaps others yet unknown): a slow PTZ motor or buggy camera firmware. Frigate uses an ONVIF parameter provided by the camera, `MoveStatus`, to determine when the PTZ's motor is moving or idle. According to some users, Hikvision PTZs (even with the latest firmware), are not updating this value after PTZ movement. Unfortunately there is no workaround to this bug in Hikvision firmware, so autotracking will not function correctly and should be disabled in your config. This may also be the case with other non-Hikvision cameras utilizing Hikvision firmware.
-
-### I tried calibrating my camera, but the logs show that it is stuck at 0% and Frigate is not starting up.
-
-This is often caused by the same reason as above - the `MoveStatus` ONVIF parameter is not changing due to a bug in your camera's firmware. Also, see the note above: Frigate's web UI and all other cameras will be unresponsive while calibration is in progress. This is expected and normal. But if you don't see log entries every few seconds for calibration progress, your camera is not compatible with autotracking.
-
-### I'm seeing this error in the logs: "Autotracker: motion estimator couldn't get transformations". What does this mean?
+
To maintain object tracking during PTZ moves, Frigate tracks the motion of your camera based on the details of the frame. If you are seeing this message, it could mean that your `zoom_factor` may be set too high, the scene around your detected object does not have enough details (like hard edges or color variations), or your camera's shutter speed is too slow and motion blur is occurring. Try reducing `zoom_factor`, finding a way to alter the scene around your object, or changing your camera's shutter speed.
-### Calibration seems to have completed, but the camera is not actually moving to track my object. Why?
+
-Some cameras have firmware that reports that FOV RelativeMove, the ONVIF command that Frigate uses for autotracking, is supported. However, if the camera does not pan or tilt when an object comes into the required zone, your camera's firmware does not actually support FOV RelativeMove. One such camera is the Uniview IPC672LR-AX4DUPK. It actually moves its zoom motor instead of panning and tilting and does not follow the ONVIF standard whatsoever.
+
-### Frigate reports an error saying that calibration has failed. Why?
+When the PTZ moves, the entire frame changes at once. Frigate's motion detection treats sudden scene-wide changes (like a lightning flash, an infrared mode switch, or a camera move) specially and pauses detection momentarily until the scene stabilizes. This is expected and normal, and detection resumes shortly after the camera stops moving. If detection does not resume once the camera is stationary, use the [debug view](/usage/live#the-single-camera-view) to see what is happening.
-Calibration measures the amount of time it takes for Frigate to make a series of movements with your PTZ. This error message is recorded in the log if these values are too high for Frigate to support calibrated autotracking. This is often the case when your camera's motor or network connection is too slow or your camera's firmware doesn't report the motor status in a timely manner. You can try running without calibration (just remove the `movement_weights` line from your config and restart), but if calibration fails, this often means that autotracking will behave unpredictably.
+
+
+
+
+Yes. Autotracking can be toggled per camera at runtime over MQTT with the [`frigate//ptz_autotracker/set`](../integrations/mqtt.md#frigatecamera_nameptz_autotrackerset) topic, and the [Home Assistant integration](../integrations/home-assistant.md) exposes a switch for it. This pairs well with the "spotter" camera automations described in [Usage applications](#usage-applications) above, for example only enabling autotracking at night or when nobody is home.
+
+
diff --git a/docs/docs/configuration/bird_classification.md b/docs/docs/configuration/bird_classification.md
index 3987292905..1c521314e0 100644
--- a/docs/docs/configuration/bird_classification.md
+++ b/docs/docs/configuration/bird_classification.md
@@ -3,8 +3,18 @@ id: bird_classification
title: Bird Classification
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
+:::info
+
+Bird classification requires a one-time internet connection to download the classification model and label map from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
## Minimum System Requirements
Bird classification runs a lightweight tflite model on the CPU, there are no significantly different system requirements than running Frigate itself.
@@ -15,7 +25,18 @@ The classification model used is the MobileNet INat Bird Classification, [availa
## Configuration
-Bird classification is disabled by default, it must be enabled in your config file before it can be used. Bird classification is a global configuration setting.
+Bird classification is disabled by default and must be enabled before it can be used. Bird classification is a global configuration setting.
+
+
+
+
+Navigate to .
+
+- Set **Bird classification config > Bird classification** to on
+- Set **Bird classification config > Minimum score** to the desired confidence score (default: 0.9)
+
+
+
```yaml
classification:
@@ -23,6 +44,9 @@ classification:
enabled: true
```
+
+
+
## Advanced Configuration
Fine-tune bird classification with these optional parameters:
diff --git a/docs/docs/configuration/birdseye.md b/docs/docs/configuration/birdseye.md
index f48299aec6..26a2386d89 100644
--- a/docs/docs/configuration/birdseye.md
+++ b/docs/docs/configuration/birdseye.md
@@ -1,11 +1,21 @@
# Birdseye
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
In addition to Frigate's Live camera dashboard, Birdseye allows a portable heads-up view of your cameras to see what is going on around your property / space without having to watch all cameras that may have nothing happening. Birdseye allows specific modes that intelligently show and disappear based on what you care about.
-Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the "+" icon on the Live page, and choose "Birdseye" as one of the cameras.
+Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the pencil icon in the sidebar on the Live page, and choose "Birdseye" as one of the cameras.
Birdseye can also be used in Home Assistant dashboards, cast to media devices, etc.
+:::note
+
+Each camera tile in Birdseye is composed from the frames of the stream assigned the `detect` role, so a camera's image quality in Birdseye matches its detect stream resolution rather than a higher-resolution recording stream. If a camera looks low quality in Birdseye, increasing the detect width and height (or assigning the `detect` role to a higher-resolution stream) is what affects it. See [setting up camera inputs](./cameras.md#setting-up-camera-inputs) for how roles are assigned.
+
+:::
+
## Birdseye Behavior
### Birdseye Modes
@@ -22,7 +32,22 @@ A custom icon can be added to the birdseye background by providing a 180x180 ima
### Birdseye view override at camera level
-If you want to include a camera in Birdseye view only for specific circumstances, or just don't include it at all, the Birdseye setting can be set at the camera level.
+To include a camera in Birdseye view only for specific circumstances, or exclude it entirely, configure Birdseye at the camera level.
+
+
+
+
+**Global settings:** Navigate to to configure the default Birdseye behavior for all cameras.
+
+**Per-camera overrides:** Navigate to to override the mode or disable Birdseye for a specific camera.
+
+| Field | Description |
+| ------------------- | ------------------------------------------------------------- |
+| **Enable Birdseye** | Whether this camera appears in Birdseye view |
+| **Tracking mode** | When to show the camera: `continuous`, `motion`, or `objects` |
+
+
+
```yaml {8-10,12-14}
# Include all cameras by default in Birdseye view
@@ -41,9 +66,24 @@ cameras:
enabled: False
```
+
+
+
### Birdseye Inactivity
-By default birdseye shows all cameras that have had the configured activity in the last 30 seconds, this can be configured:
+By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------ | --------------------------------------------------------------------------- |
+| **Inactivity threshold** | Seconds of inactivity before a camera is hidden from Birdseye (default: 30) |
+
+
+
```yaml
birdseye:
@@ -52,12 +92,28 @@ birdseye:
inactivity_threshold: 15
```
+
+
+
## Birdseye Layout
### Birdseye Dimensions
The resolution and aspect ratio of birdseye can be configured. Resolution will increase the quality but does not affect the layout. Changing the aspect ratio of birdseye does affect how cameras are laid out.
+
+
+
+Navigate to .
+
+| Field | Description |
+| ---------- | ----------------------------------------------- |
+| **Width** | Birdseye output width in pixels (default: 1280) |
+| **Height** | Birdseye output height in pixels (default: 720) |
+
+
+
+
```yaml
birdseye:
enabled: True
@@ -65,10 +121,20 @@ birdseye:
height: 720
```
+
+
+
### Sorting cameras in the Birdseye view
-It is possible to override the order of cameras that are being shown in the Birdseye view.
-The order needs to be set at the camera level.
+It is possible to override the order of cameras that are being shown in the Birdseye view. The order is set at the camera level (when using YAML).
+
+
+
+
+Navigate to and in the **Camera order** field, use the drag handle next to each camera name to control the display order.
+
+
+
```yaml
# Include all cameras by default in Birdseye view
@@ -87,13 +153,26 @@ cameras:
order: 2
```
+
+
+
_Note_: Cameras are sorted by default using their name to ensure a constant view inside Birdseye.
### Birdseye Cameras
It is possible to limit the number of cameras shown on birdseye at one time. When this is enabled, birdseye will show the cameras with most recent activity. There is a cooldown to ensure that cameras do not switch too frequently.
-For example, this can be configured to only show the most recently active camera.
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------ | ----------------------------------------------------------------------------------- |
+| **Layout > Max cameras** | Maximum number of cameras shown at once (e.g., `1` for only the most active camera) |
+
+
+
```yaml {3-4}
birdseye:
@@ -102,13 +181,31 @@ birdseye:
max_cameras: 1
```
+
+
+
### Birdseye Scaling
By default birdseye tries to fit 2 cameras in each row and then double in size until a suitable layout is found. The scaling can be configured with a value between 1.0 and 5.0 depending on use case.
+
+
+
+Navigate to .
+
+| Field | Description |
+| --------------------------- | -------------------------------------------------------- |
+| **Layout > Scaling factor** | Camera scaling factor between 1.0 and 5.0 (default: 2.0) |
+
+
+
+
```yaml {3-4}
birdseye:
enabled: True
layout:
scaling_factor: 3.0
```
+
+
+
diff --git a/docs/docs/configuration/camera_specific.md b/docs/docs/configuration/camera_specific.md
index c18b87f2e8..c83942e495 100644
--- a/docs/docs/configuration/camera_specific.md
+++ b/docs/docs/configuration/camera_specific.md
@@ -3,6 +3,8 @@ id: camera_specific
title: Camera Specific Configurations
---
+import NavPath from "@site/src/components/NavPath";
+
:::note
This page makes use of presets of FFmpeg args. For more information on presets, see the [FFmpeg Presets](/configuration/ffmpeg_presets) page.
@@ -148,19 +150,34 @@ WEB Digest Algorithm - MD5
Reolink has many different camera models with inconsistently supported features and behavior. The below table shows a summary of various features and recommendations.
-| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
-| ----------------- | ------------------------- | --------------------------------- | ----------------------------------------------------------------------- |
-| 5MP or lower | All | http-flv | Stream is h264 |
-| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 |
-| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
+| Camera Resolution | Camera Generation | Recommended Stream Type | Additional Notes |
+| ----------------- | ------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------- |
+| 5MP or lower | All | http-flv | Stream is h264 |
+| 6MP or higher | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 (Frigate's default) |
+| 6MP or higher | Older (ex: RLC-8##) | rtsp | |
-Frigate works much better with newer reolink cameras that are setup with the below options:
+Frigate works much better with newer Reolink cameras that are setup with the below options:
If available, recommended settings are:
- `On, fluency first` this sets the camera to CBR (constant bit rate)
- `Interframe Space 1x` this sets the iframe interval to the same as the frame rate
+#### Setup via the Add Camera Wizard
+
+The [Add Camera Wizard](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add a standard Reolink camera. Before starting, make sure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. The wizard uses the camera's HTTP API to determine its resolution and choose the recommended stream type from the table above.
+
+1. Click **Add Camera** in .
+2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.
+3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower, or an RTSP stream for higher resolution cameras.
+4. In the validation step, enable **Use stream compatibility mode** for http-flv streams when the wizard recommends it.
+
+If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras where http-flv is recommended, the wizard will show a warning in the validation step.
+
+The wizard covers standard single-camera setups. For two way talk, cameras connected through a Reolink NVR, or audio transcoding for WebRTC live view, configure the camera manually as shown below.
+
+#### Manual configuration
+
According to [this discussion](https://github.com/blakeblackshear/frigate/issues/3235#issuecomment-1135876973), the http video streams seem to be the most reliable for Reolink.
Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
@@ -175,7 +192,7 @@ Reolink's latest cameras support two way audio via go2rtc and other applications
NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
-Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+Ensure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
:::
@@ -187,7 +204,7 @@ go2rtc:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
your_reolink_camera_sub:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
- # example for connectin to a Reolink camera that supports two way talk
+ # example for connecting to a Reolink camera that supports two way talk
your_reolink_camera_twt:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
- "rtsp://username:password@reolink_ip/Preview_01_sub"
@@ -225,13 +242,14 @@ cameras:
roles:
- detect
```
+
### Unifi Protect Cameras
-:::note
+:::note
-Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.
+Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not possible to enable it in standalone mode.
:::
@@ -246,7 +264,7 @@ go2rtc:
- rtspx://192.168.1.1:7441/abcdefghijk
```
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-rtsp)
In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
@@ -269,7 +287,6 @@ Some community members have found better performance on Wyze cameras by using an
To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
- Preparation outside of Frigate:
-
- Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
- Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
- If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
diff --git a/docs/docs/configuration/cameras.md b/docs/docs/configuration/cameras.md
index eed430b520..640b78841f 100644
--- a/docs/docs/configuration/cameras.md
+++ b/docs/docs/configuration/cameras.md
@@ -3,6 +3,78 @@ id: cameras
title: Camera Configuration
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+## Adding a camera with the Add Camera Wizard
+
+The Add Camera Wizard is the recommended way to add a camera. Click **Add Camera** in . The wizard connects to your camera, tests each stream, and writes the camera's configuration for you, including the [go2rtc](go2rtc.md) restream and the live view stream mapping, so a standard setup needs no hand-written YAML.
+
+### Step 1: Name and connection
+
+Enter a name for the camera along with its host or IP address and credentials, then choose how the wizard should find the camera's streams:
+
+- **Probe camera** queries the camera over ONVIF (the ONVIF port is usually 80 or 8080) and asks it for its stream URLs. Some cameras use a separate ONVIF/service account rather than the device admin user, and some require **Use digest authentication** to be enabled.
+- **Manual selection** builds a stream URL from a template for the camera brand you pick (Dahua/Amcrest/EmpireTech, Hikvision/Uniview/Annke, Ubiquiti, Reolink, Axis, TP-Link, or Foscam). Choose **Other** to enter a custom RTSP URL directly. Non-RTSP stream types must be [configured manually](#setting-up-camera-inputs).
+
+The name you enter is lowercased and spaces become underscores. If the result still isn't a valid config key, the wizard generates a safe name and stores what you typed as `friendly_name`.
+
+### Step 2: Probe or snapshot
+
+In probe mode, the wizard reports what the camera returned (manufacturer, model, firmware, profile count, and whether PTZ, presets, and [autotracking](autotracking.md) are supported) along with the RTSP URLs it discovered. Test each candidate to see its resolution, frame rate, and codecs together with a snapshot, then select the one you want to use.
+
+In manual mode, the wizard tests the templated URL and shows the same metadata and snapshot.
+
+If no RTSP URLs are found, the credentials may be wrong or the camera may not support ONVIF. Go back and use manual selection instead.
+
+### Step 3: Stream configuration
+
+Assign [roles](#setting-up-camera-inputs) to the stream, and use **Add Another Stream** to add the camera's other streams, for example a substream for `detect` alongside the main stream for `record`. At least one stream must have the `detect` role before you can continue.
+
+**Reduce connections to camera** routes that input through the go2rtc restream so Frigate and the live view share a single connection to the camera instead of each opening their own. See [restream](restream.md) for more detail.
+
+### Step 4: Validation and testing
+
+Connect each stream to get a live preview, an estimated bandwidth figure, and a list of validation results. The wizard checks for the most common misconfigurations, including:
+
+- A detect resolution that is too high (increased resource usage) or too low for reliable detection, or one it could not probe at all
+- A stream marked `record` whose audio codec is not AAC, or that has no audio at all
+- A stream marked `audio` that carries no audio stream
+- Using a restreamed input for the `record` role
+- Brand-specific issues, such as an RTSP stream on a Reolink camera that should use http-flv, or a Dahua/Hikvision substream selected for `detect`
+
+**Use stream compatibility mode** passes the stream through go2rtc's ffmpeg module. Enable it if a stream fails to load after several attempts. Note that this also prevents [two way talk](/configuration/live#two-way-talk) from being detected for that stream.
+
+**Save New Camera** writes the configuration and starts the camera right away. No restart is required.
+
+Other features, including [hardware acceleration](hardware_acceleration_video.md), [two way talk](/configuration/live#two-way-talk), and audio transcoding, is configured after the camera has been added. For camera model specific quirks, see the [camera specific](camera_specific.md) docs.
+
+## Deleting a camera
+
+Click **Delete Camera** in , choose the camera, and confirm. Deleting a camera requires the `admin` role and cannot be undone.
+
+:::warning
+
+Deleting a camera permanently removes its recordings, tracked objects, and configuration. If you only want to stop processing a camera, set its state to **Off** or **Disabled** in instead. See [camera state](/configuration/live#camera-state).
+
+:::
+
+Deleting a camera removes:
+
+- The camera's section of your config file, along with its entries in any [role](authentication.md#user-roles) camera list. A custom role left with no cameras is removed as well.
+- Every database record for the camera: tracked objects, review items, recordings, previews, timeline entries, the saved region grid, and [triggers](semantic_search.md#triggers).
+- Every media file for the camera: recordings, snapshots, thumbnails, and preview clips.
+
+[Exports](/usage/exports) are kept by default, so saved footage survives the deletion of the camera it came from. Turn on **Also delete exports for this camera** in the confirmation step to remove those too.
+
+The camera's processes are stopped and the change takes effect immediately, so no restart is required. If the resulting config cannot be parsed, Frigate restores the previous config and reports an error instead of leaving Frigate in a broken state.
+
+Two things are not cleaned up for you:
+
+- **go2rtc streams.** Frigate makes a best effort to stop a running [go2rtc](go2rtc.md) stream named after the camera, but stream entries in your config file remain and are recreated on the next restart. Remove them in or in your config file.
+- **Camera groups.** A deleted camera stays listed in any [camera group](#setting-up-camera-groups) that referenced it. The group skips the missing camera, so this is harmless, but you can edit the group to drop the stale entry.
+
## Setting Up Camera Inputs
Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
@@ -17,6 +89,27 @@ Each role can only be assigned to one input per camera. The options for roles ar
| `record` | Saves segments of the video feed based on configuration settings. [docs](record.md) |
| `audio` | Feed for audio based detection. [docs](audio_detectors.md) |
+
+
+
+Navigate to .
+
+| Field | Description |
+| ----------------- | ------------------------------------------------------------------- |
+| **Camera inputs** | List of input stream definitions (paths and roles) for this camera. |
+
+For each input you can choose its source: select **Restream (go2rtc)** to pick an existing [go2rtc stream](restream.md) from a dropdown (Frigate uses the `rtsp://127.0.0.1:8554/` path and `preset-rtsp-restream` input args for that input automatically), or **Manual input path** to type the stream URL directly.
+
+Navigate to .
+
+| Field | Description |
+| ----------------- | ------------------------------------------------------------------------------------------------------ |
+| **Detect width** | Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution. |
+| **Detect height** | Height (pixels) of frames used for the detect stream; leave empty to use the native stream resolution. |
+
+
+
+
```yaml
mqtt:
host: mqtt.server.com
@@ -36,7 +129,18 @@ cameras:
height: 720 # <- optional, by default Frigate tries to automatically detect resolution
```
-Additional cameras are simply added to the config under the `cameras` entry.
+
+
+
+Additional cameras are simply added under the camera configuration section.
+
+
+
+
+Navigate to and use the [Add Camera Wizard](#adding-a-camera-with-the-add-camera-wizard) to configure each additional camera.
+
+
+
```yaml
mqtt: ...
@@ -46,6 +150,9 @@ cameras:
side: ...
```
+
+
+
:::note
If you only define one stream in your `inputs` and do not assign a `detect` role to it, Frigate will automatically assign it the `detect` role. Frigate will always decode a stream to support motion detection, Birdseye, the API image endpoints, and other features, even if you have disabled object detection with `enabled: False` in your config's `detect` section.
@@ -64,7 +171,19 @@ Not every PTZ supports ONVIF, which is the standard protocol Frigate uses to com
:::
-Add the onvif section to your camera in your configuration file:
+Configure the ONVIF connection for your camera to enable PTZ controls.
+
+
+
+
+1. Navigate to and select your camera.
+ - Set **ONVIF host** to your camera's IP address, e.g.: `10.0.10.10`
+ - Set **ONVIF port** to your camera's ONVIF port, e.g.: `8000`
+ - Set **ONVIF username** to your camera's ONVIF username, e.g.: `admin`
+ - Set **ONVIF password** to your camera's ONVIF password, e.g.: `password`
+
+
+
```yaml {4-8}
cameras:
@@ -77,6 +196,9 @@ cameras:
password: password
```
+
+
+
If the ONVIF connection is successful, PTZ controls will be available in the camera's WebUI.
:::note
@@ -91,6 +213,13 @@ If your ONVIF camera does not require authentication credentials, you may still
:::
+If a camera connects but fails to authenticate, two optional fields can help:
+
+- `tls_insecure`: Skips TLS certificate verification and sends the ONVIF password as plaintext (`PasswordText`) instead of a hashed digest (`PasswordDigest`). Some cameras reject the digest token and only accept plaintext. This weakens connection security, so only enable it on a trusted local network.
+- `ignore_time_mismatch`: ONVIF authentication tokens include a timestamp, and a camera will reject the token if its clock differs too much from Frigate's. Enabling this makes Frigate compensate for the time offset so authentication can still succeed. Running NTP on both the camera and the Frigate host is the recommended fix; only use this in a "safe" environment, as it slightly weakens token validation.
+
+If your camera has multiple ONVIF profiles, you can specify which one to use for PTZ control with the `profile` option, matched by token or name. When not set, Frigate selects the first profile with a valid PTZ configuration. Check the Frigate debug logs (`frigate.ptz.onvif: debug`) to see available profile names and tokens for your camera.
+
An ONVIF-capable camera that supports relative movement within the field of view (FOV) can also be configured to automatically track moving objects and keep them in the center of the frame. For autotracking setup, see the [autotracking](autotracking.md) docs.
## ONVIF PTZ camera recommendations
@@ -120,7 +249,7 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
| Hikvision DS-2DE3A404IWG-E/W | ✅ | ✅ | |
| Reolink | ✅ | ❌ | |
| Speco O8P32X | ✅ | ❌ | |
-| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable. |
+| Sunba 405-D20X | ✅ | ❌ | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatible. |
| Tapo | ✅ | ❌ | Many models supported, ONVIF Service Port: 2020 |
| Uniview IPC672LR-AX4DUPK | ✅ | ❌ | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands |
| Uniview IPC6612SR-X33-VG | ✅ | ✅ | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working. |
@@ -128,13 +257,15 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
## Setting up camera groups
-:::tip
+Camera groups let you organize cameras together with a shared name and icon, making it easier to review and filter them. A default group for all cameras is always available.
-It is recommended to set up camera groups using the UI.
+
+
-:::
+On the Live dashboard, press the **pencil icon** in the main navigation to add a new camera group. Configure the group name, select which cameras to include, choose an icon, and set the display order.
-Cameras can be grouped together and assigned a name and icon, this allows them to be reviewed and filtered together. There will always be the default group for all cameras.
+
+
```yaml
camera_groups:
@@ -146,6 +277,9 @@ camera_groups:
order: 0
```
+
+
+
## Two-Way Audio
See the guide [here](/configuration/live/#two-way-talk)
diff --git a/docs/docs/configuration/config.md b/docs/docs/configuration/config.md
new file mode 100644
index 0000000000..76458e4d36
--- /dev/null
+++ b/docs/docs/configuration/config.md
@@ -0,0 +1,383 @@
+---
+id: config
+title: Frigate Configuration
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Frigate can be configured through the **Settings UI** or by editing the YAML configuration file directly. The Settings UI is the recommended approach. It provides validation and a guided experience for all configuration options.
+
+## Using the Settings UI
+
+The Settings UI groups every configuration option into sections that are listed in the left-hand menu. Each section presents a guided form with validation, so you don't need to remember the structure of the YAML or look up option names by hand.
+
+### Global vs. camera-level configuration
+
+Settings are organized into two scopes:
+
+- **Global configuration**: values under apply to every camera by default. This is where you set the baseline behavior for object detection, recording, snapshots, motion, and so on.
+- **Camera configuration**: values under apply to a single camera. Use the camera selector button at the top of these pages to choose which camera you are editing.
+
+When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only: the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.` takes precedence over the same value set at the top level. See [Global and Camera-Level Configuration](./config_overrides.md) for the full details, including how lists and maps are handled and which settings must be enabled globally first.
+
+To undo an override and go back to inheriting from the parent scope, use the reset button at the bottom of the section:
+
+- On a camera section, the button is labeled **Reset to Global** and restores the camera to the global value.
+- On a global section, the button is labeled **Reset to Default** and restores Frigate's built-in default.
+
+Resetting asks for confirmation and cannot be undone once applied.
+
+### Saving changes and the Save All button
+
+Edits are not applied until you save them. As soon as you change a value, the UI tracks it as a pending change:
+
+- The edited section shows a **Modified** badge, and the changed fields are highlighted.
+- A **You have unsaved changes** notice appears above the section's **Save** and **Undo** buttons. **Save** commits just that section; **Undo** discards its pending edits.
+
+Because pending changes can span multiple sections (and multiple cameras), the header provides a **Save All** button that writes every pending change at once. Next to it, **Review pending changes** opens a summary that lists each pending edit with its scope (Global or a specific camera), the affected field, and the new value, so you can confirm exactly what will be written before committing. **Undo All** discards every pending change across all sections.
+
+### Restart-required indicators
+
+Most settings take effect immediately, but some require Frigate to restart before they apply. Fields that require a restart are marked with a small restart icon and a **Restart required** tooltip next to the field label.
+
+When you save a change that touches one of these fields, Frigate confirms the save and reminds you that a restart is needed (for example, _"Settings saved successfully. Restart Frigate to apply your changes."_). The notification includes a one-click **Restart Frigate** action so you can apply the change right away, or you can continue editing and restart later.
+
+### The colored dots in the camera configuration menu
+
+When you are working under , small colored dots can appear next to a section's name in the menu. They give you an at-a-glance summary of that section's state for the selected camera:
+
+- **Blue dot**: this section **overrides the global configuration**. One or more values in the section have been set specifically for this camera and differ from the global defaults.
+- **Profile-colored dot**: when you are viewing a [camera profile](./profiles.md), a dot in that profile's assigned color indicates the section is **overridden by that profile**. Each profile is given its own distinct color so you can tell at a glance which sections it changes.
+- **Amber dot**: this section has **unsaved changes**. It appears alongside the **Modified** badge whenever you have pending edits in the section that haven't been saved yet.
+
+Hover over any dot to see a tooltip describing what it means. Open a section to see exactly which fields are overridden: the section header indicates how many fields differ from the global (or base) configuration.
+
+## Configuration File Location
+
+For users who prefer to edit the YAML configuration file directly, it is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
+
+- **Home Assistant App:** `/addon_configs//config.yml` (see [directory list](#accessing-app-config-dir))
+- **All other installations:** Map to `/config/config.yml` inside the container
+
+It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
+
+A minimal starting configuration:
+
+```yaml
+mqtt:
+ enabled: False
+
+cameras:
+ dummy_camera: # <--- this will be changed to your actual camera later
+ enabled: False
+ ffmpeg:
+ inputs:
+ - path: rtsp://127.0.0.1:554/rtsp
+ roles:
+ - detect
+```
+
+## Accessing the Home Assistant App configuration directory {#accessing-app-config-dir}
+
+When running Frigate through the HA App, the Frigate `/config` directory is mapped to `/addon_configs/` in the host, where `` is specific to the variant of the Frigate App you are running.
+
+| App Variant | Configuration directory |
+| -------------------------- | ----------------------------------------- |
+| Frigate | `/addon_configs/ccab4aaf_frigate` |
+| Frigate (Full Access) | `/addon_configs/ccab4aaf_frigate-fa` |
+| Frigate Beta | `/addon_configs/ccab4aaf_frigate-beta` |
+| Frigate Beta (Full Access) | `/addon_configs/ccab4aaf_frigate-fa-beta` |
+
+**Whenever you see `/config` in the documentation, it refers to this directory.**
+
+If for example you are running the standard App variant and use the [VS Code App](https://github.com/hassio-addons/addon-vscode) to browse your files, you can click _File_ > _Open folder..._ and navigate to `/addon_configs/ccab4aaf_frigate` to access the Frigate `/config` directory and edit the `config.yaml` file. You can also use the built-in config editor in the Frigate UI.
+
+## VS Code Configuration Schema
+
+VS Code supports JSON schemas for automatically validating configuration files. You can enable this feature by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the beginning of the configuration file. Replace `frigate_host` with the IP address or hostname of your Frigate server. If you're using both VS Code and Frigate as an App, you should use `ccab4aaf-frigate` instead. Make sure to expose the internal unauthenticated port `5000` when accessing the config from VS Code on another machine.
+
+## Environment Variable Substitution
+
+Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./advanced/reference.md). For example, the following values can be replaced at runtime by using environment variables:
+
+```yaml
+mqtt:
+ host: "{FRIGATE_MQTT_HOST}"
+ user: "{FRIGATE_MQTT_USER}"
+ password: "{FRIGATE_MQTT_PASSWORD}"
+```
+
+```yaml
+- path: rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:8554/unicast
+```
+
+```yaml
+onvif:
+ host: "192.168.1.12"
+ port: 8000
+ user: "{FRIGATE_RTSP_USER}"
+ password: "{FRIGATE_RTSP_PASSWORD}"
+```
+
+```yaml
+go2rtc:
+ rtsp:
+ username: "{FRIGATE_GO2RTC_RTSP_USERNAME}"
+ password: "{FRIGATE_GO2RTC_RTSP_PASSWORD}"
+```
+
+```yaml
+genai:
+ my_provider:
+ api_key: "{FRIGATE_GENAI_API_KEY}"
+```
+
+## Common configuration examples
+
+Here are some common starter configuration examples. These can be configured through the Settings UI or via YAML. Refer to the [reference config](./advanced/reference.md) for detailed information about all config values.
+
+### Raspberry Pi Home Assistant App with USB Coral
+
+- Single camera with 720p, 5fps stream for detect
+- MQTT connected to the Home Assistant Mosquitto App
+- Hardware acceleration for decoding video
+- USB Coral detector
+- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
+- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Save snapshots for 30 days
+- Motion mask for the camera timestamp
+
+
+
+
+1. Navigate to and configure the MQTT connection to your Home Assistant Mosquitto broker
+2. Navigate to and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
+3. Navigate to and add a detector with **Type** `EdgeTPU` and **Device** `usb`
+4. Navigate to and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
+5. Navigate to and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
+6. Navigate to and add your camera with the appropriate RTSP stream URL
+7. Navigate to to add a motion mask for the camera timestamp
+
+
+
+
+```yaml
+mqtt:
+ host: core-mosquitto
+ user: mqtt-user
+ password: xxxxxxxxxx
+
+ffmpeg:
+ hwaccel_args: preset-rpi-64-h264
+
+detectors:
+ coral:
+ type: edgetpu
+ device: usb
+
+record:
+ enabled: True
+ motion:
+ days: 7
+ alerts:
+ retain:
+ days: 30
+ mode: motion
+ detections:
+ retain:
+ days: 30
+ mode: motion
+
+snapshots:
+ enabled: True
+ retain:
+ default: 30
+
+cameras:
+ name_of_your_camera:
+ detect:
+ width: 1280
+ height: 720
+ fps: 5
+ ffmpeg:
+ inputs:
+ - path: rtsp://10.0.10.10:554/rtsp
+ roles:
+ - detect
+ motion:
+ mask:
+ timestamp:
+ friendly_name: "Camera timestamp"
+ enabled: true
+ coordinates: "0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400"
+```
+
+
+
+
+### Standalone Intel Mini PC with USB Coral
+
+- Single camera with 720p, 5fps stream for detect
+- MQTT disabled (not integrated with Home Assistant)
+- VAAPI hardware acceleration for decoding video
+- USB Coral detector
+- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
+- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Save snapshots for 30 days
+- Motion mask for the camera timestamp
+
+
+
+
+1. Navigate to and set **Enable MQTT** to off
+2. Navigate to and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
+3. Navigate to and add a detector with **Type** `EdgeTPU` and **Device** `usb`
+4. Navigate to and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
+5. Navigate to and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
+6. Navigate to and add your camera with the appropriate RTSP stream URL
+7. Navigate to to add a motion mask for the camera timestamp
+
+
+
+
+```yaml
+mqtt:
+ enabled: False
+
+ffmpeg:
+ hwaccel_args: preset-vaapi
+
+detectors:
+ coral:
+ type: edgetpu
+ device: usb
+
+record:
+ enabled: True
+ motion:
+ days: 7
+ alerts:
+ retain:
+ days: 30
+ mode: motion
+ detections:
+ retain:
+ days: 30
+ mode: motion
+
+snapshots:
+ enabled: True
+ retain:
+ default: 30
+
+cameras:
+ name_of_your_camera:
+ detect:
+ width: 1280
+ height: 720
+ fps: 5
+ ffmpeg:
+ inputs:
+ - path: rtsp://10.0.10.10:554/rtsp
+ roles:
+ - detect
+ motion:
+ mask:
+ timestamp:
+ friendly_name: "Camera timestamp"
+ enabled: true
+ coordinates: "0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400"
+```
+
+
+
+
+### Home Assistant integrated Intel Mini PC with OpenVINO
+
+- Single camera with 720p, 5fps stream for detect
+- MQTT connected to same MQTT server as Home Assistant
+- VAAPI hardware acceleration for decoding video
+- OpenVINO detector
+- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
+- Continue to keep all video if it qualified as an alert or detection for 30 days
+- Save snapshots for 30 days
+- Motion mask for the camera timestamp
+
+
+
+
+1. Navigate to and configure the connection to your MQTT broker
+2. Navigate to and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
+3. Navigate to and add a detector with **Type** `openvino` and **Device** `AUTO`
+4. On the same page, in the **Custom Model** tab, configure the OpenVINO model path and settings
+5. Navigate to and set **Enable recording** to on, **Motion retention > Retention days** to `7`, **Alert retention > Event retention > Retention days** to `30`, **Alert retention > Event retention > Retention mode** to `motion`, **Detection retention > Event retention > Retention days** to `30`, **Detection retention > Event retention > Retention mode** to `motion`
+6. Navigate to and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
+7. Navigate to and add your camera with the appropriate RTSP stream URL
+8. Navigate to to add a motion mask for the camera timestamp
+
+
+
+
+```yaml
+mqtt:
+ host: 192.168.X.X # <---- same mqtt broker that home assistant uses
+ user: mqtt-user
+ password: xxxxxxxxxx
+
+ffmpeg:
+ hwaccel_args: preset-vaapi
+
+detectors:
+ ov:
+ type: openvino
+ device: AUTO
+
+model:
+ width: 300
+ height: 300
+ input_tensor: nhwc
+ input_pixel_format: bgr
+ path: /openvino-model/ssdlite_mobilenet_v2.xml
+ labelmap_path: /openvino-model/coco_91cl_bkgr.txt
+
+record:
+ enabled: True
+ motion:
+ days: 7
+ alerts:
+ retain:
+ days: 30
+ mode: motion
+ detections:
+ retain:
+ days: 30
+ mode: motion
+
+snapshots:
+ enabled: True
+ retain:
+ default: 30
+
+cameras:
+ name_of_your_camera:
+ detect:
+ width: 1280
+ height: 720
+ fps: 5
+ ffmpeg:
+ inputs:
+ - path: rtsp://10.0.10.10:554/rtsp
+ roles:
+ - detect
+ motion:
+ mask:
+ timestamp:
+ friendly_name: "Camera timestamp"
+ enabled: true
+ coordinates: "0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400"
+```
+
+
+
diff --git a/docs/docs/configuration/config_overrides.md b/docs/docs/configuration/config_overrides.md
new file mode 100644
index 0000000000..61aa545f44
--- /dev/null
+++ b/docs/docs/configuration/config_overrides.md
@@ -0,0 +1,244 @@
+---
+id: config_overrides
+title: Global and Camera-Level Configuration
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Most of Frigate's configuration can be set once for all cameras and then adjusted for individual cameras. The global value acts as the default for every camera, and any camera can override it.
+
+This page explains how that inheritance works. For a tour of the Settings UI itself, see [Frigate Configuration](./config.md).
+
+## The basics
+
+Set a value globally and every camera uses it. Set the same value on a camera and that camera uses its own value instead.
+
+
+
+
+1. Navigate to and set **Detect FPS** to `5`. Every camera now detects at 5 fps.
+2. Navigate to , select the `driveway` camera, and set **Detect FPS** to `10`.
+
+The `driveway` camera now detects at 10 fps. Every other camera still uses the global value of 5.
+
+
+
+
+```yaml
+detect:
+ fps: 5 # every camera detects at 5 fps
+
+cameras:
+ front_door:
+ ffmpeg: ...
+ driveway:
+ ffmpeg: ...
+ detect:
+ fps: 10 # except this one
+```
+
+`front_door` inherits `fps: 5`, and `driveway` uses `10`.
+
+
+
+
+## Overrides apply per value, not per section
+
+Overriding one value in a section does not detach the rest of that section. Everything you don't set on the camera still comes from the global configuration.
+
+
+
+
+If you set a camera's **Motion threshold** but leave **Contour area** alone, only the threshold is overridden. The contour area continues to follow , and changing it there still affects that camera.
+
+Open a section to see which values are overridden: the section header indicates how many fields differ from the global configuration.
+
+
+
+
+```yaml
+motion:
+ threshold: 30
+ contour_area: 10
+
+cameras:
+ driveway:
+ motion:
+ threshold: 40
+```
+
+The `driveway` camera ends up with `threshold: 40` and `contour_area: 10`. Only the value you wrote was overridden.
+
+
+
+
+## Returning a camera to the global value
+
+
+
+
+A camera section that has its own values shows an **Overridden** badge. To remove the override and go back to inheriting, use the **Reset to Global** button at the bottom of the section.
+
+
+
+
+Frigate treats a camera value as an override because it is written in the config file, not because it differs from the global value. Repeating the global value under a camera still creates an override:
+
+```yaml
+snapshots:
+ enabled: true
+
+cameras:
+ driveway:
+ snapshots:
+ enabled: true # this is an override, even though it matches
+```
+
+If you later change the global `snapshots.enabled` to `false`, `driveway` keeps saving snapshots, because it has its own value. To make a camera follow the global value again, delete the key from the camera rather than setting it to match.
+
+
+
+
+## Lists replace, maps merge
+
+This is the distinction that surprises people most.
+
+**Lists are replaced entirely.** A camera's list does not add to the global list, it takes its place.
+
+
+
+
+The camera page shows the objects the camera is currently tracking, starting from the global list. Changing that selection under replaces the list for that camera, so make sure every object you want tracked is selected, not just the ones you are adding.
+
+
+
+
+```yaml
+objects:
+ track:
+ - person
+ - car
+
+cameras:
+ backyard:
+ objects:
+ track:
+ - dog # backyard tracks ONLY dog, not person or car
+```
+
+To track `dog` in addition to the global objects, list all of them on the camera.
+
+
+
+
+An empty list is a valid override, and is the normal way to opt a camera out of something:
+
+```yaml
+review:
+ alerts:
+ labels:
+ - person
+
+cameras:
+ street:
+ review:
+ alerts:
+ labels: [] # this camera never creates alerts
+```
+
+**Maps are merged key by key.** A camera can add an entry without redeclaring the others.
+
+
+
+
+Adding a filter for one object under does not remove the filters inherited from . The camera keeps both.
+
+
+
+
+```yaml
+objects:
+ filters:
+ person:
+ min_area: 5000
+
+cameras:
+ driveway:
+ objects:
+ filters:
+ car:
+ min_area: 10000
+```
+
+The `driveway` camera ends up with both the `car` filter it defined and the `person` filter from the global configuration.
+
+
+
+
+## Which settings can be overridden
+
+Most, but not all. The [full reference config](./advanced/reference.md) is the authoritative source: sections that support camera-level overrides are marked with the comment `# NOTE: Can be overridden at the camera level`. In the UI, a setting can be overridden if it appears under both and .
+
+A few things worth knowing beyond that:
+
+- Some sections are **global only** and have no camera-level equivalent, including `go2rtc`, `genai` providers, `classification`, `telemetry`, `camera_groups`, and `ui`.
+- Some sections exist **only at the camera level**, such as `zones` and `onvif`.
+- Some sections are **partially overridable**, meaning a camera accepts only a few of the keys available globally. `face_recognition`, `lpr`, and `audio_transcription` work this way, and the reference config notes which keys apply.
+
+## Enrichments that must be enabled globally first
+
+License plate recognition and face recognition are special: the global setting is not just a default, it is a switch that must be on before any camera can use the feature. Enabling one on a camera while it is disabled globally is a configuration error, and Frigate will refuse to start:
+
+```
+Camera driveway has lpr enabled but lpr is disabled at the global level of the config. You must enable lpr at the global level.
+```
+
+Enable the feature globally, then turn it off on the cameras that don't need it.
+
+
+
+
+1. Navigate to and enable **LPR**.
+2. Navigate to , select each camera that should not run LPR, and disable the **Enable LPR** toggle.
+
+
+
+
+```yaml
+lpr:
+ enabled: true
+
+cameras:
+ driveway:
+ ffmpeg: ... # inherits lpr, enabled
+ backyard:
+ ffmpeg: ...
+ lpr:
+ enabled: false # opted out
+```
+
+
+
+
+:::note
+
+This applies only to `lpr` and `face_recognition`, because the global setting controls whether the supporting background process starts at all. Other features do not work this way. Audio transcription, for example, can be enabled on a single camera without being enabled globally.
+
+:::
+
+## Profiles
+
+[Profiles](./profiles.md) add a further layer on top of everything described above. A profile is a named set of camera overrides that you can switch on and off while Frigate is running, for example to change detection and recording behavior when you leave the house.
+
+Profiles are applied on top of a camera's already-resolved configuration, so a profile value wins over both the camera and the global value while that profile is active. Profiles cover a subset of the camera sections and do not modify your config file.
+
+## Summary
+
+- A camera inherits every value you don't set on it.
+- Overriding one value does not detach the rest of the section.
+- Writing a value on a camera overrides it, even if it matches the global value. Remove it to inherit again.
+- Lists replace the global list. Maps merge into it.
+- An empty list is an override, not an omission.
+- `lpr` and `face_recognition` must be enabled globally before a camera can use them.
diff --git a/docs/docs/configuration/custom_classification/object_classification.md b/docs/docs/configuration/custom_classification/object_classification.md
index caf05d8f3c..4f65934e13 100644
--- a/docs/docs/configuration/custom_classification/object_classification.md
+++ b/docs/docs/configuration/custom_classification/object_classification.md
@@ -3,13 +3,23 @@ id: object_classification
title: Object Classification
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
Object classification allows you to train a custom MobileNetV2 classification model to run on tracked objects (persons, cars, animals, etc.) to identify a finer category or attribute for that object. Classification results are visible in the Tracked Object Details pane in Explore, through the `frigate/tracked_object_details` MQTT topic, in Home Assistant sensors via the official Frigate integration, or through the event endpoints in the HTTP API.
+:::info
+
+Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
## Minimum System Requirements
Object classification models are lightweight and run very fast on CPU.
-Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
+Training the model does briefly use a high amount of system resources for about 1-3 minutes per training run. On lower-power devices, training may take longer.
A CPU with AVX + AVX2 instructions is required for training and inference.
@@ -27,7 +37,7 @@ For object classification:
### Classification Type
- **Sub label**:
- - Applied to the object’s `sub_label` field.
+ - Applied to the object's `sub_label` field.
- Ideal for a single, more specific identity or type.
- Example: `cat` → `Leo`, `Charlie`, `None`.
@@ -55,7 +65,7 @@ This two-step verification prevents false positives by requiring consistent pred
### Sub label
-- **Known pet vs unknown**: For `dog` objects, set sub label to your pet’s name (e.g., `buddy`) or `none` for others.
+- **Known pet vs unknown**: For `dog` objects, set sub label to your pet's name (e.g., `buddy`) or `none` for others.
- **Mail truck vs normal car**: For `car`, classify as `mail_truck` vs `car` to filter important arrivals.
- **Delivery vs non-delivery person**: For `person`, classify `delivery` vs `visitor` based on uniform/props.
@@ -68,7 +78,27 @@ This two-step verification prevents false positives by requiring consistent pred
## Configuration
-Object classification is configured as a custom classification model. Each model has its own name and settings. You must list which object labels should be classified.
+Object classification is configured as a custom classification model. Each model has its own name and settings. Specify which object labels should be classified.
+
+
+
+
+Navigate to the **Classification** page from the main navigation sidebar, then click **Add Classification**.
+
+In the **Create New Classification** dialog:
+
+| Field | Description |
+| ----------------------- | ------------------------------------------------------------- |
+| **Name** | A name for your classification model (e.g., `dog`) |
+| **Type** | Select **Object** for object classification |
+| **Object Label** | The object label to classify (e.g., `dog`, `person`, `car`) |
+| **Classification Type** | Whether to assign results as a **Sub Label** or **Attribute** |
+| **Classes** | The class names the model will learn to distinguish between |
+
+The `threshold` (default: `0.8`) can be adjusted in the YAML configuration.
+
+
+
```yaml
classification:
@@ -82,6 +112,9 @@ classification:
An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For object classification models, the default is 200.
+
+
+
## Training the model
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
@@ -102,18 +135,47 @@ If examples for some of your classes do not appear in the grid, you can continue
### Improving the Model
+:::tip Diversity matters far more than volume
+
+Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**
+
+For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
+
+:::
+
+- **Start small and iterate**: Begin with a small, representative set of images per class. Models often begin working well with surprisingly few examples and improve naturally over time.
+- **Favor hard examples**: When images appear in the Recent Classifications tab, prioritize images scoring below 90-100% or those captured under new lighting, weather, or distance conditions.
+- **Avoid bulk training similar images**: Training large batches of images that already score 100% (or close) adds little new information and increases the risk of overfitting.
+- **The wizard is just the starting point**: You don't need to find and label every class upfront. Missing classes will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
-- **Data collection**: Use the model’s Recent Classification tab to gather balanced examples across times of day, weather, and distances.
-- **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
-- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
+- **Preprocessing**: Ensure examples reflect object crops similar to Frigate's boxes; keep the subject centered.
+- **Crop size**: Aim for crops of at least 100×100 pixels (a 10,000 pixel area). Crops smaller than ~80×80 get stretched 3-7× by the model's 224×224 input resize and tend to collapse into a generic "blob" region of feature space where identity becomes unreliable. If most of your detections are small because the camera is far from the subject, consider repositioning the camera for closer crops.
+- **Class balance**: Aim to keep your largest class within ~3× the count of your smallest. Beyond that, the model becomes biased toward the dominant class and tends to default borderline predictions to it (the "everything looks like Buddy" failure mode).
- **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
+:::tip `none` works differently from named classes
+
+Named classes work best with visually uniform examples. Every Buddy photo should look like Buddy. The `none` class needs the opposite: visual diversity across sizes, framings, and qualities, because at inference it has to absorb everything that isn't one of your named classes. Don't apply the same "only keep large, well-framed images" rule to `none` that you would to a named class. Mix in small crops, partial views, and false positives deliberately - otherwise the model has no signal for "small/ambiguous thing = not one of my known classes" and will force those crops into a named class by default.
+
+:::
+
## Debugging Classification Models
To troubleshoot issues with object classification models, enable debug logging to see detailed information about classification attempts, scores, and consensus calculations.
Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
+
+
+
+Navigate to .
+
+- Set **Logging level** to `debug`
+- Set **Per-process log level > `frigate.data_processing.real_time.custom_classification`** to `debug` for verbose classification logging
+
+
+
+
```yaml
logger:
default: info
@@ -122,6 +184,9 @@ logger:
frigate.data_processing.real_time.custom_classification: debug
```
+
+
+
The debug logs will show:
- Classification probabilities for each attempt
diff --git a/docs/docs/configuration/custom_classification/state_classification.md b/docs/docs/configuration/custom_classification/state_classification.md
index c41d05439f..b24ce17922 100644
--- a/docs/docs/configuration/custom_classification/state_classification.md
+++ b/docs/docs/configuration/custom_classification/state_classification.md
@@ -3,13 +3,23 @@ id: state_classification
title: State Classification
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region. Classification results are available through the `frigate//classification/` MQTT topic and in Home Assistant sensors via the official Frigate integration.
+:::info
+
+Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
## Minimum System Requirements
State classification models are lightweight and run very fast on CPU.
-Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
+Training the model does briefly use a high amount of system resources for about 1-3 minutes per training run. On lower-power devices, training may take longer.
A CPU with AVX + AVX2 instructions is required for training and inference.
@@ -33,7 +43,25 @@ For state classification:
## Configuration
-State classification is configured as a custom classification model. Each model has its own name and settings. You must provide at least one camera crop under `state_config.cameras`.
+State classification is configured as a custom classification model. Each model has its own name and settings. Provide at least one camera crop under `state_config.cameras`.
+
+
+
+
+Navigate to the **Classification** page from the main navigation sidebar, select the **States** tab, then click **Add Classification**.
+
+In the **Create New Classification** dialog:
+
+| Field | Description |
+| ----------- | ------------------------------------------------------------------------------------ |
+| **Name** | A name for your state classification model (e.g., `front_door`) |
+| **Type** | Select **State** for state classification |
+| **Classes** | The state names the model will learn to distinguish between (e.g., `open`, `closed`) |
+
+After creating the model, the wizard will guide you through selecting the camera crop area and assigning training examples. The `threshold` (default: `0.8`), `motion`, and `interval` settings can be adjusted in the YAML configuration.
+
+
+
```yaml
classification:
@@ -45,11 +73,18 @@ classification:
interval: 10 # also run every N seconds (optional)
cameras:
front:
- crop: [0, 180, 220, 400]
+ # [x1, y1, x2, y2] as decimals between 0 and 1, relative to the
+ # camera's detect resolution
+ crop: [0.0, 0.25, 0.3, 0.85]
```
+Crop coordinates are normalized: each value is a fraction of the camera's `detect` width or height, not a pixel value. Drawing the crop in the UI wizard writes these values for you.
+
An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
+
+
+
## Training the model
Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
@@ -70,10 +105,21 @@ Once some images are assigned, training will begin automatically.
### Improving the Model
+:::tip Diversity matters far more than volume
+
+Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
+
+For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
+
+:::
+
+- **Start small and iterate**: Begin with a small, representative set of images per class. Models often begin working well with surprisingly few examples and improve naturally over time.
- **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
- **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
- **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
-- **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
+- **Favor hard examples**: When images appear in the Recent Classifications tab, prioritize images scoring below 90-100% or those captured under new conditions (e.g., first snow of the year, seasonal changes, objects temporarily in view, insects at night). These represent scenarios different from the default state and help prevent overfitting.
+- **Avoid bulk training similar images**: Training large batches of images that already score 100% (or close) adds little new information and increases the risk of overfitting.
+- **The wizard is just the starting point**: You don't need to find and label every state upfront. Missing states will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
## Debugging Classification Models
@@ -81,6 +127,17 @@ To troubleshoot issues with state classification models, enable debug logging to
Enable debug logs for classification models by adding `frigate.data_processing.real_time.custom_classification: debug` to your `logger` configuration. These logs are verbose, so only keep this enabled when necessary. Restart Frigate after this change.
+
+
+
+Navigate to .
+
+- Set **Logging level** to `debug`
+- Set **Per-process log level > `frigate.data_processing.real_time.custom_classification`** to `debug` for verbose classification logging
+
+
+
+
```yaml
logger:
default: info
@@ -89,6 +146,9 @@ logger:
frigate.data_processing.real_time.custom_classification: debug
```
+
+
+
The debug logs will show:
- Classification probabilities for each attempt
diff --git a/docs/docs/configuration/face_recognition.md b/docs/docs/configuration/face_recognition.md
index 74fd810710..333c3ff1e7 100644
--- a/docs/docs/configuration/face_recognition.md
+++ b/docs/docs/configuration/face_recognition.md
@@ -3,8 +3,19 @@ id: face_recognition
title: Face Recognition
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
+
Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
+:::info
+
+Face recognition requires a one-time internet connection to download detection and embedding models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
## Model Requirements
### Face Detection
@@ -40,56 +51,115 @@ The `large` model is optimized for accuracy, an integrated or discrete GPU / NPU
## Configuration
-Face recognition is disabled by default, face recognition must be enabled in the UI or in your config file before it can be used. Face recognition is a global configuration setting.
+Face recognition is disabled by default and must be enabled before it can be used. Face recognition is a global configuration setting.
+
+
+
+
+Navigate to .
+
+- Set **Enable face recognition** to on
+
+
+
```yaml
face_recognition:
enabled: true
```
+
+
+
Like the other real-time processors in Frigate, face recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
## Advanced Configuration
-Fine-tune face recognition with these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
+Fine-tune face recognition with these optional parameters. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
### Detection
-- `detection_threshold`: Face detection confidence score required before recognition runs:
+
+
+
+Navigate to .
+
+- **Detection threshold**: Face detection confidence score required before recognition runs. This field only applies to the standalone face detection model; `min_score` should be used to filter for models that have face detection built in.
- Default: `0.7`
- - Note: This is field only applies to the standalone face detection model, `min_score` should be used to filter for models that have face detection built in.
-- `min_area`: Defines the minimum size (in pixels) a face must be before recognition runs.
- - Default: `500` pixels.
- - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
+- **Minimum face area**: Minimum size (in pixels) a face must be before recognition runs. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
+ - Default: `750` pixels
+
+
+
+
+```yaml
+face_recognition:
+ enabled: true
+ detection_threshold: 0.7
+ min_area: 750
+```
+
+
+
### Recognition
-- `model_size`: Which model size to use, options are `small` or `large`
-- `unknown_score`: Min score to mark a person as a potential match, matches at or below this will be marked as unknown.
- - Default: `0.8`.
-- `recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
- - Default: `0.9`.
-- `min_faces`: Min face recognitions for the sub label to be applied to the person object.
+
+
+
+Navigate to .
+
+- **Model size**: Which model size to use, options are `small` or `large`.
+- **Unknown score threshold**: Min score to mark a person as a potential match; matches at or below this will be marked as unknown.
+ - Default: `0.8`
+- **Recognition threshold**: Recognition confidence score required to add the face to the object as a sub label.
+ - Default: `0.9`
+- **Minimum faces**: Min face recognitions for the sub label to be applied to the person object.
- Default: `1`
-- `save_attempts`: Number of images of recognized faces to save for training.
- - Default: `200`.
-- `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
- - Default: `True`.
-- `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
- - Default: `None`.
- - Note: This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)
+- **Save attempts**: Number of images of recognized faces to save for training.
+ - Default: `200`
+- **Blur confidence filter**: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
+ - Default: `True`
+- **Device**: Target a specific device to run the face recognition model on (multi-GPU installation). This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/).
+ - Default: `None`
+
+
+
+
+```yaml
+face_recognition:
+ enabled: true
+ model_size: small
+ unknown_score: 0.8
+ recognition_threshold: 0.9
+ min_faces: 1
+ save_attempts: 200
+ blur_confidence_filter: true
+ device: None
+```
+
+
+
## Usage
Follow these steps to begin:
-1. **Enable face recognition** in your configuration file and restart Frigate.
+1. **Enable face recognition** in your configuration and restart Frigate.
2. **Upload one face** using the **Add Face** button's wizard in the Face Library section of the Frigate UI. Read below for the best practices on expanding your training set.
3. When Frigate detects and attempts to recognize a face, it will appear in the **Train** tab of the Face Library, along with its associated recognition confidence.
4. From the **Train** tab, you can **assign the face** to a new or existing person to improve recognition accuracy for the future.
## Creating a Robust Training Set
+:::tip
+
+**The short version:** Start with a few clear, front-facing photos of each person. As faces are detected in the Recent Recognitions tab, train clear images that scored lower, adding variety (different angles, lighting, and expressions) slowly. Diversity matters far more than volume, and low-quality images hurt recognition more than they help.
+
+For a step-by-step narrative of these best practices (and the same principles applied to state and object classification), see the [Frigate Tips: Best Practices for Training](https://github.com/blakeblackshear/frigate/discussions/21374) discussion.
+
+:::
+
The number of images needed for a sufficient training set for face recognition varies depending on several factors:
- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
@@ -110,7 +180,7 @@ When choosing images to include in the face training set it is recommended to al
- If it is difficult to make out details in a persons face it will not be helpful in training.
- Avoid images with extreme under/over-exposure.
- Avoid blurry / pixelated images.
-- Avoid training on infrared (gray-scale). The models are trained on color images and will be able to extract features from gray-scale images.
+- Avoid training on infrared (gray-scale). The models are trained on color images and will not be able to extract features from gray-scale images.
- Using images of people wearing hats / sunglasses may confuse the model.
- Do not upload too many similar images at the same time, it is recommended to train no more than 4-6 similar images for each person to avoid over-fitting.
@@ -120,9 +190,27 @@ When choosing images to include in the face training set it is recommended to al
The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
-Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
+Each face image is labeled with a name (or `Unknown`) along with the confidence score of that recognition attempt. Images are grouped by the person they were matched against, not by who they actually are, so a group labeled with a person's name can contain a crop that is really someone else but happened to score as a partial match. The name and score shown on each individual crop describe that single attempt.
-Refer to the guidelines below for best practices on selecting images for training.
+While each image can be used to train the system for a specific person, not all images are suitable for training. Refer to the guidelines below for best practices on selecting images for training.
+
+### How Frigate Decides Who a Person Is
+
+Recognition does not happen one frame at a time. While a `person` is in view, Frigate runs face recognition on many frames, not just a single frame. The final `sub_label` is decided from all of those attempts together, weighted by the area of each face (larger, closer faces count more), not from any single frame.
+
+This has a few practical consequences:
+
+- A handful of wrong guesses on blurry or distant frames usually do not change the result. If Frigate sees a person as "Tom, Tom, Sam, Tom, Tom," it will still conclude the person was Tom.
+- The goal is not for every individual face crop to be correct. The goal is for each person to be recognized correctly overall, across all the faces captured while they were present.
+- A single very high confidence match will not by itself assign a sub label. Recognition must be consistent. See [I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?](#i-see-scores-above-the-threshold-in-the-recent-recognitions-tab-but-a-sub-label-wasnt-assigned) below.
+
+### Which Faces Are Worth Training?
+
+Whether a face is worth training has little to do with what it was recognized as. A crop is a good training candidate when all of these are true:
+
+- It did not already score high and correctly. Faces that are already recognized confidently add little and increase the risk of over-fitting.
+- It is clear enough to be useful: not blurry, not heavily off-axis, not infrared (gray-scale). If it is hard for you to make out the face, it will not help the model.
+- It adds something new: a different angle, lighting, expression, or distance than what you already have.
### Step 1 - Building a Strong Foundation
@@ -138,39 +226,81 @@ Once front-facing images are performing well, start choosing slightly off-angle
## FAQ
-### How do I debug Face Recognition issues?
+### Getting Recognition Working
+
+
Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
-1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
+1. Enable debug logs to see exactly what Frigate is doing.
+ - Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
+
+ ```yaml
+ logger:
+ default: info
+ logs:
+ # highlight-next-line
+ frigate.data_processing.real_time.face: debug
+ ```
+
+ - These logs report where the pipeline stopped for each `person` object, such as no face being found within the person's bounding box, the detected face being smaller than `min_area`, or a face being recognized but scoring too low.
+ - If you see no face-related messages at all, also add `frigate.embeddings.maintainer: debug` to confirm that the face processor was created at startup and that `person` updates are reaching it.
+
+2. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
If you are using a Frigate+ or `face` detecting model:
- - Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
+ - Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
- You may need to adjust the `min_score` for the `face` object if faces are not being detected.
If you are **not** using a Frigate+ or `face` detecting model:
- Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
- You may need to lower your `detection_threshold` if faces are not being detected.
-2. Any detected faces will then be _recognized_.
+3. Any detected faces will then be _recognized_.
- Make sure you have trained at least one face per the recommendations above.
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
-### Detection does not work well with blurry images?
+
-Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
+
+
+Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
+
+1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
+2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
+3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
+
+
+
+### Improving Accuracy and Training
+
+
+
+Accuracy is definitely going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
-### Why can't I bulk upload photos?
+
+
+
+
+The embedding models are trained on color images, so gray-scale and infrared (IR) faces sit in a different feature distribution and are more easily confused with other people. Prefer color images, and avoid mixing gray-scale samples in early while you are building a foundation. If someone only ever appears at night, gray-scale training is acceptable, but keep those samples limited and as clear as possible, and add them only once color recognition is stable for your other people.
+
+
+
+
It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.
-### Why can't I bulk reprocess faces?
+
+
+
Face embedding models work by breaking apart faces into different features. This means that when reprocessing an image, only images from a similar angle will have its score affected.
-### Why do unknown people score similarly to known people?
+
+
+
This can happen for a few different reasons, but this is usually an indicator that the training set needs to be improved. This is often related to over-fitting:
@@ -180,33 +310,54 @@ This can happen for a few different reasons, but this is usually an indicator th
Review your face collections and remove most of the unclear or low-quality images. Then, use the **Reprocess** button on each face in the **Train** tab to evaluate how the changes affect recognition scores.
-Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower - ideally with different lighting, angles, and conditions—to help the model generalize more effectively.
+Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower (ideally with different lighting, angles, and conditions) to help the model generalize more effectively.
-### Frigate misidentified a face. Can I tell it that a face is "not" a specific person?
+
+
+
+
+Only if it is a good image. Reassigning a face does add it to that person's training set, but two things are true at once:
+
+- Reassigning a single misclassified frame has a small effect. The image is weighted against every other sample for that person, so correcting 1 frame out of 20 will not move recognition much. Occasional wrong guesses on poor frames are normal and do not need to be fixed.
+- Reassigning a poor image (blurry, off-angle, low-resolution, gray-scale) can hurt more than the misidentification did, because low-quality samples degrade recognition for that whole person.
+
+So the decision is about image quality, not about the wrong label. If the crop is clear, well-lit, and reasonably front-facing, and it scored low or was wrong, assigning it to the correct person is useful. If you can barely make out the face yourself, ignore it; do not train it just to correct the label.
+
+If a person is repeatedly misidentified, do not keep reassigning the same frame. Instead, remove low-quality or misleading images and add a few high-quality samples to the correct person. See [Why do unknown people score similarly to known people?](#why-do-unknown-people-score-similarly-to-known-people) above.
+
+
+
+
No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
For more guidance, refer to the section above on improving recognition accuracy.
-### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
+This also applies to a stranger who is repeatedly matched to a known person (for example, a delivery driver recognized as you). Do not create a profile for them and do not reassign their faces to yourself, as this pollutes your training set and makes recognition worse. Leave the detection as unknown and improve the known person's training set instead. Face recognition learns who someone is, not who they are not.
-The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
+
-### Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?
+
+
+Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
+
+
+
+### Compatibility and Maintenance
+
+
No, using another face recognition service will interfere with Frigate's built in face recognition. When using double-take the sub_label feature must be disabled if the built in face recognition is also desired.
-### Does face recognition run on the recording stream?
+
-Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
-
-1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
-2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
-3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
-
-### I get an unknown error when taking a photo directly with my iPhone
+
By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
-### How can I delete the face database and start over?
+
+
+
Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.
+
+
diff --git a/docs/docs/configuration/ffmpeg_presets.md b/docs/docs/configuration/ffmpeg_presets.md
index 8bba62e363..5c1d0fc3b1 100644
--- a/docs/docs/configuration/ffmpeg_presets.md
+++ b/docs/docs/configuration/ffmpeg_presets.md
@@ -3,47 +3,75 @@ id: ffmpeg_presets
title: FFmpeg presets
---
-Some presets of FFmpeg args are provided by default to make the configuration easier. All presets can be seen in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
-### Hwaccel Presets
+Frigate ships with a set of FFmpeg presets to keep your configuration short and readable. Each preset expands to a longer list of FFmpeg arguments at runtime. You can see exactly what every preset expands to in [this file](https://github.com/blakeblackshear/frigate/blob/master/frigate/ffmpeg_presets.py).
-It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.
+In the config file you reference a preset by its name (for example, `preset-vaapi`). In the UI, the same preset is shown with a friendly label (for example, **VAAPI (Intel/AMD GPU)**). Both refer to the same thing: the tables below list the config name alongside the label you'll see in the UI.
-See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more info on how to setup hwaccel for your GPU / iGPU.
+### Hwaccel (Hardware Acceleration) Presets {#hwaccel-presets}
-| Preset | Usage | Other Notes |
-| --------------------- | ------------------------------ | ----------------------------------------------------- |
-| preset-rpi-64-h264 | 64 bit Rpi with h264 stream | |
-| preset-rpi-64-h265 | 64 bit Rpi with h265 stream | |
-| preset-vaapi | Intel & AMD VAAPI | Check hwaccel docs to ensure correct driver is chosen |
-| preset-intel-qsv-h264 | Intel QSV with h264 stream | If issues occur recommend using vaapi preset instead |
-| preset-intel-qsv-h265 | Intel QSV with h265 stream | If issues occur recommend using vaapi preset instead |
-| preset-nvidia | Nvidia GPU | |
-| preset-jetson-h264 | Nvidia Jetson with h264 stream | |
-| preset-jetson-h265 | Nvidia Jetson with h265 stream | |
-| preset-rkmpp | Rockchip MPP | Use image with \*-rk suffix and privileged mode |
+Hardware acceleration arguments tell FFmpeg to decode your camera's video stream on a GPU or integrated graphics chip instead of the CPU, which dramatically lowers CPU usage. Using a preset is highly recommended. Beyond replacing a long list of arguments, each preset also tells Frigate what hardware is available so it can offload additional work to the GPU, for example, encoding the Birdseye restream or scaling a stream whose resolution differs from the camera's native size.
+
+See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md) for details on setting up hardware acceleration for your GPU / iGPU, then select the preset that matches your hardware.
+
+| Preset (YAML config) | UI Label | Usage | Notes |
+| --------------------- | ----------------------- | --------------------------------- | --------------------------------------------------------------- |
+| preset-rpi-64-h264 | Raspberry Pi (H.264) | 64-bit Raspberry Pi, H.264 stream | |
+| preset-rpi-64-h265 | Raspberry Pi (H.265) | 64-bit Raspberry Pi, H.265 stream | |
+| preset-vaapi | VAAPI (Intel/AMD GPU) | Intel or AMD GPU via VAAPI | Check the hwaccel docs to ensure the correct driver is selected |
+| preset-intel-qsv-h264 | Intel QuickSync (H.264) | Intel QuickSync, H.264 stream | If you have issues, use the VAAPI preset instead |
+| preset-intel-qsv-h265 | Intel QuickSync (H.265) | Intel QuickSync, H.265 stream | If you have issues, use the VAAPI preset instead |
+| preset-nvidia | NVIDIA GPU | NVIDIA GPU | |
+| preset-jetson-h264 | NVIDIA Jetson (H.264) | NVIDIA Jetson, H.264 stream | |
+| preset-jetson-h265 | NVIDIA Jetson (H.265) | NVIDIA Jetson, H.265 stream | |
+| preset-rkmpp | Rockchip RKMPP | Rockchip MPP | Use an image with the `-rk` suffix and run in privileged mode |
+
+
+
+
+1. Navigate to and set **Hardware acceleration arguments** to the appropriate preset for your hardware.
+2. To override for a specific camera, navigate to and set **Hardware acceleration arguments** for that camera.
+
+
+
+
+```yaml
+ffmpeg:
+ hwaccel_args: preset-vaapi
+
+cameras:
+ front_door:
+ ffmpeg:
+ hwaccel_args: preset-nvidia
+```
+
+
+
### Input Args Presets
-Input args presets help make the config more readable and handle use cases for different types of streams to ensure maximum compatibility.
+Input arguments are passed to FFmpeg before your camera source and control how Frigate connects to and reads the stream: the transport protocol, timeouts, reconnection behavior, and how the stream is probed. The right input args ensure a reliable connection and maximum compatibility for each type of stream.
-See [the camera specific docs](/configuration/camera_specific.md) for more info on non-standard cameras and recommendations for using them in Frigate.
+See [the camera-specific docs](/configuration/camera_specific.md) for more on non-standard cameras and recommendations for using them in Frigate.
-| Preset | Usage | Other Notes |
-| -------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------ |
-| preset-http-jpeg-generic | HTTP Live Jpeg | Recommend restreaming live jpeg instead |
-| preset-http-mjpeg-generic | HTTP Mjpeg Stream | Recommend restreaming mjpeg stream instead |
-| preset-http-reolink | Reolink HTTP-FLV Stream | Only for reolink http, not when restreaming as rtsp |
-| preset-rtmp-generic | RTMP Stream | |
-| preset-rtsp-generic | RTSP Stream | This is the default when nothing is specified |
-| preset-rtsp-restream | RTSP Stream from restream | Use for rtsp restream as source for frigate |
-| preset-rtsp-restream-low-latency | RTSP Stream from restream | Use for rtsp restream as source for frigate to lower latency, may cause issues with some cameras |
-| preset-rtsp-udp | RTSP Stream via UDP | Use when camera is UDP only |
-| preset-rtsp-blue-iris | Blue Iris RTSP Stream | Use when consuming a stream from Blue Iris |
+| Preset (config) | UI Label | Usage | Notes |
+| -------------------------------- | ----------------------------------------- | --------------------------- | ------------------------------------------------------------------------------- |
+| preset-http-jpeg-generic | HTTP JPEG (Generic) | HTTP live JPEG | Restreaming the live JPEG is recommended instead |
+| preset-http-mjpeg-generic | HTTP MJPEG (Generic) | HTTP MJPEG stream | Restreaming the MJPEG stream is recommended instead |
+| preset-http-reolink | HTTP - Reolink Cameras | Reolink HTTP-FLV stream | Only for Reolink HTTP, not when restreaming as RTSP |
+| preset-rtmp-generic | RTMP (Generic) | RTMP stream | |
+| preset-rtsp-generic | RTSP (Generic) | RTSP stream | The default when no input args are specified |
+| preset-rtsp-restream | RTSP - Restream from go2rtc | RTSP stream from a restream | Use when a go2rtc restream is the source for Frigate |
+| preset-rtsp-restream-low-latency | RTSP - Restream from go2rtc (Low Latency) | RTSP stream from a restream | Lowers latency for a go2rtc restream source; may cause issues with some cameras |
+| preset-rtsp-udp | RTSP - UDP | RTSP stream over UDP | Use when the camera only supports UDP |
+| preset-rtsp-blue-iris | RTSP - Blue Iris | Blue Iris RTSP stream | Use when consuming a stream from Blue Iris |
:::warning
-It is important to be mindful of input args when using restream because you can have a mix of protocols. `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, when using a reolink cam with the rtsp restream as a source for record the preset-http-reolink will cause a crash. In this case presets will need to be set at the stream level. See the example below.
+Be mindful of input arguments when restreaming, because you can end up with a mix of protocols. The `http` and `rtmp` presets cannot be used with `rtsp` streams. For example, using a Reolink camera with an RTSP restream as the recording source while `preset-http-reolink` is applied will cause a crash. In cases like this, set the preset at the stream level instead. See the example below.
:::
@@ -68,13 +96,13 @@ cameras:
### Output Args Presets
-Output args presets help make the config more readable and handle use cases for different types of streams to ensure consistent recordings.
+Output arguments are passed to FFmpeg after your camera source and control how recordings are written: which codecs are used and whether audio and video are copied as-is or re-encoded. The right output args ensure consistent, playable recordings for each type of stream.
-| Preset | Usage | Other Notes |
-| -------------------------------- | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| preset-record-generic | Record WITHOUT audio | If your camera doesn’t have audio, or if you don’t want to record audio, use this option |
-| preset-record-generic-audio-copy | Record WITH original audio | Use this to enable audio in recordings |
-| preset-record-generic-audio-aac | Record WITH transcoded aac audio | This is the default when no option is specified. Use it to transcode audio to AAC. If the source is already in AAC format, use preset-record-generic-audio-copy instead to avoid unnecessary re-encoding |
-| preset-record-mjpeg | Record an mjpeg stream | Recommend restreaming mjpeg stream instead |
-| preset-record-jpeg | Record live jpeg | Recommend restreaming live jpeg instead |
-| preset-record-ubiquiti | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio |
+| Preset (config) | UI Label | Usage | Notes |
+| -------------------------------- | ------------------------------- | ----------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| preset-record-generic | Record (Generic, no audio) | Record without audio | Use this if your camera has no audio, or if you don't want to record audio |
+| preset-record-generic-audio-copy | Record (Generic + Copy Audio) | Record with the original audio | Use this to keep the camera's audio in recordings without re-encoding |
+| preset-record-generic-audio-aac | Record (Generic + Audio to AAC) | Record with audio transcoded to AAC | The default when no output args are specified. Transcodes audio to AAC. If the source is already AAC, use `preset-record-generic-audio-copy` to avoid re-encoding |
+| preset-record-mjpeg | Record - MJPEG Cameras | Record an MJPEG stream | Restreaming the MJPEG stream is recommended instead |
+| preset-record-jpeg | Record - JPEG Cameras | Record a live JPEG | Restreaming the live JPEG is recommended instead |
+| preset-record-ubiquiti | Record - Ubiquiti Cameras | Record a Ubiquiti stream with audio | Handles Ubiquiti's non-standard audio format |
diff --git a/docs/docs/configuration/genai/config.md b/docs/docs/configuration/genai/config.md
index cde503e8b1..a3cabcbace 100644
--- a/docs/docs/configuration/genai/config.md
+++ b/docs/docs/configuration/genai/config.md
@@ -3,41 +3,79 @@ id: genai_config
title: Configuring Generative AI
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
+
## Configuration
-A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
+A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 5 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI-Compatible section below.
-To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+`genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
-## Ollama
+
+
+
+1. Navigate to .
+ - Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
+ - Set **Provider** to the service you are using (e.g., `ollama`)
+ - Set **Base URL**, **API key**, and **Model** as required by that provider
+ - Set **Roles** to the roles this provider should handle.
+
+
+
+
+```yaml
+genai:
+ my_provider: # any name you like
+ provider: ollama
+ base_url: http://localhost:11434
+ model: qwen3-vl:4b
+ roles:
+ - descriptions
+ - embeddings
+ - chat
+```
+
+
+
+
+The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
+
+Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
+
+If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+
+## Local Providers
+
+Local providers run on your own hardware and keep all data processing private. These require a GPU or dedicated hardware for best performance.
:::warning
-Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
+Running Generative AI models on CPU is not recommended, as high inference times make using Generative AI impractical.
:::
-[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
+### Recommended Local Models
-Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
+#### Vision models
-Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests).
+You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
-### Model Types: Instruct vs Thinking
+| Model | Notes |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. |
+| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
+| `gemma4` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. |
-Most vision-language models are available as **instruct** models, which are fine-tuned to follow instructions and respond concisely to prompts. However, some models (such as certain Qwen-VL or minigpt variants) offer both **instruct** and **thinking** versions.
+#### Embedding models
-- **Instruct models** are always recommended for use with Frigate. These models generate direct, relevant, actionable descriptions that best fit Frigate's object and event summary use case.
-- **Thinking models** are fine-tuned for more free-form, open-ended, and speculative outputs, which are typically not concise and may not provide the practical summaries Frigate expects. For this reason, Frigate does **not** recommend or support using thinking models.
+The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
-Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, Frigate will always use instruct-style prompts and specifically disables thinking-mode behaviors to ensure concise, useful responses.
-
-**Recommendation:**
-Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider’s documentation or model library for guidance on the correct model variant to use.
-
-### Supported Models
-
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). Note that Frigate will not automatically download the model you specify in your config, Ollama will try to download the model but it may take longer than the timeout, it is recommended to pull the model beforehand by running `ollama pull your_model` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+| Model | Notes |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
:::info
@@ -45,49 +83,218 @@ Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger s
:::
+:::note
+
+You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 24 GB to run the 33B models.
+
+:::
+
+### Model Types: Instruct vs Thinking
+
+Vision-language models come in **instruct** variants (fine-tuned to follow instructions and respond concisely), **thinking** variants (fine-tuned for free-form, speculative reasoning), and **hybrid** variants that support both modes per request. Most modern vision-language models are hybrid.
+
+Frigate manages reasoning per task automatically:
+
+- **Description tasks** (object descriptions, review descriptions, review summaries) are synthesis-only and benefit from concise, direct output, so Frigate disables thinking for these calls when the model exposes a per-request toggle.
+- **Chat** lets you toggle thinking on or off from the composer when the configured model supports it.
+
+You can use a pure instruct, hybrid, or thinking-capable model with Frigate. No extra configuration is required to disable thinking for descriptions.
+
+### llama.cpp
+
+[llama.cpp](https://github.com/ggml-org/llama.cpp) is a C++ implementation of LLaMA that provides a high-performance inference server.
+
+It is highly recommended to host the llama.cpp server on a machine with a discrete graphics card, or on an Apple silicon Mac for best performance.
+
+#### Supported Models
+
+You must use a vision capable model with Frigate. The llama.cpp server supports various vision models in GGUF format.
+
+#### Configuration
+
+All llama.cpp native options can be passed through `provider_options`, including `temperature`, `top_k`, `top_p`, `min_p`, `repeat_penalty`, `repeat_last_n`, `seed`, `grammar`, and more. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for a complete list of available parameters.
+
+
+
+
+1. Navigate to .
+ - Set **Provider** to `llamacpp`
+ - Set **Base URL** to your llama.cpp server address (e.g., `http://localhost:8080`)
+ - Set **Model** to the name of your model
+ - Optionally, under **Provider Options**, set `context_size` to override the context size Frigate detects from the server
+
+
+
+
+```yaml
+genai:
+ my_provider:
+ provider: llamacpp
+ base_url: http://localhost:8080
+ model: your-model-name
+ provider_options:
+ context_size: 16000 # Optional, overrides the context size reported by the server.
+```
+
+
+
+
+Frigate queries the llama.cpp server for the model's context size at startup and logs it along with the other detected capabilities. If `context_size` is set in `provider_options`, that value is always used instead, even when the server reports its own.
+
+### Ollama
+
+[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
+
+Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
+
+Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests).
+
:::tip
If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. qwen3-VL supports vision and tools simultaneously in Ollama.
:::
-The following models are recommended:
+Note that Frigate will not automatically download the model you specify in your config. Ollama will try to download the model but it may take longer than the timeout, so it is recommended to pull the model beforehand by running `ollama pull your_model` on your Ollama server/Docker container. The model specified in Frigate's config must match the downloaded model tag.
-| Model | Notes |
-| ------------- | -------------------------------------------------------------------- |
-| `qwen3-vl` | Strong visual and situational understanding, higher vram requirement |
-| `Intern3.5VL` | Relatively fast with good vision comprehension |
-| `gemma3` | Strong frame-to-frame understanding, slower inference times |
-| `qwen2.5-vl` | Fast but capable model with good vision comprehension |
+#### Configuration
-:::note
+
+
-You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
+1. Navigate to .
+ - Set **Provider** to `ollama`
+ - Set **Base URL** to your Ollama server address (e.g., `http://localhost:11434`)
+ - Set **Model** to the model tag (e.g., `qwen3-vl:4b`)
+ - Under **Provider Options**, set `keep_alive` (e.g., `-1`) and `options.num_ctx` to match your desired context size
-:::
-
-#### Ollama Cloud models
-
-Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
-
-### Configuration
+
+
```yaml
genai:
- provider: ollama
- base_url: http://localhost:11434
- model: qwen3-vl:4b
+ my_provider:
+ provider: ollama
+ base_url: http://localhost:11434
+ model: qwen3-vl:4b
+ provider_options: # other Ollama client options can be defined
+ keep_alive: -1
+ options:
+ num_ctx: 8192 # make sure the context matches other services that are using ollama
```
-## Google Gemini
+
+
+
+### OpenAI-Compatible
+
+Frigate supports any provider that implements the OpenAI API standard. This includes self-hosted solutions like [vLLM](https://docs.vllm.ai/), [LocalAI](https://localai.io/), and other OpenAI-compatible servers.
+
+:::tip
+
+For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
+
+```yaml
+genai:
+ my_provider:
+ provider: openai
+ base_url: http://your-llama-server
+ model: your-model-name
+ provider_options:
+ context_size: 8192 # Specify the configured context size
+```
+
+This ensures Frigate uses the correct context window size when generating prompts.
+
+:::
+
+#### Configuration
+
+
+
+
+1. Navigate to .
+ - Set **Provider** to `openai`
+ - Set **Base URL** to your server address (e.g., `http://your-server:port`)
+ - Set **API key** if required by your server
+ - Set **Model** to the model name
+
+
+
+
+```yaml
+genai:
+ my_provider:
+ provider: openai
+ base_url: http://your-server:port
+ api_key: your-api-key # May not be required for local servers
+ model: your-model-name
+```
+
+
+
+
+To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
+
+## Cloud Providers
+
+Cloud providers run on remote infrastructure and require an API key for authentication. These services handle all model inference on their servers.
+
+:::info
+
+Cloud Generative AI providers require an active internet connection to send images and prompts for processing. Local providers like llama.cpp and Ollama (with local models) do not require internet. See [Network Requirements](/frigate/network_requirements#generative-ai) for details.
+
+:::
+
+### Ollama Cloud
+
+Ollama also supports [cloud models](https://ollama.com/cloud), where model inference is performed in the cloud. You can connect directly to Ollama Cloud by setting `base_url` to `https://ollama.com` and providing an API key. Alternatively, you can run Ollama locally and use a cloud model name so your local instance forwards requests to the cloud. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
+
+#### Configuration
+
+
+
+
+1. Navigate to .
+ - Set **Provider** to `ollama`
+ - Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`) or `https://ollama.com` for direct cloud inference
+ - Set **API key** if required by your endpoint (e.g., when using `https://ollama.com`)
+ - Set **Model** to the cloud model name
+
+
+
+
+```yaml
+genai:
+ my_provider:
+ provider: ollama
+ base_url: http://localhost:11434
+ model: cloud-model-name
+```
+
+or when using Ollama Cloud directly
+
+```yaml
+genai:
+ my_provider:
+ provider: ollama
+ base_url: https://ollama.com
+ model: cloud-model-name
+ api_key: your-api-key
+```
+
+
+
+
+### Google Gemini
Google Gemini has a [free tier](https://ai.google.dev/pricing) for the API, however the limits may not be sufficient for standard Frigate usage. Choose a plan appropriate for your installation.
-### Supported Models
+#### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
-### Get API Key
+#### Get API Key
To start using Gemini, you must first get an API key from [Google AI Studio](https://aistudio.google.com).
@@ -96,52 +303,83 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
3. Click "Create API key in new project"
4. Copy the API key for use in your config
-### Configuration
+#### Configuration
+
+
+
+
+1. Navigate to .
+ - Set **Provider** to `gemini`
+ - Set **API key** to your Gemini API key (or use an environment variable such as `{FRIGATE_GEMINI_API_KEY}`)
+ - Set **Model** to the desired model (e.g., `gemini-2.5-flash`)
+
+
+
```yaml
genai:
- provider: gemini
- api_key: "{FRIGATE_GEMINI_API_KEY}"
- model: gemini-2.5-flash
+ my_provider:
+ provider: gemini
+ api_key: "{FRIGATE_GEMINI_API_KEY}"
+ model: gemini-2.5-flash
```
+
+
+
:::note
To use a different Gemini-compatible API endpoint, set the `provider_options` with the `base_url` key to your provider's API URL. For example:
-```yaml {4,5}
+```yaml {5,6}
genai:
- provider: gemini
- ...
- provider_options:
- base_url: https://...
+ my_provider:
+ provider: gemini
+ ...
+ provider_options:
+ base_url: https://...
```
Other HTTP options are available, see the [python-genai documentation](https://github.com/googleapis/python-genai).
:::
-## OpenAI
+### OpenAI
-OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.
+OpenAI does not have a free tier for their API.
-### Supported Models
+#### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
-### Get API Key
+#### Get API Key
To start using OpenAI, you must first [create an API key](https://platform.openai.com/api-keys) and [configure billing](https://platform.openai.com/settings/organization/billing/overview).
-### Configuration
+#### Configuration
+
+
+
+
+1. Navigate to .
+ - Set **Provider** to `openai`
+ - Set **API key** to your OpenAI API key (or use an environment variable such as `{FRIGATE_OPENAI_API_KEY}`)
+ - Set **Model** to the desired model (e.g., `gpt-4o`)
+
+
+
```yaml
genai:
- provider: openai
- api_key: "{FRIGATE_OPENAI_API_KEY}"
- model: gpt-4o
+ my_provider:
+ provider: openai
+ api_key: "{FRIGATE_OPENAI_API_KEY}"
+ model: gpt-4o
```
+
+
+
:::note
To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
@@ -152,37 +390,133 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
-```yaml {5,6}
+```yaml {6,7}
genai:
- provider: openai
- base_url: http://your-llama-server
- model: your-model-name
- provider_options:
- context_size: 8192 # Specify the configured context size
+ my_provider:
+ provider: openai
+ base_url: http://your-llama-server
+ model: your-model-name
+ provider_options:
+ context_size: 8192 # Specify the configured context size
```
This ensures Frigate uses the correct context window size when generating prompts.
:::
-## Azure OpenAI
+### Azure OpenAI
Microsoft offers several vision models through Azure OpenAI. A subscription is required.
-### Supported Models
+#### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
-### Create Resource and Get API Key
+#### Create Resource and Get API Key
To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
-### Configuration
+#### Configuration
+
+
+
+
+1. Navigate to .
+ - Set **Provider** to `azure_openai`
+ - Set **Base URL** to your Azure resource URL including the `api-version` parameter (e.g., `https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview`)
+ - Set **Model** to your deployed model name (e.g., `gpt-5-mini`)
+ - Set **API key** to your Azure OpenAI API key (or use an environment variable such as `{FRIGATE_OPENAI_API_KEY}`)
+
+
+
```yaml
genai:
- provider: azure_openai
- base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
- model: gpt-5-mini
- api_key: "{FRIGATE_OPENAI_API_KEY}"
+ my_provider:
+ provider: azure_openai
+ base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
+ model: gpt-5-mini
+ api_key: "{FRIGATE_OPENAI_API_KEY}"
```
+
+
+
+
+## FAQ
+
+
+
+Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
+
+1. Confirm a provider is available and holds the `descriptions` role.
+ - Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in (`genai..roles`).
+ - A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
+ - In , use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
+
+2. Confirm the feature you expect is actually enabled.
+ - Object descriptions are disabled by default. Turn on (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
+ - Review descriptions are disabled by default. Turn on (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
+
+3. If object descriptions are never requested, check the filters that skip generation.
+ - (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
+ - Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
+ - **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for , file not found` and no description is generated.
+ - **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
+
+4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
+
+ ```yaml
+ logger:
+ default: info
+ logs:
+ # highlight-start
+ frigate.genai: debug
+ frigate.data_processing.post.object_descriptions: debug
+ frigate.data_processing.post.review_descriptions: debug
+ # highlight-end
+ ```
+
+5. Save the exact images and prompts that were sent to your provider.
+ - Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
+ - Review descriptions write `genai-requests//` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
+ - Review summary reports write `genai-requests/-/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
+ - Object descriptions write `genai-requests//` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
+ - Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
+
+
+
+
+For review descriptions, navigate to and set **GenAI config > Save thumbnails** to on.
+
+For object descriptions, navigate to , expand **GenAI object config**, and set **Save thumbnails** to on.
+
+
+
+
+```yaml
+review:
+ genai:
+ enabled: true
+ # highlight-next-line
+ debug_save_thumbnails: true
+
+objects:
+ genai:
+ enabled: true
+ # highlight-next-line
+ debug_save_thumbnails: true
+```
+
+
+
+
+6. Verify the prompt is what you think it is.
+ - Object description prompts are the ones you control directly. A camera-level (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
+ - Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
+
+7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
+ - Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
+ - Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under (`genai..provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
+ - Check **Review Description Speed** and **Object Description Speed** in . If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
+
+
diff --git a/docs/docs/configuration/genai/objects.md b/docs/docs/configuration/genai/objects.md
index e3ae31393d..2e281dc0ec 100644
--- a/docs/docs/configuration/genai/objects.md
+++ b/docs/docs/configuration/genai/objects.md
@@ -3,6 +3,10 @@ id: genai_objects
title: Object Descriptions
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle, or can optionally be sent earlier after a number of significantly changed frames, for example in use in more real-time notifications. Descriptions can also be regenerated manually via the Frigate UI. Note that if you are manually entering a description for tracked objects prior to its end, this will be overwritten by the generated response.
@@ -11,13 +15,13 @@ By default, descriptions will be generated for all tracked objects and all zones
Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
-Generative AI object descriptions can also be toggled dynamically for a camera via MQTT with the topic `frigate//object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
+Generative AI object descriptions can also be toggled dynamically for a camera via MQTT with the topic `frigate//object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt#frigatecamera_nameobject_descriptionsset).
## Usage and Best Practices
-Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
+Frigate's thumbnail search excels at identifying specific details about tracked objects -- for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate's default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
-While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
+While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate's default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what's happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they're moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation's context.
## Custom Prompts
@@ -33,13 +37,25 @@ Prompts can use variable replacements `{label}`, `{sub_label}`, and `{camera}` t
:::
-You are also able to define custom prompts in your configuration.
+You can define custom prompts at the global level and per-object type. To configure custom prompts:
+
+
+
+
+1. Navigate to .
+ - Expand the **GenAI object config** section
+ - Set **Caption prompt** to your custom prompt text
+ - Under **Object prompts**, add entries keyed by object type (e.g., `person`, `car`) with custom prompts for each
+
+
+
```yaml
genai:
- provider: ollama
- base_url: http://localhost:11434
- model: qwen3-vl:8b-instruct
+ my_provider:
+ provider: ollama
+ base_url: http://localhost:11434
+ model: qwen3-vl:8b-instruct
objects:
genai:
@@ -49,7 +65,25 @@ objects:
car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
```
-Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
+
+
+
+Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera. To configure camera-level overrides:
+
+
+
+
+1. Navigate to for the desired camera.
+ - Expand the **GenAI object config** section
+ - Set **Enable GenAI** to on
+ - Set **Use snapshots** to on if desired
+ - Set **Caption prompt** to a camera-specific prompt
+ - Under **Object prompts**, add entries keyed by object type with camera-specific prompts
+ - Set **GenAI objects** to the list of object types that should receive descriptions (e.g., `person`, `cat`)
+ - Set **Required zones** to limit descriptions to objects in specific zones (e.g., `steps`)
+
+
+
```yaml
cameras:
@@ -69,6 +103,9 @@ cameras:
- steps
```
+
+
+
### Experiment with prompts
Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
@@ -76,3 +113,7 @@ Many providers also have a public facing chat interface for their models. Downlo
- OpenAI - [ChatGPT](https://chatgpt.com)
- Gemini - [Google AI Studio](https://aistudio.google.com)
- Ollama - [Open WebUI](https://docs.openwebui.com/)
+
+## Troubleshooting
+
+If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
diff --git a/docs/docs/configuration/genai/review_summaries.md b/docs/docs/configuration/genai/review_summaries.md
index c0d677a013..eabf094a15 100644
--- a/docs/docs/configuration/genai/review_summaries.md
+++ b/docs/docs/configuration/genai/review_summaries.md
@@ -3,11 +3,15 @@ id: genai_review
title: Review Summaries
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
Generative AI can be used to automatically generate structured summaries of review items. These summaries will show up in Frigate's native notifications as well as in the UI. Generative AI can also be used to take a collection of summaries over a period of time and provide a report, which may be useful to get a quick report of everything that happened while out for some amount of time.
Requests for a summary are requested automatically to your AI provider for alert review items when the activity has ended, they can also be optionally enabled for detections as well.
-Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
+Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt#frigatecamera_namereview_descriptionsset).
## Review Summary Usage and Best Practices
@@ -28,6 +32,30 @@ This will show in multiple places in the UI to give additional context about eac
Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response.
+To configure the activity context prompt:
+
+
+
+
+Navigate to .
+
+- Set **GenAI config > Activity context prompt** to your custom activity context text
+
+
+
+
+```yaml
+review:
+ genai:
+ activity_context_prompt: |
+ ### Normal Activity Indicators (Level 0)
+ - Known/verified people in any zone at any time
+ ...
+```
+
+
+
+
Default Activity Context Prompt
@@ -74,7 +102,18 @@ review:
### Image Source
-By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, you can configure Frigate to extract frames directly from recordings at a higher resolution:
+By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, configure Frigate to extract frames directly from recordings at a higher resolution.
+
+
+
+
+Navigate to .
+
+- Set **GenAI config > Enable GenAI descriptions** to on
+- Set **GenAI config > Review image source** to `recordings` (default is `preview`)
+
+
+
```yaml
review:
@@ -84,6 +123,9 @@ review:
image_source: recordings # Options: "preview" (default) or "recordings"
```
+
+
+
When using `recordings`, frames are extracted at 480px height while maintaining the camera's original aspect ratio, providing better detail for the LLM while being mindful of context window size. This is particularly useful for scenarios where fine details matter, such as identifying license plates, reading text, or analyzing distant objects.
The number of frames sent to the LLM is dynamically calculated based on:
@@ -103,7 +145,17 @@ If recordings are not available for a given time period, the system will automat
### Additional Concerns
-Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. These concerns can be configured so that the review summaries will make note of them if the activity requires additional review. For example:
+Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. Configure these concerns so that review summaries will make note of them if the activity requires additional review.
+
+
+
+
+Navigate to .
+
+- Set **GenAI config > Additional concerns** to a list of your concerns (e.g., `animals in the garden`)
+
+
+
```yaml {4,5}
review:
@@ -113,9 +165,22 @@ review:
- animals in the garden
```
+
+
+
### Preferred Language
-By default, review summaries are generated in English. You can configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option:
+By default, review summaries are generated in English. Configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option.
+
+
+
+
+Navigate to .
+
+- Set **GenAI config > Preferred language** to the desired language (e.g., `Spanish`)
+
+
+
```yaml {4}
review:
@@ -124,6 +189,9 @@ review:
preferred_language: Spanish
```
+
+
+
## Review Reports
Along with individual review item summaries, Generative AI can also produce a single report of review items from all cameras marked "suspicious" over a specified time period (for example, a daily summary of suspicious activity while you're on vacation).
@@ -133,3 +201,7 @@ Along with individual review item summaries, Generative AI can also produce a si
Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
+
+## Troubleshooting
+
+If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
diff --git a/docs/docs/configuration/go2rtc.md b/docs/docs/configuration/go2rtc.md
new file mode 100644
index 0000000000..3672fdf575
--- /dev/null
+++ b/docs/docs/configuration/go2rtc.md
@@ -0,0 +1,72 @@
+---
+id: go2rtc
+title: go2rtc
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Frigate uses the bundled go2rtc to power a number of key features:
+
+- WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
+- Live stream support for cameras in Home Assistant Integration
+- RTSP relay for use with other consumers to reduce the number of connections to your camera streams
+
+:::tip[Most users no longer need to configure go2rtc by hand]
+
+The [**camera setup wizard**](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add cameras. Click **Add Camera** in , and the wizard probes your camera and writes its configuration for you, including the go2rtc restream and the live stream mapping, so go2rtc is set up automatically.
+
+This guide is mainly useful if you are **upgrading from an older version and have existing cameras that don't yet use go2rtc**, or if you want to fine-tune a stream by hand (for example, to transcode a codec your browser can't play). The [go2rtc troubleshooting guide](/troubleshooting/go2rtc) applies regardless of how your cameras were added.
+
+:::
+
+## Adding a go2rtc stream manually
+
+If you added your cameras with the wizard, go2rtc is already configured. You can skip straight to [troubleshooting](/troubleshooting/go2rtc). The steps below are for upgrading users with existing cameras that aren't using go2rtc yet, or for anyone who prefers to configure a stream by hand.
+
+Configure go2rtc to connect to your camera by adding the stream you want to use for live view. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-streams), not just rtsp.
+
+:::tip
+
+For the best experience, set the stream name under `go2rtc` to match the name of your camera so that Frigate will automatically map it and be able to use better live view options for the camera.
+
+See [the live view docs](/configuration/live#setting-streams-for-live-ui) for more information.
+
+:::
+
+
+
+
+Navigate to and click **Add stream**. Give the stream a name (use the camera's name so Frigate can auto-map it - for example, if your camera's name is `back`, use `back` as the go2rtc stream name), then paste the camera's stream URL into the **Source** field. Save the section.
+
+
+
+
+```yaml
+go2rtc:
+ streams:
+ back:
+ - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+```
+
+
+
+
+After adding this to the config, restart Frigate and try to watch the live stream for a single camera by clicking on it from the dashboard. It should look much clearer and more fluent than the original jsmpeg stream.
+
+### Next steps
+
+1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
+2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
+3. If your camera supports two-way talk, you must configure your stream with `#backchannel=0` to prevent go2rtc from blocking other applications from accessing the camera's audio output. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
+
+## Troubleshooting
+
+If your stream won't play, has no audio, uses excessive CPU, or otherwise misbehaves, see the dedicated [go2rtc troubleshooting guide](/troubleshooting/go2rtc). It walks through how to isolate where the problem is and covers the most common issues: unsupported codecs, H.265/HEVC, audio, WebRTC and two-way talk, hardware-accelerated transcoding with FFmpeg 8, and camera-specific quirks.
+
+## Homekit Configuration
+
+To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://:1984/editor.html`. Pairings are saved back to that file automatically.
+
+See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.
diff --git a/docs/docs/configuration/hardware_acceleration_video.md b/docs/docs/configuration/hardware_acceleration_video.md
index 318e1b23e5..360ca1a50c 100644
--- a/docs/docs/configuration/hardware_acceleration_video.md
+++ b/docs/docs/configuration/hardware_acceleration_video.md
@@ -4,6 +4,9 @@ title: Video Decoding
---
import CommunityBadge from '@site/src/components/CommunityBadge';
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
# Video Decoding
@@ -14,8 +17,6 @@ Some types of hardware acceleration are detected and used automatically, but you
- Check the logs: A message will either say that hardware acceleration was automatically detected, or there will be a warning that no hardware acceleration was automatically detected
- If hardware acceleration is specified in the config, verification can be done by ensuring the logs are free from errors. There is no CPU fallback for hardware acceleration.
-:::info
-
Frigate supports presets for optimal hardware accelerated video decoding:
**AMD**
@@ -46,29 +47,24 @@ Frigate supports presets for optimal hardware accelerated video decoding:
Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
-:::
-
## Intel-based CPUs
Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.
-:::info
-
**Recommended hwaccel Preset**
-| CPU Generation | Intel Driver | Recommended Preset | Notes |
-| -------------- | ------------ | ------------------- | ------------------------------------------- |
-| gen1 - gen5 | i965 | preset-vaapi | qsv is not supported, may not support H.265 |
-| gen6 - gen7 | iHD | preset-vaapi | qsv is not supported |
-| gen8 - gen12 | iHD | preset-vaapi | preset-intel-qsv-\* can also be used |
-| gen13+ | iHD / Xe | preset-intel-qsv-\* | |
-| Intel Arc GPU | iHD / Xe | preset-intel-qsv-\* | |
-
-:::
+| CPU Generation | Intel Driver | Recommended Preset | Notes |
+| ------------------ | ------------ | ------------------- | ------------------------------------------- |
+| gen1 - gen5 | i965 | preset-vaapi | qsv is not supported, may not support H.265 |
+| gen6 - gen7 | iHD | preset-vaapi | qsv is not supported |
+| gen8 - gen12 | iHD | preset-vaapi | preset-intel-qsv-\* can also be used |
+| gen13+ | iHD / Xe | preset-intel-qsv-\* | |
+| Intel Arc A-series | iHD / Xe | preset-intel-qsv-\* | |
+| Intel Arc B-series | iHD / Xe | preset-intel-qsv-\* | Requires host kernel 6.12+ |
:::note
-The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
+The default driver is `iHD`. You may need to change the driver to `i965` by adding the following environment variable `LIBVA_DRIVER_NAME=i965` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/000005505/processors.html) to figure out what generation your CPU is.
@@ -78,111 +74,86 @@ See [The Intel Docs](https://www.intel.com/content/www/us/en/support/articles/00
VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to .
+
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-vaapi
```
+
+
+
### Via Quicksync
#### H.264 streams
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to .
+
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-intel-qsv-h264
```
+
+
+
#### H.265 streams
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to .
+
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-intel-qsv-h265
```
-### Configuring Intel GPU Stats in Docker
+
+
-Additional configuration is needed for the Docker container to be able to access the `intel_gpu_top` command for GPU stats. There are two options:
+### Configuring Intel GPU Stats
-1. Run the container as privileged.
-2. Add the `CAP_PERFMON` capability (note: you might need to set the `perf_event_paranoid` low enough to allow access to the performance event system.)
+Frigate reads Intel GPU utilization directly from the kernel's per-client DRM usage counters exposed at `/proc//fdinfo/`. This requires:
-#### Run as privileged
+- Linux kernel **5.19 or newer** for the `i915` driver, or any release of the `xe` driver.
+- Frigate running with permission to read other processes' fdinfo. Running as root inside the container (the default) satisfies this; non-root setups may need `CAP_SYS_PTRACE`.
-This method works, but it gives more permissions to the container than are actually needed.
+No `intel_gpu_top` binary, `CAP_PERFMON`, privileged mode, or `perf_event_paranoid` tuning is required.
-##### Docker Compose - Privileged
+#### Stats for SR-IOV or specific devices
-```yaml
-services:
- frigate:
- ...
- image: ghcr.io/blakeblackshear/frigate:stable
- # highlight-next-line
- privileged: true
-```
-
-##### Docker Run CLI - Privileged
-
-```bash {4}
-docker run -d \
- --name frigate \
- ...
- --privileged \
- ghcr.io/blakeblackshear/frigate:stable
-```
-
-#### CAP_PERFMON
-
-Only recent versions of Docker support the `CAP_PERFMON` capability. You can test to see if yours supports it by running: `docker run --cap-add=CAP_PERFMON hello-world`
-
-##### Docker Compose - CAP_PERFMON
-
-```yaml {5,6}
-services:
- frigate:
- ...
- image: ghcr.io/blakeblackshear/frigate:stable
- cap_add:
- - CAP_PERFMON
-```
-
-##### Docker Run CLI - CAP_PERFMON
-
-```bash {4}
-docker run -d \
- --name frigate \
- ...
- --cap-add=CAP_PERFMON \
- ghcr.io/blakeblackshear/frigate:stable
-```
-
-#### perf_event_paranoid
-
-_Note: This setting must be changed for the entire system._
-
-For more information on the various values across different distributions, see https://askubuntu.com/questions/1400874/what-does-perf-paranoia-level-four-do.
-
-Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
-
-#### Stats for SR-IOV or other devices
-
-When using virtualized GPUs via SR-IOV, you need to specify the device path to use to gather stats from `intel_gpu_top`. This example may work for some systems using SR-IOV:
+If the host has more than one Intel GPU (e.g. an iGPU plus a discrete GPU, or SR-IOV virtual functions), pin stats collection to a specific device by setting `intel_gpu_device` to either its PCI bus address or a DRM card/render-node path:
```yaml
telemetry:
stats:
- intel_gpu_device: "sriov"
+ intel_gpu_device: "0000:00:02.0"
```
-For other virtualized GPUs, try specifying the direct path to the device instead:
-
```yaml
telemetry:
stats:
- intel_gpu_device: "drm:/dev/dri/card0"
+ intel_gpu_device: "/dev/dri/card1"
```
-If you are passing in a device path, make sure you've passed the device through to the container.
+When passing a device path, make sure the device is also passed through to the container.
## AMD-based CPUs
@@ -190,20 +161,31 @@ Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video
### Configuring Radeon Driver
-You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced.md#environment_vars).
+You need to change the driver to `radeonsi` by adding the following environment variable `LIBVA_DRIVER_NAME=radeonsi` to your docker-compose file or [in the `config.yml` for HA App users](advanced/system.md#environment_vars).
### Via VAAPI
VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to .
+
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-vaapi
```
+
+
+
## NVIDIA GPUs
-While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
+While older GPUs may work, it is recommended to use modern, supported GPUs. NVIDIA provides a [matrix of supported GPUs and features](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). If your card is on the list and supports CUVID/NVDEC, it will most likely work with Frigate for decoding. However, you must also use [a driver version that will work with FFmpeg](https://github.com/FFmpeg/nv-codec-headers/blob/master/README). Older driver versions may be missing symbols and fail to work, and older cards are not supported by newer driver versions. The only way around this is to [provide your own FFmpeg](/configuration/advanced/system#custom-ffmpeg-build) that will work with your driver version, but this is unsupported and may not work well if at all.
A more complete list of cards and their compatible drivers is available in the [driver release readme](https://download.nvidia.com/XFree86/Linux-x86_64/525.85.05/README/supportedchips.html).
@@ -244,11 +226,22 @@ docker run -d \
Using `preset-nvidia` ffmpeg will automatically select the necessary profile for the incoming video, and will log an error if the profile is not supported by your GPU.
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to .
+
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-nvidia
```
+
+
+
If everything is working correctly, you should see a significant improvement in performance.
Verify that hardware decoding is working by running `nvidia-smi`, which should show `ffmpeg`
processes:
@@ -296,6 +289,14 @@ These instructions were originally based on the [Jellyfin documentation](https:/
Ensure you increase the allocated RAM for your GPU to at least 128 (`raspi-config` > Performance Options > GPU Memory).
If you are using the HA App, you may need to use the full access variant and turn off _Protection mode_ for hardware acceleration.
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)` (for H.264 streams) or `Raspberry Pi (H.265)` (for H.265/HEVC streams). For per-camera overrides, navigate to .
+
+
+
+
```yaml
# if you want to decode a h264 stream
ffmpeg:
@@ -306,6 +307,9 @@ ffmpeg:
hwaccel_args: preset-rpi-64-h265
```
+
+
+
:::note
If running Frigate through Docker, you either need to run in privileged mode or
@@ -405,11 +409,22 @@ A list of supported codecs (you can use `ffmpeg -decoders | grep nvmpi` in the c
For example, for H264 video, you'll select `preset-jetson-h264`.
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to .
+
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-jetson-h264
```
+
+
+
If everything is working correctly, you should see a significant reduction in ffmpeg CPU load and power consumption.
Verify that hardware decoding is working by running `jtop` (`sudo pip3 install -U jetson-stats`), which should show
that NVDEC/NVDEC1 are in use.
@@ -424,13 +439,24 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
### Configuration
-Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
+Set the FFmpeg hwaccel preset to enable hardware video processing.
+
+
+
+
+Navigate to and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to .
+
+
+
```yaml
ffmpeg:
hwaccel_args: preset-rkmpp
```
+
+
+
:::note
Make sure that your SoC supports hardware acceleration for your input stream. For example, if your camera streams with h265 encoding and a 4k resolution, your SoC must be able to de- and encode h265 with a 4k resolution or higher. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
@@ -451,7 +477,7 @@ Error marking filters as finished
Restarting ffmpeg...
```
-you should try to uprade to FFmpeg 7. This can be done using this config option:
+you should try to upgrade to FFmpeg 7. This can be done using this config option:
```yaml
ffmpeg:
@@ -472,7 +498,7 @@ cameras:
## Synaptics
-Hardware accelerated video de-/encoding is supported on Synpatics SL-series SoC.
+Hardware accelerated video de-/encoding is supported on Synaptics SL-series SoC.
### Prerequisites
@@ -480,7 +506,15 @@ Make sure to follow the [Synaptics specific installation instructions](/frigate/
### Configuration
-Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
+Set the FFmpeg hwaccel args to enable hardware video processing.
+
+
+
+
+Navigate to and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to .
+
+
+
```yaml {2}
ffmpeg:
@@ -490,6 +524,9 @@ output_args:
record: preset-record-generic-audio-aac
```
+
+
+
:::warning
Make sure that your SoC supports hardware acceleration for your input stream and your input stream is h264 encoding. For example, if your camera streams with h264 encoding, your SoC must be able to de- and encode with it. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
diff --git a/docs/docs/configuration/index.md b/docs/docs/configuration/index.md
deleted file mode 100644
index a7495b28bb..0000000000
--- a/docs/docs/configuration/index.md
+++ /dev/null
@@ -1,267 +0,0 @@
----
-id: index
-title: Frigate Configuration
----
-
-For Home Assistant App installations, the config file should be at `/addon_configs//config.yml`, where `` is specific to the variant of the Frigate App you are running. See the list of directories [here](#accessing-app-config-dir).
-
-For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.
-
-It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
-
-It is recommended to start with a minimal configuration and add to it as described in [this guide](../guides/getting_started.md) and use the built in configuration editor in Frigate's UI which supports validation.
-
-```yaml
-mqtt:
- enabled: False
-
-cameras:
- dummy_camera: # <--- this will be changed to your actual camera later
- enabled: False
- ffmpeg:
- inputs:
- - path: rtsp://127.0.0.1:554/rtsp
- roles:
- - detect
-```
-
-## Accessing the Home Assistant App configuration directory {#accessing-app-config-dir}
-
-When running Frigate through the HA App, the Frigate `/config` directory is mapped to `/addon_configs/` in the host, where `` is specific to the variant of the Frigate App you are running.
-
-| App Variant | Configuration directory |
-| -------------------------- | ----------------------------------------- |
-| Frigate | `/addon_configs/ccab4aaf_frigate` |
-| Frigate (Full Access) | `/addon_configs/ccab4aaf_frigate-fa` |
-| Frigate Beta | `/addon_configs/ccab4aaf_frigate-beta` |
-| Frigate Beta (Full Access) | `/addon_configs/ccab4aaf_frigate-fa-beta` |
-
-**Whenever you see `/config` in the documentation, it refers to this directory.**
-
-If for example you are running the standard App variant and use the [VS Code App](https://github.com/hassio-addons/addon-vscode) to browse your files, you can click _File_ > _Open folder..._ and navigate to `/addon_configs/ccab4aaf_frigate` to access the Frigate `/config` directory and edit the `config.yaml` file. You can also use the built-in file editor in the Frigate UI to edit the configuration file.
-
-## VS Code Configuration Schema
-
-VS Code supports JSON schemas for automatically validating configuration files. You can enable this feature by adding `# yaml-language-server: $schema=http://frigate_host:5000/api/config/schema.json` to the beginning of the configuration file. Replace `frigate_host` with the IP address or hostname of your Frigate server. If you're using both VS Code and Frigate as an App, you should use `ccab4aaf-frigate` instead. Make sure to expose the internal unauthenticated port `5000` when accessing the config from VS Code on another machine.
-
-## Environment Variable Substitution
-
-Frigate supports the use of environment variables starting with `FRIGATE_` **only** where specifically indicated in the [reference config](./reference.md). For example, the following values can be replaced at runtime by using environment variables:
-
-```yaml
-mqtt:
- host: "{FRIGATE_MQTT_HOST}"
- user: "{FRIGATE_MQTT_USER}"
- password: "{FRIGATE_MQTT_PASSWORD}"
-```
-
-```yaml
-- path: rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:8554/unicast
-```
-
-```yaml
-onvif:
- host: "192.168.1.12"
- port: 8000
- user: "{FRIGATE_RTSP_USER}"
- password: "{FRIGATE_RTSP_PASSWORD}"
-```
-
-```yaml
-go2rtc:
- rtsp:
- username: "{FRIGATE_GO2RTC_RTSP_USERNAME}"
- password: "{FRIGATE_GO2RTC_RTSP_PASSWORD}"
-```
-
-```yaml
-genai:
- api_key: "{FRIGATE_GENAI_API_KEY}"
-```
-
-## Common configuration examples
-
-Here are some common starter configuration examples. Refer to the [reference config](./reference.md) for detailed information about all the config values.
-
-### Raspberry Pi Home Assistant App with USB Coral
-
-- Single camera with 720p, 5fps stream for detect
-- MQTT connected to the Home Assistant Mosquitto App
-- Hardware acceleration for decoding video
-- USB Coral detector
-- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
-- Save snapshots for 30 days
-- Motion mask for the camera timestamp
-
-```yaml
-mqtt:
- host: core-mosquitto
- user: mqtt-user
- password: xxxxxxxxxx
-
-ffmpeg:
- hwaccel_args: preset-rpi-64-h264
-
-detectors:
- coral:
- type: edgetpu
- device: usb
-
-record:
- enabled: True
- motion:
- days: 7
- alerts:
- retain:
- days: 30
- mode: motion
- detections:
- retain:
- days: 30
- mode: motion
-
-snapshots:
- enabled: True
- retain:
- default: 30
-
-cameras:
- name_of_your_camera:
- detect:
- width: 1280
- height: 720
- fps: 5
- ffmpeg:
- inputs:
- - path: rtsp://10.0.10.10:554/rtsp
- roles:
- - detect
- motion:
- mask:
- - 0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400
-```
-
-### Standalone Intel Mini PC with USB Coral
-
-- Single camera with 720p, 5fps stream for detect
-- MQTT disabled (not integrated with home assistant)
-- VAAPI hardware acceleration for decoding video
-- USB Coral detector
-- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
-- Save snapshots for 30 days
-- Motion mask for the camera timestamp
-
-```yaml
-mqtt:
- enabled: False
-
-ffmpeg:
- hwaccel_args: preset-vaapi
-
-detectors:
- coral:
- type: edgetpu
- device: usb
-
-record:
- enabled: True
- motion:
- days: 7
- alerts:
- retain:
- days: 30
- mode: motion
- detections:
- retain:
- days: 30
- mode: motion
-
-snapshots:
- enabled: True
- retain:
- default: 30
-
-cameras:
- name_of_your_camera:
- detect:
- width: 1280
- height: 720
- fps: 5
- ffmpeg:
- inputs:
- - path: rtsp://10.0.10.10:554/rtsp
- roles:
- - detect
- motion:
- mask:
- - 0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400
-```
-
-### Home Assistant integrated Intel Mini PC with OpenVino
-
-- Single camera with 720p, 5fps stream for detect
-- MQTT connected to same mqtt server as home assistant
-- VAAPI hardware acceleration for decoding video
-- OpenVino detector
-- Save all video with any detectable motion for 7 days regardless of whether any objects were detected or not
-- Continue to keep all video if it qualified as an alert or detection for 30 days
-- Save snapshots for 30 days
-- Motion mask for the camera timestamp
-
-```yaml
-mqtt:
- host: 192.168.X.X # <---- same mqtt broker that home assistant uses
- user: mqtt-user
- password: xxxxxxxxxx
-
-ffmpeg:
- hwaccel_args: preset-vaapi
-
-detectors:
- ov:
- type: openvino
- device: AUTO
-
-model:
- width: 300
- height: 300
- input_tensor: nhwc
- input_pixel_format: bgr
- path: /openvino-model/ssdlite_mobilenet_v2.xml
- labelmap_path: /openvino-model/coco_91cl_bkgr.txt
-
-record:
- enabled: True
- motion:
- days: 7
- alerts:
- retain:
- days: 30
- mode: motion
- detections:
- retain:
- days: 30
- mode: motion
-
-snapshots:
- enabled: True
- retain:
- default: 30
-
-cameras:
- name_of_your_camera:
- detect:
- width: 1280
- height: 720
- fps: 5
- ffmpeg:
- inputs:
- - path: rtsp://10.0.10.10:554/rtsp
- roles:
- - detect
- motion:
- mask:
- - 0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400
-```
diff --git a/docs/docs/configuration/license_plate_recognition.md b/docs/docs/configuration/license_plate_recognition.md
index a44006b634..2c7c8e4cbb 100644
--- a/docs/docs/configuration/license_plate_recognition.md
+++ b/docs/docs/configuration/license_plate_recognition.md
@@ -3,17 +3,28 @@ id: license_plate_recognition
title: License Plate Recognition (LPR)
---
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
+
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
+:::info
+
+License plate recognition requires a one-time internet connection to download OCR and detection models from GitHub. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
When a plate is recognized, the details are:
- Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.
- Viewable in the Details pane in Review/History.
- Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
- Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
## Model Requirements
@@ -24,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
:::note
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
:::
@@ -34,14 +45,35 @@ License plate recognition works by running AI models locally on your system. The
## Configuration
-License plate recognition is disabled by default. Enable it in your config file:
+License plate recognition is disabled by default and must be enabled before it can be used.
+
+
+
+
+Navigate to .
+
+- Set **Enable LPR** to on
+
+
+
```yaml
lpr:
enabled: True
```
-Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. You should disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras:
+
+
+
+Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. Disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras.
+
+
+
+
+Navigate to for the desired camera and disable the **Enable LPR** toggle.
+
+
+
```yaml {4,5}
cameras:
@@ -51,65 +83,144 @@ cameras:
enabled: False
```
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+
+
+
+For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
## Advanced Configuration
-Fine-tune the LPR feature using these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
+Fine-tune the LPR feature using these optional parameters. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
### Detection
-- **`detection_threshold`**: License plate object detection confidence score required before recognition runs.
+
+
+
+Navigate to .
+
+- **Detection threshold**: License plate object detection confidence score required before recognition runs. This field only applies to the standalone license plate detection model; `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
- Default: `0.7`
- - Note: This is field only applies to the standalone license plate detection model, `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
-- **`min_area`**: Defines the minimum area (in pixels) a license plate must be before recognition runs.
- - Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
- - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
-- **`device`**: Device to use to run license plate detection _and_ recognition models.
+- **Minimum plate area**: Minimum area (in pixels) a license plate must be before recognition runs. This is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
+ - Default: `1000` pixels
+- **Device**: Device to use to run license plate detection _and_ recognition models. Auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
- Default: `None`
- - This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
-- **`model_size`**: The size of the model used to identify regions of text on plates.
+- **Model size**: The size of the model used to identify regions of text on plates. The `small` model is fast and identifies groups of Latin and Chinese characters. The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. If your country or region does not use multi-line plates, you should use the `small` model.
- Default: `small`
- - This can be `small` or `large`.
- - The `small` model is fast and identifies groups of Latin and Chinese characters.
- - The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the `small` model.
- - If your country or region does not use multi-line plates, you should use the `small` model as performance is much better for single-line plates.
+
+
+
+
+```yaml
+lpr:
+ enabled: True
+ detection_threshold: 0.7
+ min_area: 1000
+ device: CPU
+ model_size: small
+```
+
+
+
### Recognition
-- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
- - Default: `0.9`.
-- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label` to an object.
- - Use this to filter out short, incomplete, or incorrect detections.
-- **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
- - `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7"
- - `"^[A-Z]{2}[0-9]{2} [A-Z]{3}$"` matches plates like "AB12 XYZ" or "XY68 ABC"
- - Websites like https://regex101.com/ can help test regular expressions for your plates.
+
+
+
+Navigate to .
+
+- **Recognition threshold**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
+ - Default: `0.9`
+- **Min plate length**: Minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label`. Use this to filter out short, incomplete, or incorrect detections.
+- **Plate format regex**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded. Websites like https://regex101.com/ can help test regular expressions for your plates.
+
+
+
+
+```yaml
+lpr:
+ enabled: True
+ recognition_threshold: 0.9
+ min_plate_length: 4
+ format: "^[A-Z]{2}[0-9]{2} [A-Z]{3}$"
+```
+
+
+
### Matching
-- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` and `motorcycle` objects when a recognized plate matches a known value.
- - These labels appear in the UI, filters, and notifications.
- - Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
-- **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
- - For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`.
- - This parameter will _not_ operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`.
+
+
+
+Navigate to .
+
+- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
+
+
+
+
+```yaml
+lpr:
+ enabled: True
+ match_distance: 1
+ known_plates:
+ Wife's Car:
+ - "ABC-1234"
+ Johnny:
+ - "J*N-*234"
+```
+
+
+
### Image Enhancement
-- **`enhancement`**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. This preprocessing step can sometimes improve accuracy but may also have the opposite effect.
+
+
+
+Navigate to .
+
+- **Enhancement level**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters. This setting is best adjusted at the camera level if running LPR on multiple cameras.
- Default: `0` (no enhancement)
- - Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters, actually making them much harder for Frigate to recognize.
- - This setting is best adjusted at the camera level if running LPR on multiple cameras.
- - If Frigate is already recognizing plates correctly, leave this setting at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 5 and adjusting as needed. You should see how different enhancement levels affect your plates. Use the `debug_save_plates` configuration option (see below).
+
+
+
+
+```yaml
+lpr:
+ enabled: True
+ enhancement: 1
+```
+
+
+
+
+If Frigate is already recognizing plates correctly, leave enhancement at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 3 and adjusting as needed. Use the `debug_save_plates` configuration option (see below) to see how different enhancement levels affect your plates.
### Normalization Rules
-- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially and are applied _before_ the `format` regex, if specified. Each rule must have a `pattern` (which can be a string or a regex) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
+
+
-These rules must be defined at the global level of your `lpr` config.
+Navigate to .
+
+Under **Replacement rules**, add regex rules to normalize detected plate strings before matching. Rules fire in order. For example:
+
+| Pattern | Replacement | Description |
+| ---------------- | ----------- | -------------------------------------------------- |
+| `[%#*?]` | _(empty)_ | Remove noise symbols |
+| `[= ]` | `-` | Normalize `=` or space to dash |
+| `O` | `0` | Swap `O` to `0` (common OCR error) |
+| `I` | `1` | Swap `I` to `1` |
+| `(\w{3})(\w{3})` | `\1-\2` | Split 6 chars into groups (e.g., ABC123 → ABC-123) |
+
+
+
```yaml
lpr:
@@ -126,6 +237,11 @@ lpr:
replacement: '\1-\2'
```
+
+
+
+These rules must be defined at the global level of your `lpr` config.
+
- Rules fire in order: In the example above: clean noise first, then separators, then swaps, then splits.
- Backrefs (`\1`, `\2`) allow dynamic replacements (e.g., capture groups).
- Any changes made by the rules are printed to the LPR debug log.
@@ -133,13 +249,50 @@ lpr:
### Debugging
-- **`debug_save_plates`**: Set to `True` to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `/`, and named based on the capture timestamp.
- - These saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
- - **Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
+
+
+
+Navigate to .
+
+- **Save debug plates**: Set to on to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `/`, and named based on the capture timestamp.
+
+
+
+
+```yaml
+lpr:
+ enabled: True
+ debug_save_plates: True
+```
+
+
+
+
+The saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
+
+**Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
## Configuration Examples
-These configuration parameters are available at the global level of your config. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
+These configuration parameters are available at the global level. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------------ | ----------------------------------------------------------------------------------------------------- |
+| **Enable LPR** | Set to on |
+| **Minimum plate area** | Set to `1500` to ignore plates with an area (length x width) smaller than 1500 pixels |
+| **Min plate length** | Set to `4` to only recognize plates with 4 or more characters |
+| **Known plates > Wife's Car** | `ABC-1234`, `ABC-I234` (accounts for potential confusion between the number one and capital letter I) |
+| **Known plates > Johnny** | `J*N-*234` (matches JHN-1234 and JMN-I234; `*` matches any number of characters) |
+| **Known plates > Sally** | `[S5]LL 1234` (matches both SLL 1234 and 5LL 1234) |
+| **Known plates > Work Trucks** | `EMP-[0-9]{3}[A-Z]` (matches plates like EMP-123A, EMP-456Z) |
+
+
+
```yaml
lpr:
@@ -158,27 +311,20 @@ lpr:
- "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
```
-```yaml
-lpr:
- enabled: True
- min_area: 4000 # Run recognition on larger plates only (4000 pixels represents a 63x63 pixel square in your image)
- recognition_threshold: 0.85
- format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers
- match_distance: 1 # Allow one character variation in plate matching
- replace_rules:
- - pattern: "O"
- replacement: "0" # Replace the letter O with the number 0 in every plate
- known_plates:
- Delivery Van:
- - "RJ K5678"
- - "UP A1234"
- Supervisor:
- - "MN D3163"
-```
+
+
:::note
-If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+
+
+
+
+Navigate to for the desired camera and disable the **Enable LPR** toggle.
+
+
+
```yaml
cameras:
@@ -188,13 +334,16 @@ cameras:
...
```
+
+
+
:::
## Dedicated LPR Cameras
Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to capture license plates on distant vehicles, often with fine-tuned settings to capture plates at night.
-To mark a camera as a dedicated LPR camera, add `type: "lpr"` the camera configuration.
+To mark a camera as a dedicated LPR camera, set `type: "lpr"` in the camera configuration.
:::note
@@ -210,6 +359,55 @@ Users running a Frigate+ model (or any model that natively detects `license_plat
An example configuration for a dedicated LPR camera using a `license_plate`-detecting model:
+
+
+
+Navigate to and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
+
+Navigate to and add your camera streams.
+
+Navigate to .
+
+| Field | Description |
+| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
+| **Enable object detection** | Set to on |
+| **Detect FPS** | Set to `5`. Increase to `10` if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended. |
+| **Minimum initialization frames** | Set to `2` |
+| **Detect width** | Set to `1920` |
+| **Detect height** | Set to `1080` |
+
+Navigate to .
+
+| Field | Description |
+| ---------------------------------------------- | ------------------- |
+| **Objects to track** | Add `license_plate` |
+| **Object filters > License Plate > Threshold** | Set to `0.7` |
+
+Navigate to .
+
+| Field | Description |
+| -------------------- | --------------------------------------------------------------------- |
+| **Motion threshold** | Set to `30` |
+| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
+| **Improve contrast** | Set to off |
+
+Also add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
+
+Navigate to .
+
+| Field | Description |
+| -------------------- | -------------------------------------------------------- |
+| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
+
+Navigate to .
+
+| Field | Description |
+| -------------------- | ----------- |
+| **Enable snapshots** | Set to on |
+
+
+
+
```yaml
# LPR global configuration
lpr:
@@ -248,6 +446,9 @@ cameras:
- license_plate
```
+
+
+
With this setup:
- License plates are treated as normal objects in Frigate.
@@ -255,14 +456,69 @@ With this setup:
- Snapshots will have license plate bounding boxes on them.
- The `frigate/events` MQTT topic will publish tracked object updates.
- Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
### Using the Secondary LPR Pipeline (Without Frigate+)
-If you are not running a Frigate+ model, you can use Frigate’s built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
+If you are not running a Frigate+ model, you can use Frigate's built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
An example configuration for a dedicated LPR camera using the secondary pipeline:
+
+
+
+Navigate to and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available and the correct Docker image is used). Set **Detection threshold** to `0.7` (change if necessary).
+
+Navigate to for your dedicated LPR camera.
+
+| Field | Description |
+| --------------------- | -------------------------------------------------------------------------------- |
+| **Enable LPR** | Set to on |
+| **Enhancement level** | Set to `3` (optional, enhances the image before trying to recognize characters) |
+
+Navigate to and add your camera streams.
+
+Navigate to .
+
+| Field | Description |
+| --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
+| **Enable object detection** | Set to off to disable Frigate's standard object detection pipeline |
+| **Detect FPS** | Set to `5`. Increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU. |
+| **Detect width** | Set to `1920` (recommended value, but depends on your camera) |
+| **Detect height** | Set to `1080` (recommended value, but depends on your camera) |
+
+Navigate to .
+
+| Field | Description |
+| -------------------- | -------------------------------------------------------------------------------------- |
+| **Objects to track** | Set to an empty list, required when not using a Frigate+ model for dedicated LPR mode |
+
+Navigate to .
+
+| Field | Description |
+| -------------------- | --------------------------------------------------------------------- |
+| **Motion threshold** | Set to `30` |
+| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
+| **Improve contrast** | Set to off |
+
+Navigate to and add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
+
+Navigate to .
+
+| Field | Description |
+| -------------------- | -------------------------------------------------------- |
+| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
+
+Navigate to .
+
+| Field | Description |
+| ----------------------------------------- | --------------- |
+| **Detections config > Enable detections** | Set to on |
+| **Detections config > Retain > Default** | Set to `7` days |
+
+
+
+
```yaml
# LPR global configuration
lpr:
@@ -299,6 +555,9 @@ cameras:
default: 7
```
+
+
+
With this setup:
- The standard object detection pipeline is bypassed. Any detected license plates on dedicated LPR cameras are treated similarly to manual events in Frigate. You must **not** specify `license_plate` as an object to track.
@@ -333,7 +592,9 @@ By selecting the appropriate configuration, users can optimize their dedicated L
## FAQ
-### Why isn't my license plate being detected and recognized?
+### Detection and Recognition
+
+
Ensure that:
@@ -348,41 +609,70 @@ Recognized plates will show as object labels in the debug view and will appear i
If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.
-### Can I run LPR without detecting `car` or `motorcycle` objects?
+
-In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+Can I run LPR without detecting vehicle objects?}>
-### How can I improve detection accuracy?
+In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+
+
+
+
- Use high-quality cameras with good resolution.
- Adjust `detection_threshold` and `recognition_threshold` values.
- Define a `format` regex to filter out invalid detections.
-### Does LPR work at night?
+
+
+
Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.
-### Can I limit LPR to specific zones?
+
+
+
LPR, like other Frigate enrichments, runs at the camera level rather than the zone level. While you can't restrict LPR to specific zones directly, you can control when recognition runs by setting a `min_area` value to filter out smaller detections.
-### How can I match known plates with minor variations?
+
+
+
Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`.
-### How do I debug LPR issues?
+
+
+### Performance and Troubleshooting
+
+
Start with ["Why isn't my license plate being detected and recognized?"](#why-isnt-my-license-plate-being-detected-and-recognized). If you are still having issues, work through these steps.
1. Start with a simplified LPR config.
- Remove or comment out everything in your LPR config, including `min_area`, `min_plate_length`, `format`, `known_plates`, or `enhancement` values so that the only values left are `enabled` and `debug_save_plates`. This will run LPR with Frigate's default values.
- ```yaml
- lpr:
- enabled: true
- device: CPU
- debug_save_plates: true
- ```
+
+
+
+Navigate to .
+
+- Set **Enable LPR** to on
+- Set **Device** to `CPU`
+- Set **Save debug plates** to on
+
+
+
+
+```yaml
+lpr:
+ enabled: true
+ device: CPU
+ debug_save_plates: true
+```
+
+
+
2. Enable debug logs to see exactly what Frigate is doing.
- Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary. Restart Frigate after this change.
@@ -391,14 +681,14 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
logger:
default: info
logs:
- # highlight-next-line
+ # highlight-next-line
frigate.data_processing.common.license_plate: debug
```
3. Ensure your plates are being _detected_.
If you are using a Frigate+ or `license_plate` detecting model:
- - Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
+ - Watch the [Debug view](/usage/live#the-single-camera-view) to ensure that `license_plate` is being detected.
- View MQTT messages for `frigate/events` to verify detected plates.
- You may need to adjust your `min_score` and/or `threshold` for the `license_plate` object if your plates are not being detected.
@@ -407,28 +697,39 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
- You may need to adjust your `detection_threshold` if your plates are not being detected.
4. Ensure the characters on detected plates are being _recognized_.
+ - Check the **Plate recognition** inference time in Enrichment metrics (). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
- Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
- - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
+ - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
-### Will LPR slow down my system?
+
+
+
LPR's performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU or GPU for optimal results. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.
-### I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?
+
+
+I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects license_plate. Why is the YOLOv9 model running?}>
The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
-### It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?
+
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+
+
+This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
-### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
+
+
+
This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.
+
+
diff --git a/docs/docs/configuration/live.md b/docs/docs/configuration/live.md
index 8e7eff163b..6f32c9ec3e 100644
--- a/docs/docs/configuration/live.md
+++ b/docs/docs/configuration/live.md
@@ -3,11 +3,16 @@ id: live
title: Live View
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
+
Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
### Live View technologies
-Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be configured as shown in the [step by step guide](/guides/configuring_go2rtc).
+Frigate intelligently uses three different streaming technologies to display your camera streams on the dashboard and the single camera view, switching between available modes based on network bandwidth, player errors, or required features like two-way talk. The highest quality and fluency of the Live view requires the bundled `go2rtc` to be [configured](/configuration/go2rtc).
The jsmpeg live view will use more browser and client GPU resources. Using go2rtc is highly recommended and will provide a superior experience.
@@ -17,13 +22,19 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
| mse | native | native | yes (depends on audio codec) | yes | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured. |
| webrtc | native | native | yes (depends on audio codec) | yes | Requires extra configuration. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature. |
+:::info
+
+WebRTC may use an external STUN server for NAT traversal. MSE and HLS streaming do not require any internet access. See [Network Requirements](/frigate/network_requirements#webrtc-stun) for details.
+
+:::
+
### Camera Settings Recommendations
If you are using go2rtc, you should adjust the following settings in your camera's firmware for the best experience with Live view:
- Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
- Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
+- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://web.archive.org/web/20251213190836/https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
@@ -63,19 +74,36 @@ go2rtc:
### Setting Streams For Live UI
-You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the `live -> streams` list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
+You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
:::note
-Frigate's default dashboard ("All Cameras") will always use the first entry you've defined in `streams:` when playing live streams from your cameras.
+Frigate's default dashboard ("All Cameras") will always use the first entry you've defined in streams when playing live streams from your cameras.
:::
-Configure the `streams` option with a "friendly name" for your stream followed by the go2rtc stream name.
+Configure a "friendly name" for your stream followed by the go2rtc stream name. Using Frigate's internal version of go2rtc is required to use this feature. You cannot specify paths in the streams configuration, only go2rtc stream names.
-Using Frigate's internal version of go2rtc is required to use this feature. You cannot specify paths in the `streams` configuration, only go2rtc stream names.
+
+
+
+1. Navigate to and select your camera.
+2. Under **Live stream names**, click **Add stream** to add a new entry.
+3. In the **Stream name** field, enter a friendly name that will appear in the Live UI's stream dropdown (e.g., `Main Stream`).
+4. In the **go2rtc stream** field, open the dropdown and select the go2rtc stream this name should map to (e.g., `test_cam`). The dropdown lists every stream configured under `go2rtc.streams`. If the go2rtc stream hasn't been created yet, you can type the name and choose **Use "..."** to save a custom value.
+5. Repeat for each additional stream you want to expose (e.g., `Sub Stream` → `test_cam_sub`).
+6. Use the trash icon on a row to remove a stream, then **Save** the section.
+
+:::tip
+
+Configure your go2rtc streams first under so the dropdown is populated with valid options.
+
+:::
+
+
+
```yaml {3,6,8,25-29}
go2rtc:
@@ -109,6 +137,9 @@ cameras:
Special Stream: test_cam_another_sub
```
+
+
+
### WebRTC extra configuration:
WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
@@ -165,7 +196,7 @@ services:
:::
-See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
+See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-webrtc) for more information about this.
### Two way talk
@@ -185,7 +216,7 @@ To prevent go2rtc from blocking other applications from accessing your camera's
Frigate provides a dialog in the Camera Group Edit pane with several options for streaming on a camera group's dashboard. These settings are _per device_ and are saved in your device's local storage.
-- Stream selection using the `live -> streams` configuration option (see _Setting Streams For Live UI_ above)
+- Stream selection using the streams configuration option (see _Setting Streams For Live UI_ above)
- Streaming type:
- _No streaming_: Camera images will only update once per minute and no live streaming will occur.
- _Smart Streaming_ (default, recommended setting): Smart streaming will update your camera image once per minute when no detectable activity is occurring to conserve bandwidth and resources, since a static picture is the same as a streaming image with no motion or objects. When motion or objects are detected, the image seamlessly switches to a live stream.
@@ -203,19 +234,81 @@ Use a camera group if you want to change any of these settings from the defaults
:::
-### Disabling cameras
+### jsmpeg Stream Quality
-Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.
+The jsmpeg live view resolution and encoding quality can be adjusted globally or per camera. These settings only affect the jsmpeg player and do not apply when go2rtc is used for live view.
-:::note
+
+
-Disabling a camera via the Frigate UI or MQTT is temporary and does not persist through restarts of Frigate.
+Navigate to for global defaults, or and select a camera for per-camera overrides.
-:::
+| Field | Description |
+| ---------------- | --------------------------------------------------------------------------------------------------- |
+| **Live height** | Height in pixels for the jsmpeg live stream; must be less than or equal to the detect stream height |
+| **Live quality** | Encoding quality for the jsmpeg stream (1 = highest, 31 = lowest) |
-For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
+
+
-Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
+```yaml
+# Global defaults
+live:
+ height: 720
+ quality: 8
+
+# Per-camera override
+cameras:
+ front_door:
+ live:
+ height: 480
+ quality: 4
+```
+
+
+
+
+### Camera state
+
+Each camera has three possible states, surfaced as a status selector in **Settings → Global configuration → Camera management**:
+
+- **On**: streams are processed normally. Object detection, recording, and Live view are active.
+- **Off**: Frigate's ffmpeg processes are paused. Recording stops, object detection is paused, and the Live dashboard displays a blank image with a "Camera is off" message. The camera is still visible in the Live dashboard and its past review items, tracked objects, and historical footage remain accessible via the UI. The Off state persists across Frigate restarts via a `.runtime_state.json` file alongside `config.yml` (see [Runtime toggle persistence](#runtime-toggle-persistence)).
+- **Disabled**: the change is saved to your configuration file (`enabled: False`). The camera stops immediately, Frigate stops ffmpeg processes, and all live and historical UI elements for the camera are no longer visible but remains retained on disk. The camera is still listed in **Settings → Global configuration → Camera management** so it can be re-enabled. **A restart of Frigate is required to bring a disabled camera back to On.**
+
+#### Turning a camera on or off
+
+Turning a camera off is temporary and does not require a restart. The available controls are:
+
+- The power button in the single-camera Live view header
+- The right-click context menu on a camera tile on the Live dashboard
+- The Camera management settings pane (status set to **Off**)
+- The mobile settings drawer on the single-camera Live view (admin users only)
+- The [MQTT topic](/integrations/mqtt#frigatecamera_nameenabledset) `frigate//enabled/set` with payload `ON` or `OFF`
+- The Home Assistant integration via the [`camera.turn_on` / `camera.turn_off` actions](/integrations/home-assistant#camera-api)
+
+#### Disabling a camera
+
+Disabling a camera saves the change to your configuration file. Navigate to **Settings → Global configuration → Camera management** and set the camera's status to **Disabled**. Runtime processing stops immediately; the change persists across restarts.
+
+Re-enabling a disabled camera requires a restart of Frigate so that the ffmpeg processes and other camera-scoped resources can be initialized. The UI will prompt you to restart when you switch a disabled camera back to On.
+
+#### Restream behavior
+
+For both Off and Disabled cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
+
+#### Choosing Off versus Disabled
+
+If you want a camera's historical data (review items, tracked objects, footage) to stay accessible in the UI while you stop processing, set the camera to **Off**. If you want the camera fully removed from the Live dashboard, review filters, and other UI surfaces, set it to **Disabled**. The Disabled state still keeps the camera in Camera management so it can be re-enabled later; if you want to remove all traces of a camera including its configuration, delete it via Camera management instead.
+
+#### Runtime toggle persistence
+
+The Live view toggles for **camera on/off**, **detect**, **recordings**, **snapshots**, and **audio detection** (along with the equivalent MQTT `/set` topics) write the new state to `.runtime_state.json` next to your `config.yml`. The file is replayed on Frigate startup so your last-known toggle states survive a restart. Two interactions worth knowing:
+
+- **Settings UI saves win.** When you save a field through **Settings → Global configuration**, the matching entry is cleared from `.runtime_state.json` so the new value in your config file is the durable source.
+- **Switching profiles clears all runtime overrides.** Activating or deactivating a [profile](/configuration/profiles) is treated as a deliberate state change, so the file is wiped to avoid stale overrides replaying on top of the new profile.
+
+If you hand-edit `config.yml` while runtime overrides exist, the overrides will still replay on restart. Delete `.runtime_state.json` to reset to the YAML-defined defaults.
### Live player error messages
@@ -241,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
- **stalled**
- What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
- - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+ - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in .
- Possible console messages from the player code:
- `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
@@ -249,94 +342,155 @@ When your browser runs into problems playing back your camera streams, it will l
## Live view FAQ
-1. **Why don't I have audio in my Live view?**
+### Getting Live View Working
- You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
+
- Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
+You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
-2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?**
+If the audio controls don't appear in the UI at all, verify that the Live view is actually using your go2rtc stream. If your go2rtc stream names don't match your Frigate camera name, you must map them with the `live -> streams` config (see [Setting Streams For Live UI](#setting-streams-for-live-ui) above); otherwise the UI falls back to the video-only jsmpeg player.
- Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
+Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
- When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
+
- If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
+
- Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
- - Network issues (e.g., MSE or WebRTC network connection problems).
- - Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
- - Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
- - Browser compatibility problems (e.g., iOS Safari limitations with MSE).
+If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
- To view browser console logs:
- 1. Open the Frigate Live View in your browser.
- 2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
- 3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
- 4. Look for messages prefixed with the camera name.
+
- These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
- - Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera_settings_recommendations)).
- - Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
- - Test with a different stream via the UI dropdown (if `live -> streams` is configured).
- - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see (WebRTC Extra Configuration)(#webrtc-extra-configuration)).
- - If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
+
-3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
+The debug view plays the `detect` stream processed by Frigate itself, while the Live view plays your go2rtc stream directly in the browser. If the debug view works but the Live view doesn't, your browser usually can't decode what the camera is sending, most often H.265 video or an incompatible audio track.
- On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
+Work through the [go2rtc troubleshooting guide](/troubleshooting/go2rtc#live-view-is-black-buffering-or-stuck-in-low-bandwidth-mode) to isolate the problem. Two fixes resolve the majority of cases:
-4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?**
+1. Restream through go2rtc's FFmpeg module by prefixing your source with `ffmpeg:`, for example `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream`.
+2. If that doesn't help, transcode to compatible codecs: `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream#video=h264#audio=aac#hardware`.
- This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
+
-5. **How does "smart streaming" work?**
+
- Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
+For a full-resolution, low-latency live view in Home Assistant dashboards, use the [Advanced Camera Card](https://card.camera) with the [go2rtc live provider](https://card.camera/#/configuration/cameras/live-provider?id=go2rtc), which streams directly from Frigate's bundled go2rtc. This also supports audio and [two-way talk](#two-way-talk) on capable cameras. See the [Home Assistant integration docs](/integrations/home-assistant) for setup.
- This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
+
- Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
+### Streaming Behavior
- This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
+
-6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
+Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
- If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
+This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
-7. **My camera streams have lots of visual artifacts / distortion.**
+Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
- Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
+This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
-8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
+
- Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
+
- To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
+On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
- Example: Resolutions from two streams
- - Mismatched (may cause aspect ratio switching on the dashboard):
- - Live/go2rtc stream: 1920x1080 (16:9)
- - Detect stream: 640x352 (~1.82:1, not 16:9)
+
- - Matched (prevents switching):
- - Live/go2rtc stream: 1920x1080 (16:9)
- - Detect stream: 640x360 (16:9)
+
- You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
+Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
- ```yaml
- cameras:
- front_door:
- detect:
- width: 640
- height: 360 # set this to 360 instead of 352
- ffmpeg:
- inputs:
- - path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
- roles:
- - record
- - path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
- roles:
- - detect
- ```
+When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
+
+If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
+
+Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
+
+- Network issues (e.g., MSE or WebRTC network connection problems).
+- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
+- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
+- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
+
+To view browser console logs:
+
+1. Open the Frigate Live View in your browser.
+2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
+3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
+4. Look for messages prefixed with the camera name.
+
+These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
+
+- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
+- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
+- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
+- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
+- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
+
+
+
+
+
+A delay when a stream first starts is usually caused by your camera's I-frame (keyframe) interval. Playback cannot begin until a keyframe arrives, so an interval set higher than your camera's frame rate makes the stream take longer to start. Set the I-frame interval to match the frame rate (or "1x" on Reolink) per the [camera settings recommendations](#camera-settings-recommendations).
+
+A stream that starts on time but falls further behind live is buffering, which is usually the browser struggling to decode too many high-resolution streams at once. Select a lower-bandwidth substream for your dashboards (see [Setting Streams For Live UI](#setting-streams-for-live-ui)), reduce the number of streams open at once, or improve the network connection between your browser and Frigate. Frigate's player automatically speeds up playback to catch up to live after buffering, and falls back to low bandwidth mode if it stalls for too long. The _Reset_ option forces a fresh connection at the live edge.
+
+
+
+
+
+Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
+
+
+
+### Video Quality Issues
+
+
+
+This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
+
+
+
+
+
+Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
+
+
+
+
+
+Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
+
+To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
+
+Example: Resolutions from two streams
+
+- Mismatched (may cause aspect ratio switching on the dashboard):
+ - Live/go2rtc stream: 1920x1080 (16:9)
+ - Detect stream: 640x352 (~1.82:1, not 16:9)
+
+- Matched (prevents switching):
+ - Live/go2rtc stream: 1920x1080 (16:9)
+ - Detect stream: 640x360 (16:9)
+
+You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
+
+```yaml
+cameras:
+ front_door:
+ detect:
+ width: 640
+ height: 360 # set this to 360 instead of 352
+ ffmpeg:
+ inputs:
+ - path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
+ roles:
+ - record
+ - path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
+ roles:
+ - detect
+```
+
+The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
+
+
diff --git a/docs/docs/configuration/masks.md b/docs/docs/configuration/masks.md
index 0fcf366eda..50c9fffc31 100644
--- a/docs/docs/configuration/masks.md
+++ b/docs/docs/configuration/masks.md
@@ -3,11 +3,15 @@ id: masks
title: Masks
---
-Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with `required_zones` is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
## Motion masks
-Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the Debug feed (Settings --> Debug) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
+Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the [Debug view](/usage/live#the-single-camera-view) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
See [further clarification](#further-clarification) below on why you may not want to use a motion mask.
@@ -21,41 +25,79 @@ Object filter masks can be used to filter out stubborn false positives in fixed
## Which tool do I need?
-| What you're trying to do | Recommended tool | How it works |
-| ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Don't get alerts or recordings for activity in an area (e.g., the sidewalk in front of your house) | A [zone](zones.md) combined with `review.alerts.required_zones` (and/or `review.detections.required_zones`) | Frigate keeps detecting and tracking activity in the area, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
-| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
-| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
-| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
+| What you're trying to do | Recommended tool | How it works |
+| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Only get alerts/detections for activity in the areas you care about, ignoring activity elsewhere (e.g., alert when someone enters your yard, but not when they walk past on the sidewalk) | A [zone](zones.md) combined with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) | Frigate keeps detecting and tracking activity everywhere in the frame, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
+| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
+| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
+| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
## Using the mask creator
-To create a poly mask:
+
+
-1. Visit the Web UI
-2. Click/tap the gear icon and open "Settings"
-3. Select "Mask / zone editor"
-4. At the top right, select the camera you wish to create a mask or zone for
-5. Click the plus icon under the type of mask or zone you would like to create
-6. Click on the camera's latest image to create the points for a masked area. Click the first point again to close the polygon.
-7. When you've finished creating your mask, press Save.
+Navigate to and select a camera. Use the mask editor to draw motion masks and object filter masks directly on the camera feed. Each mask can be given a friendly name and toggled on or off.
+
+
+
Your config file will be updated with the relative coordinates of the mask/zone:
```yaml
motion:
- mask: "0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400"
+ mask:
+ # Motion mask name (required)
+ mask1:
+ # Optional: A friendly name for the mask
+ friendly_name: "Timestamp area"
+ # Optional: Whether this mask is active (default: true)
+ enabled: true
+ # Required: Coordinates polygon for the mask
+ coordinates: "0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456,0.700,0.424,0.701,0.311,0.507,0.294,0.453,0.347,0.451,0.400"
```
-Multiple masks can be listed in your config.
+Multiple motion masks can be listed in your config:
```yaml
motion:
mask:
- - 0.239,1.246,0.175,0.901,0.165,0.805,0.195,0.802
- - 0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456
+ mask1:
+ friendly_name: "Timestamp area"
+ enabled: true
+ coordinates: "0.239,1.246,0.175,0.901,0.165,0.805,0.195,0.802"
+ mask2:
+ friendly_name: "Tree area"
+ enabled: true
+ coordinates: "0.000,0.427,0.002,0.000,0.999,0.000,0.999,0.781,0.885,0.456"
```
+Object filter masks are configured under the object filters section for each object type:
+
+```yaml
+objects:
+ filters:
+ person:
+ mask:
+ person_filter1:
+ friendly_name: "Roof area"
+ enabled: true
+ coordinates: "0.000,0.000,1.000,0.000,1.000,0.400,0.000,0.400"
+ car:
+ mask:
+ car_filter1:
+ friendly_name: "Sidewalk area"
+ enabled: true
+ coordinates: "0.000,0.700,1.000,0.700,1.000,1.000,0.000,1.000"
+```
+
+
+
+
+## Enabling/Disabling Masks
+
+Both motion masks and object filter masks can be toggled on or off without removing them from the configuration. Disabled masks are completely ignored at runtime - they will not affect motion detection or object filtering. This is useful for temporarily disabling a mask during certain seasons or times of day without modifying the configuration.
+
### Further Clarification
This is a response to a [question posed on reddit](https://www.reddit.com/r/homeautomation/comments/ppxdve/replacing_my_doorbell_with_a_security_camera_a_6/hd876w4?utm_source=share&utm_medium=web2x&context=3):
@@ -97,10 +139,10 @@ That may be the case for you. Frigate will definitely work harder tracking peopl
## Common mistakes
**"I added a motion mask to ignore my driveway/sidewalk."**
-A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to `review.alerts.required_zones`. Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
+A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
**"I added an object filter mask because I don't care about cars in my yard."**
-Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with `required_zones`. If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
+Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones). If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
**"I masked everything except a thin strip on my stoop."**
-Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use `required_zones` for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.
+Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.
diff --git a/docs/docs/configuration/metrics.md b/docs/docs/configuration/metrics.md
index 662404205b..69db159cc5 100644
--- a/docs/docs/configuration/metrics.md
+++ b/docs/docs/configuration/metrics.md
@@ -3,19 +3,42 @@ id: metrics
title: Metrics
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
# Metrics
Frigate exposes Prometheus metrics at the `/api/metrics` endpoint that can be used to monitor the performance and health of your Frigate instance.
+## Enabling Telemetry
+
+Prometheus metrics are exposed via the telemetry configuration. Enable or configure telemetry to control metric availability.
+
+
+
+
+Navigate to to configure metrics and telemetry settings.
+
+
+
+
+Metrics are available at `/api/metrics` by default. No additional Frigate configuration is required to expose them.
+
+
+
+
## Available Metrics
### System Metrics
+
- `frigate_cpu_usage_percent{pid="", name="", process="", type="", cmdline=""}` - Process CPU usage percentage
- `frigate_mem_usage_percent{pid="", name="", process="", type="", cmdline=""}` - Process memory usage percentage
- `frigate_gpu_usage_percent{gpu_name=""}` - GPU utilization percentage
- `frigate_gpu_mem_usage_percent{gpu_name=""}` - GPU memory usage percentage
### Camera Metrics
+
- `frigate_camera_fps{camera_name=""}` - Frames per second being consumed from your camera
- `frigate_detection_fps{camera_name=""}` - Number of times detection is run per second
- `frigate_process_fps{camera_name=""}` - Frames per second being processed
@@ -25,21 +48,27 @@ Frigate exposes Prometheus metrics at the `/api/metrics` endpoint that can be us
- `frigate_audio_rms{camera_name=""}` - Audio RMS for camera
### Detector Metrics
+
- `frigate_detector_inference_speed_seconds{name=""}` - Time spent running object detection in seconds
- `frigate_detection_start{name=""}` - Detector start time (unix timestamp)
### Storage Metrics
+
- `frigate_storage_free_bytes{storage=""}` - Storage free bytes
- `frigate_storage_total_bytes{storage=""}` - Storage total bytes
- `frigate_storage_used_bytes{storage=""}` - Storage used bytes
- `frigate_storage_mount_type{mount_type="", storage=""}` - Storage mount type info
+These gauges report the operating system's figures for the whole filesystem (the same numbers as `df`), not Frigate's own recording footprint. For how this differs from the recordings usage shown in the UI, see [Understanding storage usage](/configuration/record#understanding-storage-usage).
+
### Service Metrics
+
- `frigate_service_uptime_seconds` - Uptime in seconds
- `frigate_service_last_updated_timestamp` - Stats recorded time (unix timestamp)
- `frigate_device_temperature{device=""}` - Device Temperature
### Event Metrics
+
- `frigate_camera_events{camera="", label=""}` - Count of camera events since exporter started
## Configuring Prometheus
@@ -48,10 +77,10 @@ To scrape metrics from Frigate, add the following to your Prometheus configurati
```yaml
scrape_configs:
- - job_name: 'frigate'
- metrics_path: '/api/metrics'
+ - job_name: "frigate"
+ metrics_path: "/api/metrics"
static_configs:
- - targets: ['frigate:5000']
+ - targets: ["frigate:5000"]
scrape_interval: 15s
```
diff --git a/docs/docs/configuration/motion_detection.md b/docs/docs/configuration/motion_detection.md
index c22491fd06..a7928954e2 100644
--- a/docs/docs/configuration/motion_detection.md
+++ b/docs/docs/configuration/motion_detection.md
@@ -3,11 +3,15 @@ id: motion_detection
title: Motion Detection
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
# Tuning Motion Detection
Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.
-Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the debug viewer.
+Once motion is detected, it tries to group up nearby areas of motion together in hopes of identifying a rectangle in the image that will capture the area worth inspecting. These are the red "motion boxes" you see in the [debug viewer](/usage/live#the-single-camera-view).
## The Goal
@@ -21,7 +25,7 @@ First, mask areas with regular motion not caused by the objects you want to dete
## Prepare For Testing
-The easiest way to tune motion detection is to use the Frigate UI under Settings > Motion Tuner. This screen allows the changing of motion detection values live to easily see the immediate effect on what is detected as motion.
+The recommended way to tune motion detection is to use the built-in Motion Tuner. Navigate to and select the camera you want to tune. This screen lets you adjust motion detection values live and immediately see the effect on what is detected as motion, making it the fastest way to find optimal settings for each camera.
## Tuning Motion Detection During The Day
@@ -37,8 +41,21 @@ Remember that motion detection is just used to determine when object detection s
The threshold value dictates how much of a change in a pixels luminance is required to be considered motion.
+
+
+
+Navigate to to set the threshold globally.
+
+To override for a specific camera, navigate to and select the camera, or use the to adjust it live.
+
+| Field | Description |
+| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Motion threshold** | The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive. The value should be between 1 and 255. (default: 30) |
+
+
+
+
```yaml
-# default threshold value
motion:
# Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
# Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
@@ -46,14 +63,30 @@ motion:
threshold: 30
```
-Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dogs blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
+
+
+
+Lower values mean motion detection is more sensitive to changes in color, making it more likely for example to detect motion when a brown dog blends in with a brown fence or a person wearing a red shirt blends in with a red car. If the threshold is too low however, it may detect things like grass blowing in the wind, shadows, etc. to be detected as motion.
Watching the motion boxes in the debug view, increase the threshold until you only see motion that is visible to the eye. Once this is done, it is important to test and ensure that desired motion is still detected.
### Contour Area
+
+
+
+Navigate to to set the contour area globally.
+
+To override for a specific camera, navigate to and select the camera, or use the to adjust it live.
+
+| Field | Description |
+| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Contour area** | Minimum size in pixels in the resized motion image that counts as motion. Increasing this value will prevent smaller areas of motion from being detected. Decreasing will make motion detection more sensitive to smaller moving objects. As a rule of thumb: 10 = high sensitivity, 30 = medium sensitivity, 50 = low sensitivity. (default: 10) |
+
+
+
+
```yaml
-# default contour_area value
motion:
# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: shown below)
# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
@@ -65,6 +98,9 @@ motion:
contour_area: 10
```
+
+
+
Once the threshold calculation is run, the pixels that have changed are grouped together. The contour area value is used to decide which groups of changed pixels qualify as motion. Smaller values are more sensitive meaning people that are far away, small animals, etc. are more likely to be detected as motion, but it also means that small changes in shadows, leaves, etc. are detected as motion. Higher values are less sensitive meaning these things won't be detected as motion but with the risk that desired motion won't be detected until closer to the camera.
Watching the motion boxes in the debug view, adjust the contour area until there are no motion boxes smaller than the smallest you'd expect frigate to detect something moving.
@@ -81,27 +117,87 @@ However, if the preferred day settings do not work well at night it is recommend
## Tuning For Large Changes In Motion
+### Lightning Threshold
+
+
+
+
+Navigate to and expand the advanced fields to find the lightning threshold setting.
+
+To override for a specific camera, navigate to and select the camera.
+
+| Field | Description |
+| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Lightning threshold** | The percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera. (default: 0.8) |
+
+
+
+
```yaml
-# default lightning_threshold:
motion:
- # Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection
- # needs to recalibrate. (default: shown below)
- # Increasing this value will make motion detection more likely to consider lightning or ir mode changes as valid motion.
- # Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching
- # a doorbell camera.
+ # Optional: The percentage of the image used to detect lightning or
+ # other substantial changes where motion detection needs to
+ # recalibrate. (default: shown below)
+ # Increasing this value will make motion detection more likely
+ # to consider lightning or IR mode changes as valid motion.
+ # Decreasing this value will make motion detection more likely
+ # to ignore large amounts of motion such as a person
+ # approaching a doorbell camera.
lightning_threshold: 0.8
```
+
+
+
+Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. `lightning_threshold` defines the percentage of the image used to detect these substantial changes. Increasing this value makes motion detection more likely to treat large changes (like IR mode switches) as valid motion. Decreasing it makes motion detection more likely to ignore large amounts of motion, such as a person approaching a doorbell camera.
+
+Note that `lightning_threshold` does **not** stop motion-based recordings from being saved. It only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.
+
:::warning
-Some cameras like doorbell cameras may have missed detections when someone walks directly in front of the camera and the lightning_threshold causes motion detection to be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
+Some cameras, like doorbell cameras, may have missed detections when someone walks directly in front of the camera and the `lightning_threshold` causes motion detection to recalibrate. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
:::
-:::note
+### Skip Motion On Large Scene Changes
-Lightning threshold does not stop motion based recordings from being saved.
+
+
+
+Navigate to and expand the advanced fields to find the skip motion threshold setting.
+
+To override for a specific camera, navigate to and select the camera.
+
+| Field | Description |
+| ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Skip motion threshold** | Fraction of the frame that must change in a single update before Frigate will completely ignore any motion in that frame. Values range between 0.0 and 1.0; leave unset (null) to disable. For example, setting this to 0.7 causes Frigate to skip reporting motion boxes when more than 70% of the image appears to change (e.g. during lightning storms, IR/color mode switches, or other sudden lighting events). |
+
+
+
+
+```yaml
+motion:
+ # Optional: Fraction of the frame that must change in a single update
+ # before Frigate will completely ignore any motion in that frame.
+ # Values range between 0.0 and 1.0, leave unset (null) to disable.
+ # Setting this to 0.7 would cause Frigate to **skip** reporting
+ # motion boxes when more than 70% of the image appears to change
+ # (e.g. during lightning storms, IR/color mode switches, or other
+ # sudden lighting events).
+ skip_motion_threshold: 0.7
+```
+
+
+
+
+This option is handy when you want to prevent large transient changes from triggering recordings or object detection. It differs from `lightning_threshold` because it completely suppresses motion instead of just forcing a recalibration.
+
+:::warning
+
+When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise. They typically only take up a few megabytes and are quick to scan in the timeline UI.
:::
-Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. This is done via the `lightning_threshold` configuration. It is defined as the percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera.
+## Reviewing Detected Motion
+
+To review what the detector picked up, or to search past recordings for motion in a specific region, see [Reviewing Motion](/usage/review#reviewing-motion) on the Review page.
diff --git a/docs/docs/configuration/notifications.md b/docs/docs/configuration/notifications.md
index b5e1600e4b..67b4f2374d 100644
--- a/docs/docs/configuration/notifications.md
+++ b/docs/docs/configuration/notifications.md
@@ -3,30 +3,53 @@ id: notifications
title: Notifications
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
+
# Notifications
Frigate offers native notifications using the [WebPush Protocol](https://web.dev/articles/push-notifications-web-push-protocol) which uses the [VAPID spec](https://tools.ietf.org/html/draft-thomson-webpush-vapid) to deliver notifications to web apps using encryption.
+:::info
+
+Push notifications require internet access from the Frigate server to the browser vendor's push service (e.g., Google FCM, Mozilla autopush). See [Network Requirements](/frigate/network_requirements#push-notifications) for details.
+
+:::
+
## Setting up Notifications
In order to use notifications the following requirements must be met:
-- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
+- Frigate must be accessed via a secure `https` connection while signed in as a Frigate user ([see the authorization docs](/configuration/authentication)).
- A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
- In order for notifications to be usable externally, Frigate must be accessible externally.
- For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
### Configuration
-To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
+Enable notifications and fill out the required fields.
-Optionally, you can change the default cooldown period for notifications through the `cooldown` parameter in your config file. This parameter can also be overridden at the camera level.
+Optionally, change the default cooldown period for notifications. The cooldown can also be overridden at the camera level.
Notifications will be prevented if either:
- The global cooldown period hasn't elapsed since any camera's last notification
- The camera-specific cooldown period hasn't elapsed for the specific camera
+#### Global notifications
+
+
+
+
+1. Navigate to .
+ - Set **Email** to your email address
+ - Enable notifications for the desired cameras
+
+
+
+
```yaml
notifications:
enabled: True
@@ -34,6 +57,21 @@ notifications:
cooldown: 10 # wait 10 seconds before sending another notification from any camera
```
+
+
+
+#### Per-camera notifications
+
+
+
+
+1. Navigate to and select the desired camera.
+ - Set **Enable notifications** to on
+ - Set **Cooldown period** to the desired number of seconds to wait before sending another notification from this camera (e.g. `30`)
+
+
+
+
```yaml
cameras:
doorbell:
@@ -43,9 +81,18 @@ cameras:
cooldown: 30 # wait 30 seconds before sending another notification from the doorbell camera
```
+
+
+
### Registration
-Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+Once notifications are enabled, press the `Register This Device` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+
+:::warning
+
+Each registration is attached to the Frigate user account you are signed in as, so you must register over a secure connection to the authenticated port (`8971`). Reverse proxies and tunnels should point at port `8971`.
+
+:::
## Supported Notifications
@@ -64,3 +111,62 @@ Different platforms handle notifications differently, some settings changes may
### Android
Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
+
+## Notifications FAQ
+
+
+
+Push notifications involve Frigate, your browser, and your browser vendor's push service, so it helps to work from the server outward.
+
+1. Enable debug logs for the push client by adding `frigate.comms.webpush: debug` to your `logger` configuration. Restart Frigate after this change.
+
+ ```yaml
+ logger:
+ default: info
+ logs:
+ # highlight-next-line
+ frigate.comms.webpush: debug
+ ```
+
+ These logs show exactly where a notification stopped, including:
+ - `Email must be provided for push notifications to be sent` means the global `email` field is empty and nothing will ever be sent.
+ - `Sending test notification` and `Sending push notification for , review ID ` mean Frigate handed the message off to the push service.
+ - `Skipping notification for - in global cooldown period` (or `camera-specific cooldown period`) means your [cooldown](#configuration) values suppressed it.
+ - `Notifications for are currently suspended` means notifications were suspended from or MQTT.
+ - `Notification endpoint expired for , received 410` means that device's subscription is no longer valid and it must be re-registered.
+ - `Failed to send notification to :: ` means the push service rejected the message. A `401` or `403` usually points at a VAPID or `email` problem, and a `5xx` is a problem on the push service's end.
+ - If you see no messages at all when an alert occurs, the notification was never queued. Confirm an actual **alert** was created (notifications are not sent for detections), and that notifications are enabled both globally and for that camera.
+
+2. Verify the basics that most reports come down to:
+ - Frigate must be reached over `https` with a certificate your device trusts. Browsers silently refuse to register a service worker otherwise, and a self-signed certificate that is not installed as trusted on the device will fail.
+ - On iOS, notifications only work when Frigate has been installed to the Home Screen via **Share > Add to Home Screen** and opened from that icon. Safari and Chrome tabs cannot receive web push on iOS.
+ - Each device must be registered individually, and Frigate must be restarted after registering before anything can be sent, including test notifications.
+ - The Frigate server needs outbound internet access to the browser vendor's push service. See [Network Requirements](/frigate/network_requirements#push-notifications).
+
+3. Test from the UI. Use the `Send a test notification` button in . If the log shows `Sending test notification` but nothing arrives on the device, the problem is between the push service and your device rather than in Frigate.
+
+4. Check the browser side on the device that is not receiving notifications:
+ - Confirm the site's notification permission is set to **Allow** in your browser or OS settings, and that a focus/do not disturb mode is not hiding them.
+ - In desktop browsers, open Developer Tools > Application > Service Workers and confirm `notifications-worker.js` is registered and activated. Unregistering it and registering the device again will rebuild a broken subscription.
+ - Check the browser console and your reverse proxy logs for failures loading `/notifications-worker.js` or errors on `/api/notifications/register`.
+
+
+
+
+
+Push subscriptions are issued by the browser vendor and can be revoked, most often after a browser update, after clearing site data, or when a device has been offline for an extended period. When this happens the device still appears registered in Frigate, but the push service rejects the message. The debug logs will show `Notification endpoint expired` with a `404` or `410` status.
+
+Unregister and re-register the affected device from , then restart Frigate.
+
+
+
+
+
+Work through these in order:
+
+- Notifications are only sent for **alerts**. If the camera is producing detections instead, adjust the camera's `review > alerts > labels` so the objects you care about are classified as alerts.
+- Confirm notifications are enabled for that camera in .
+- Check the camera's `cooldown` value, and remember that the global cooldown applies across all cameras. A busy camera can consume the global cooldown and suppress a quieter one.
+- If [authentication](/configuration/authentication) is enabled with roles, users only receive notifications for the cameras their role grants access to.
+
+
diff --git a/docs/docs/configuration/object_detectors.md b/docs/docs/configuration/object_detectors.md
index 4a0f014d49..602ebfcb85 100644
--- a/docs/docs/configuration/object_detectors.md
+++ b/docs/docs/configuration/object_detectors.md
@@ -4,11 +4,19 @@ title: Object Detectors
---
import CommunityBadge from '@site/src/components/CommunityBadge';
+import ConfigTabs from '@site/src/components/ConfigTabs';
+import TabItem from '@theme/TabItem';
+import NavPath from '@site/src/components/NavPath';
+import ModelConfigDropdown from '@site/src/components/ModelConfigDropdown';
+import objectDetectorsModels from '@site/data/object_detectors_models.yaml';
-# Supported Hardware
+### Supported hardware
+
+Object detection is what allows Frigate to identify _what_ is in your camera's view (people, cars, animals, and more) rather than just reacting to pixel changes. When Frigate's motion detection finds activity in a frame, that region is sent to an **object detector**, which returns the objects it recognizes along with their location and a confidence score. These detections are what drive tracked objects, alerts, detections, and notifications.
+
+Object detection is computationally intensive, so Frigate is designed to run it on a dedicated AI accelerator or GPU rather than the CPU. A **detector** is the specific hardware-and-model backend Frigate uses to run inference. Choosing a detector that matches your hardware is one of the most important steps in getting good performance, and the right choice depends on what device Frigate is running on.
:::info
-
Frigate supports multiple different detectors that work on different types of hardware:
**Most Hardware**
@@ -16,7 +24,6 @@ Frigate supports multiple different detectors that work on different types of ha
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
**AMD**
@@ -49,6 +56,10 @@ Frigate supports multiple different detectors that work on different types of ha
- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
+**AXERA**
+
+- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
+
**For Testing**
- [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
@@ -63,6 +74,22 @@ This does not affect using hardware for accelerating other tasks such as [semant
:::
+### Choosing a model size
+
+Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
+
+**Resolution (320x320 vs 640x640):** Frigate is optimized for `320x320` models, and `320x320` is the best choice for the vast majority of setups. Frigate is specifically designed to compensate for the smaller model by cropping a region of motion from the full frame and zooming into it before running detection, so a `320x320` model is actually _better_ at small and distant objects, not worse. A `640x640` model is slower and uses more resources, and its main benefit is fitting more objects into a single inference when many objects are spread across a large area. Recent versions of Frigate have improved support for `640x640` models, but `320x320` remains the recommended starting point for nearly all setups.
+
+**Variant size (tiny/small/medium):** Larger variants are gradually more accurate but slower. Whether the difference is noticeable depends on your specific cameras and scenes. A good rule of thumb is to use the largest model your hardware can run without skipping detections, which you can monitor on the page in the UI. Better accuracy only helps if your detector keeps up with the detection load across all cameras.
+
+**Acceptable inference time depends on your hardware.** Inference time alone does not tell the whole story, because different hardware has different capacity. A GPU can run multiple instances of the same model concurrently, so an inference time around 30ms can still keep up with several cameras. A Google Coral runs only a single instance of the model, so it needs a much lower inference time (around 10ms) to keep up.
+
+:::tip
+
+The best detection accuracy comes from a model trained on images that look like what Frigate actually sees: security camera footage cropped to regions of interest. You can train or fine-tune your own model on images like this and run it as a custom model (see the per-detector sections below), but [Frigate+](/plus) makes this much easier by handling the training for you on images submitted from your own cameras. For YOLOv9, the `s` (small) variant at `320x320` resolution is a good place to start.
+
+:::
+
# Officially Supported Detectors
Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
@@ -81,6 +108,14 @@ See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edg
### Single USB Coral
+
+
+
+Navigate to and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
+
+
+
+
```yaml
detectors:
coral:
@@ -88,8 +123,19 @@ detectors:
device: usb
```
+
+
+
### Multiple USB Corals
+
+
+
+Navigate to and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `usb:0` and `usb:1` as the device for each.
+
+
+
+
```yaml
detectors:
coral1:
@@ -100,10 +146,21 @@ detectors:
device: usb:1
```
+
+
+
### Native Coral (Dev Board)
_warning: may have [compatibility issues](https://github.com/blakeblackshear/frigate/issues/1706) after `v0.9.x`_
+
+
+
+Navigate to and select **EdgeTPU** from the detector type dropdown and click **Add**, then leave the device field empty.
+
+
+
+
```yaml
detectors:
coral:
@@ -111,8 +168,19 @@ detectors:
device: ""
```
+
+
+
### Single PCIE/M.2 Coral
+
+
+
+Navigate to and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `pci`.
+
+
+
+
```yaml
detectors:
coral:
@@ -120,8 +188,19 @@ detectors:
device: pci
```
+
+
+
### Multiple PCIE/M.2 Corals
+
+
+
+Navigate to and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `pci:0` and `pci:1` as the device for each.
+
+
+
+
```yaml
detectors:
coral1:
@@ -132,8 +211,19 @@ detectors:
device: pci:1
```
+
+
+
### Mixing Corals
+
+
+
+Navigate to and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors with different device types (e.g., `usb` and `pci`).
+
+
+
+
```yaml
detectors:
coral_usb:
@@ -144,49 +234,12 @@ detectors:
device: pci
```
-### EdgeTPU Supported Models
+
+
-| Model | Notes |
-| ----------------------- | ------------------------------------------- |
-| [Mobiledet](#mobiledet) | Default model |
-| [YOLOv9](#yolov9) | More accurate but slower than default model |
+### Configuration {#configuration-edgetpu}
-#### Mobiledet
-
-A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
-
-#### YOLOv9
-
-YOLOv9 models that are compiled for TensorFlow Lite and properly quantized are supported, but not included by default. [Instructions](#yolov9-for-google-coral-support) for downloading a model with support for the Google Coral.
-
-:::tip
-
-**Frigate+ Users:** Follow the [instructions](/integrations/plus#use-models) to set a model ID in your config file.
-
-:::
-
-
- YOLOv9 Setup & Config
-
-After placing the downloaded files for the tflite model and labels in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
- coral:
- type: edgetpu
- device: usb
-
-model:
- model_type: yolo-generic
- width: 320 # <--- should match the imgsize of the model, typically 320
- height: 320 # <--- should match the imgsize of the model, typically 320
- path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
- labelmap_path: /config/labels-coco17.txt
-```
-
-Note that due to hardware limitations of the Coral, the labelmap is a subset of the COCO labels and includes only 17 object classes.
-
-
+
---
@@ -194,97 +247,20 @@ Note that due to hardware limitations of the Coral, the labelmap is a subset of
This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified.
-See the [installation docs](../frigate/installation.md#hailo-8l) for information on configuring the Hailo hardware.
+See the [installation docs](../frigate/installation.md#hailo-8) for information on configuring the Hailo hardware.
-### Configuration
+:::info
+
+If no custom model is provided, the Hailo detector downloads a default model from the Hailo Model Zoo on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
+
+:::
+
+### Configuration {#configuration-hailo}
When configuring the Hailo detector, you have two options to specify the model: a local **path** or a **URL**.
If both are provided, the detector will first check for the model at the given local path. If the file is not found, it will download the model from the specified URL. The model file is cached under `/config/model_cache/hailo`.
-#### YOLO
-
-Use this configuration for YOLO-based models. When no custom model path or URL is provided, the detector automatically downloads the default model based on the detected hardware:
-
-- **Hailo-8 hardware:** Uses **YOLOv6n** (default: `yolov6n.hef`)
-- **Hailo-8L hardware:** Uses **YOLOv6n** (default: `yolov6n.hef`)
-
-```yaml
-detectors:
- hailo:
- type: hailo8l
- device: PCIe
-
-model:
- width: 320
- height: 320
- input_tensor: nhwc
- input_pixel_format: rgb
- input_dtype: int
- model_type: yolo-generic
- labelmap_path: /labelmap/coco-80.txt
-
- # The detector automatically selects the default model based on your hardware:
- # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
- # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
- #
- # Optionally, you can specify a local model path to override the default.
- # If a local path is provided and the file exists, it will be used instead of downloading.
- # Example:
- # path: /config/model_cache/hailo/yolov6n.hef
- #
- # You can also override using a custom URL:
- # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
- # just make sure to give it the write configuration based on the model
-```
-
-#### SSD
-
-For SSD-based models, provide either a model path or URL to your compiled SSD model. The integration will first check the local path before downloading if necessary.
-
-```yaml
-detectors:
- hailo:
- type: hailo8l
- device: PCIe
-
-model:
- width: 300
- height: 300
- input_tensor: nhwc
- input_pixel_format: rgb
- model_type: ssd
- # Specify the local model path (if available) or URL for SSD MobileNet v1.
- # Example with a local path:
- # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
- #
- # Or override using a custom URL:
- # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
-```
-
-#### Custom Models
-
-The Hailo detector supports all YOLO models compiled for Hailo hardware that include post-processing. You can specify a custom URL or a local path to download or use your model directly. If both are provided, the detector checks the local path first.
-
-```yaml
-detectors:
- hailo:
- type: hailo8l
- device: PCIe
-
-model:
- width: 640
- height: 640
- input_tensor: nhwc
- input_pixel_format: rgb
- input_dtype: int
- model_type: yolo-generic
- labelmap_path: /labelmap/coco-80.txt
- # Optional: Specify a local model path.
- # path: /config/model_cache/hailo/custom_model.hef
- #
- # Alternatively, or as a fallback, provide a custom URL:
- # path: https://custom-model-url.com/path/to/model.hef
-```
+
For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo
@@ -321,268 +297,42 @@ detectors:
:::
-### OpenVINO Supported Models
+### Intel NPU host requirements {#intel-npu-requirements}
-| Model | GPU | NPU | Notes |
-| ------------------------------------- | --- | --- | ------------------------------------------------------------ |
-| [YOLOv9](#yolo-v3-v4-v7-v9) | ✅ | ✅ | Recommended for GPU & NPU |
-| [RF-DETR](#rf-detr) | ✅ | ✅ | Requires XE iGPU or Arc |
-| [YOLO-NAS](#yolo-nas) | ✅ | ✅ | |
-| [MobileNet v2](#ssdlite-mobilenet-v2) | ✅ | ✅ | Fast and lightweight model, less accurate than larger models |
-| [YOLOX](#yolox) | ✅ | ? | |
-| [D-FINE / DEIMv2](#d-fine--deimv2) | ❌ | ❌ | |
+The NPU firmware is loaded by the host kernel and is not part of the Frigate image. Everything else the NPU needs is bundled in the container, so host NPU libraries should never be mounted in.
-#### SSDLite MobileNet v2
+Frigate bundles a specific version of Intel's [linux-npu-driver](https://github.com/intel/linux-npu-driver/releases), and the host firmware must come from that release or a newer one. Firmware older than the bundled driver may fail with `MAPPED_INFERENCE_VERSION is NOT compatible with the ELF`, where `Expected` is the version the firmware supports and `received` is the version the bundled compiler produced. Distributions often package older firmware than the driver Frigate ships, so check the build date on the host with `sudo dmesg | grep -i vpu` and update it there if needed.
-An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
+Intel NPUs cannot be used under Home Assistant OS, which does not include the NPU firmware.
-
- MobileNet v2 Config
+### Configuration {#configuration-openvino}
-Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
+
-```yaml
-detectors:
- ov:
- type: openvino
- device: GPU # Or NPU
-
-model:
- width: 300
- height: 300
- input_tensor: nhwc
- input_pixel_format: bgr
- path: /openvino-model/ssdlite_mobilenet_v2.xml
- labelmap_path: /openvino-model/coco_91cl_bkgr.txt
-```
-
-
-
-#### YOLOX
-
-This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models.
-
-#### YOLO-NAS
-
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
-
-
- YOLO-NAS Setup & Config
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
- ov:
- type: openvino
- device: GPU
-
-model:
- model_type: yolonas
- width: 320 # <--- should match whatever was set in notebook
- height: 320 # <--- should match whatever was set in notebook
- input_tensor: nchw
- input_pixel_format: bgr
- path: /config/yolo_nas_s.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-
-
-#### YOLO (v3, v4, v7, v9)
-
-YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
-
-:::tip
-
-The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well.
-
-:::
-
-
- YOLOv Setup & Config
-
-:::warning
-
-If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
-
-:::
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
- ov:
- type: openvino
- device: GPU # or NPU
-
-model:
- model_type: yolo-generic
- width: 320 # <--- should match the imgsize set during model export
- height: 320 # <--- should match the imgsize set during model export
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/yolo.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-
-
-#### RF-DETR
-
-[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
-
-:::warning
-
-Due to the size and complexity of the RF-DETR model, it is only recommended to be run with discrete Arc Graphics Cards.
-
-:::
-
-
- RF-DETR Setup & Config
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
- ov:
- type: openvino
- device: GPU
-
-model:
- model_type: rfdetr
- width: 320
- height: 320
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/rfdetr.onnx
-```
-
-
-
-#### D-FINE / DEIMv2
-
-[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
-
-:::warning
-
-Currently D-FINE / DEIMv2 models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
-
-:::
-
-
- D-FINE Setup & Config
-
-After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
-
-```yaml
-detectors:
- ov:
- type: openvino
- device: CPU
-
-model:
- model_type: dfine
- width: 640
- height: 640
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/dfine-s.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-
-
-
- DEIMv2 Setup & Config
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
- ov:
- type: openvino
- device: CPU
-
-model:
- model_type: dfine
- width: 640
- height: 640
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/deimv2_hgnetv2_n.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-
+---
## Apple Silicon detector
The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
-### Setup
+### Setup {#setup-apple-silicon}
1. Setup the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) and run the client
2. Configure the detector in Frigate and startup Frigate
-### Configuration
+### Configuration {#configuration-apple-silicon}
Using the detector config below will connect to the client:
-```yaml
-detectors:
- apple-silicon:
- type: zmq
- endpoint: tcp://host.docker.internal:5555
-```
-
-### Apple Silicon Supported Models
-
-There is no default model provided, the following formats are supported:
-
-#### YOLO (v3, v4, v7, v9)
-
-YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
-
-:::tip
-
-The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well. See [the models section](#downloading-yolo-models) for more information on downloading YOLO models for use in Frigate.
-
-:::
-
-When Frigate is started with the following config it will connect to the detector client and transfer the model automatically:
-
-```yaml
-detectors:
- apple-silicon:
- type: zmq
- endpoint: tcp://host.docker.internal:5555
-
-model:
- model_type: yolo-generic
- width: 320 # <--- should match the imgsize set during model export
- height: 320 # <--- should match the imgsize set during model export
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/yolo.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
+
Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
## AMD/ROCm GPU detector
-### Setup
+### Setup {#setup-rocm}
-Support for AMD GPUs is provided using the [ONNX detector](#ONNX). In order to utilize the AMD GPU for object detection use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
+Support for AMD GPUs is provided using the [ONNX detector](#onnx). In order to utilize the AMD GPU for object detection use a frigate docker image with `-rocm` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-rocm`.
### Docker settings for GPU access
@@ -658,7 +408,7 @@ We unset the `HSA_OVERRIDE_GFX_VERSION` to prevent an existing override from mes
$ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'
```
-### ROCm Supported Models
+### Configuration {#configuration-rocm}
:::tip
@@ -671,11 +421,13 @@ The AMD GPU kernel is known problematic especially when converting models to mxr
:::
-See [ONNX supported models](#supported-models) for supported models, there are some caveats:
+See [ONNX supported models](#onnx) for supported models, there are some caveats:
- D-FINE / DEIMv2 models are not supported
- YOLO-NAS models are known to not run well on integrated GPUs
+
+
## ONNX
ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
@@ -710,192 +462,11 @@ detectors:
:::
-### ONNX Supported Models
+### Configuration {#configuration-onnx}
-| Model | Nvidia GPU | AMD GPU | Notes |
-| ----------------------------- | ---------- | ------- | --------------------------------------------------- |
-| [YOLOv9](#yolo-v3-v4-v7-v9-2) | ✅ | ✅ | Supports CUDA Graphs for optimal Nvidia performance |
-| [RF-DETR](#rf-detr) | ✅ | ❌ | Supports CUDA Graphs for optimal Nvidia performance |
-| [YOLO-NAS](#yolo-nas-1) | ⚠️ | ⚠️ | Not supported by CUDA Graphs |
-| [YOLOX](#yolox-1) | ✅ | ✅ | Supports CUDA Graphs for optimal Nvidia performance |
-| [D-FINE / DEIMv2](#d-fine--deimv2-1) | ⚠️ | ❌ | Not supported by CUDA Graphs |
+
-There is no default model provided, the following formats are supported:
-
-#### YOLO-NAS
-
-[YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
-
-
- YOLO-NAS Setup & Config
-
-:::warning
-
-If you are using a Frigate+ YOLO-NAS model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
-
-:::
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
- onnx:
- type: onnx
-
-model:
- model_type: yolonas
- width: 320 # <--- should match whatever was set in notebook
- height: 320 # <--- should match whatever was set in notebook
- input_pixel_format: bgr
- input_tensor: nchw
- path: /config/yolo_nas_s.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-
-
-#### YOLO (v3, v4, v7, v9)
-
-YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
-
-:::tip
-
-The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well. See [the models section](#downloading-yolo-models) for more information on downloading YOLO models for use in Frigate.
-
-:::
-
-
- YOLOv Setup & Config
-
-:::warning
-
-If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
-
-:::
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
- onnx:
- type: onnx
-
-model:
- model_type: yolo-generic
- width: 320 # <--- should match the imgsize set during model export
- height: 320 # <--- should match the imgsize set during model export
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/yolo.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-#### YOLOx
-
-[YOLOx](https://github.com/Megvii-BaseDetection/YOLOX) models are supported, but not included by default. See [the models section](#downloading-yolo-models) for more information on downloading the YOLOx model for use in Frigate.
-
-
- YOLOx Setup & Config
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
- onnx:
- type: onnx
-
-model:
- model_type: yolox
- width: 416 # <--- should match the imgsize set during model export
- height: 416 # <--- should match the imgsize set during model export
- input_tensor: nchw
- input_dtype: float_denorm
- path: /config/model_cache/yolox_tiny.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-
-
-#### RF-DETR
-
-[RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
-
-
- RF-DETR Setup & Config
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
- onnx:
- type: onnx
-
-model:
- model_type: rfdetr
- width: 320
- height: 320
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/rfdetr.onnx
-```
-
-
-
-#### D-FINE / DEIMv2
-
-[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
-
-
- D-FINE Setup & Config
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
- onnx:
- type: onnx
-
-model:
- model_type: dfine
- width: 640
- height: 640
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/dfine_m_obj2coco.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-
-
-
- DEIMv2 Setup & Config
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
- onnx:
- type: onnx
-
-model:
- model_type: dfine
- width: 640
- height: 640
- input_tensor: nchw
- input_dtype: float
- path: /config/model_cache/deimv2_hgnetv2_n.onnx
- labelmap_path: /labelmap/coco-80.txt
-```
-
-
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
+---
## CPU Detector (not recommended)
@@ -911,18 +482,9 @@ The number of threads used by the interpreter can be specified using the `"num_t
A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
-```yaml
-detectors:
- cpu1:
- type: cpu
- num_threads: 3
- cpu2:
- type: cpu
- num_threads: 3
+### Configuration {#configuration-cpu}
-model:
- path: "/custom_model.tflite"
-```
+
When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.
@@ -930,19 +492,15 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
-### Setup
+### Setup {#setup-deepstack}
To get started with CodeProject.AI, visit their [official website](https://www.codeproject.com/Articles/5322557/CodeProject-AI-Server-AI-the-easy-way) to follow the instructions to download and install the AI server on your preferred device. Detailed setup instructions for CodeProject.AI are outside the scope of the Frigate documentation.
-To integrate CodeProject.AI into Frigate, you'll need to make the following changes to your Frigate configuration file:
+To integrate CodeProject.AI into Frigate, configure the detector as follows:
-```yaml
-detectors:
- deepstack:
- api_url: http://:/v1/vision/detection
- type: deepstack
- api_timeout: 0.1 # seconds
-```
+### Configuration {#configuration-deepstack}
+
+
Replace `` and `` with the IP address and port of your CodeProject.AI server.
@@ -958,155 +516,9 @@ See the [installation docs](../frigate/installation.md#memryx-mx3) for informati
To configure a MemryX detector, simply set the `type` attribute to `memryx` and follow the configuration guide below.
-### Configuration
+### Configuration {#configuration-memryx}
-To configure the MemryX detector, use the following example configuration:
-
-#### Single PCIe MemryX MX3
-
-```yaml
-detectors:
- memx0:
- type: memryx
- device: PCIe:0
-```
-
-#### Multiple PCIe MemryX MX3 Modules
-
-```yaml
-detectors:
- memx0:
- type: memryx
- device: PCIe:0
-
- memx1:
- type: memryx
- device: PCIe:1
-
- memx2:
- type: memryx
- device: PCIe:2
-```
-
-### Supported Models
-
-MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
-
-#### YOLO-NAS
-
-The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
-
-**Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
-
-The input size for **YOLO-NAS** can be set to either **320x320** (default) or **640x640**.
-
-- The default size of **320x320** is optimized for lower CPU usage and faster inference times.
-
-##### Configuration
-
-Below is the recommended configuration for using the **YOLO-NAS** (small) model with the MemryX detector:
-
-```yaml
-detectors:
- memx0:
- type: memryx
- device: PCIe:0
-
-model:
- model_type: yolonas
- width: 320 # (Can be set to 640 for higher resolution)
- height: 320 # (Can be set to 640 for higher resolution)
- input_tensor: nchw
- input_dtype: float
- labelmap_path: /labelmap/coco-80.txt
- # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
- # path: /config/yolonas.zip
- # The .zip file must contain:
- # ├── yolonas.dfp (a file ending with .dfp)
- # └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
-```
-
-#### YOLOv9
-
-The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
-
-##### Configuration
-
-Below is the recommended configuration for using the **YOLOv9** (small) model with the MemryX detector:
-
-```yaml
-detectors:
- memx0:
- type: memryx
- device: PCIe:0
-
-model:
- model_type: yolo-generic
- width: 320 # (Can be set to 640 for higher resolution)
- height: 320 # (Can be set to 640 for higher resolution)
- input_tensor: nchw
- input_dtype: float
- labelmap_path: /labelmap/coco-80.txt
- # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
- # path: /config/yolov9.zip
- # The .zip file must contain:
- # ├── yolov9.dfp (a file ending with .dfp)
-```
-
-#### YOLOX
-
-The model is sourced from the [OpenCV Model Zoo](https://github.com/opencv/opencv_zoo) and precompiled to DFP.
-
-##### Configuration
-
-Below is the recommended configuration for using the **YOLOX** (small) model with the MemryX detector:
-
-```yaml
-detectors:
- memx0:
- type: memryx
- device: PCIe:0
-
-model:
- model_type: yolox
- width: 640
- height: 640
- input_tensor: nchw
- input_dtype: float_denorm
- labelmap_path: /labelmap/coco-80.txt
- # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
- # path: /config/yolox.zip
- # The .zip file must contain:
- # ├── yolox.dfp (a file ending with .dfp)
-```
-
-#### SSDLite MobileNet v2
-
-The model is sourced from the [OpenMMLab Model Zoo](https://mmdeploy-oss.openmmlab.com/model/mmdet-det/ssdlite-e8679f.onnx) and has been converted to DFP.
-
-##### Configuration
-
-Below is the recommended configuration for using the **SSDLite MobileNet v2** model with the MemryX detector:
-
-```yaml
-detectors:
- memx0:
- type: memryx
- device: PCIe:0
-
-model:
- model_type: ssd
- width: 320
- height: 320
- input_tensor: nchw
- input_dtype: float
- labelmap_path: /labelmap/coco-80.txt
- # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
- # path: /config/ssdlite_mobilenet.zip
- # The .zip file must contain:
- # ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
- # └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
-```
+
#### Using a Custom Model
@@ -1227,20 +639,7 @@ The TensorRT detector uses `.trt` model files that are located in `/config/model
Use the config below to work with generated TRT models:
-```yaml
-detectors:
- tensorrt:
- type: tensorrt
- device: 0 #This is the default, select the first GPU
-
-model:
- path: /config/model_cache/tensorrt/yolov7-320.trt
- labelmap_path: /labelmap/coco-80.txt
- input_tensor: nchw
- input_pixel_format: rgb
- width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
- height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
-```
+
## Synaptics
@@ -1254,28 +653,11 @@ This implementation is based on sdk `v1.5.0`.
See the [installation docs](../frigate/installation.md#synaptics) for information on configuring the SL-series NPU hardware.
-### Configuration
+### Configuration {#configuration-synaptics}
When configuring the Synap detector, you have to specify the model: a local **path**.
-#### SSD Mobilenet
-
-A synap model is provided in the container at /mobilenet.synap and is used by this detector type by default. The model comes from [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
-
-Use the model configuration shown below when using the synaptics detector with the default synap model:
-
-```yaml
-detectors: # required
- synap_npu: # required
- type: synaptics # required
-
-model: # required
- path: /synaptics/mobilenet.synap # required
- width: 224 # required
- height: 224 # required
- tensor_format: nhwc # default value (optional. If you change the model, it is required)
- labelmap_path: /labelmap/coco-80.txt # required
-```
+
## Rockchip platform
@@ -1289,6 +671,12 @@ Hardware accelerated object detection is supported on the following SoCs:
This implementation uses the [Rockchip's RKNN-Toolkit2](https://github.com/airockchip/rknn-toolkit2/), version v2.3.2.
+:::info
+
+If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
+
+:::
+
:::tip
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
@@ -1324,16 +712,6 @@ $ cat /sys/kernel/debug/rknpu/load
This `config.yml` shows all relevant options to configure the detector and explains them. All values shown are the default values (except for two). Lines that are required at least to use the detector are labeled as required, all other lines are optional.
-```yaml
-detectors: # required
- rknn: # required
- type: rknn # required
- # number of NPU cores to use
- # 0 means choose automatically
- # increase for better performance if you have a multicore NPU e.g. set to 3 on rk3588
- num_cores: 0
-```
-
The inference time was determined on a rk3588 with 3 NPU cores.
| Model | Size in mb | Inference time in ms |
@@ -1348,75 +726,13 @@ The inference time was determined on a rk3588 with 3 NPU cores.
- All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
- You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
-#### YOLO-NAS
-
-```yaml
-model: # required
- # name of model (will be automatically downloaded) or path to your own .rknn model file
- # possible values are:
- # - deci-fp16-yolonas_s
- # - deci-fp16-yolonas_m
- # - deci-fp16-yolonas_l
- # your yolonas_model.rknn
- path: deci-fp16-yolonas_s
- model_type: yolonas
- width: 320
- height: 320
- input_pixel_format: bgr
- input_tensor: nhwc
- labelmap_path: /labelmap/coco-80.txt
-```
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
-
-:::
-
-#### YOLO (v9)
-
-```yaml
-model: # required
- # name of model (will be automatically downloaded) or path to your own .rknn model file
- # possible values are:
- # - frigate-fp16-yolov9-t
- # - frigate-fp16-yolov9-s
- # - frigate-fp16-yolov9-m
- # - frigate-fp16-yolov9-c
- # - frigate-fp16-yolov9-e
- # your yolo_model.rknn
- path: frigate-fp16-yolov9-t
- model_type: yolo-generic
- width: 320
- height: 320
- input_tensor: nhwc
- labelmap_path: /labelmap/coco-80.txt
-```
-
-#### YOLOx
-
-```yaml
-model: # required
- # name of model (will be automatically downloaded) or path to your own .rknn model file
- # possible values are:
- # - rock-i8-yolox_nano
- # - rock-i8-yolox_tiny
- # - rock-fp16-yolox_nano
- # - rock-fp16-yolox_tiny
- # your yolox_model.rknn
- path: rock-i8-yolox_nano
- model_type: yolox
- width: 416
- height: 416
- input_tensor: nhwc
- labelmap_path: /labelmap/coco-80.txt
-```
+
### Converting your own onnx model to rknn format
To convert a onnx model to the rknn format using the [rknn-toolkit2](https://github.com/airockchip/rknn-toolkit2/) you have to:
-- Place one ore more models in onnx format in the directory `config/model_cache/rknn_cache/onnx` on your docker host (this might require `sudo` privileges).
+- Place one or more models in onnx format in the directory `config/model_cache/rknn_cache/onnx` on your docker host (this might require `sudo` privileges).
- Save the configuration file under `config/conv2rknn.yaml` (see below for details).
- Run `docker exec python3 /opt/conv2rknn.py`. If the conversion was successful, the rknn models will be placed in `config/model_cache/rknn_cache`.
@@ -1434,267 +750,37 @@ config:
quant_img_RGB2BGR: true
```
-Explanation of the paramters:
+Explanation of the parameters:
- `soc`: A list of all SoCs you want to build the rknn model for. If you don't specify this parameter, the script tries to find out your SoC and builds the rknn model for this one.
- `quantization`: true: 8 bit integer (i8) quantization, false: 16 bit float (fp16). Default: false.
- `output_name`: The output name of the model. The following variables are available:
- `quant`: "i8" or "fp16" depending on the config
- - `input_basename`: the basename of the input model (e.g. "my_model" if the input model is calles "my_model.onnx")
+ - `input_basename`: the basename of the input model (e.g. "my_model" if the input model is called "my_model.onnx")
- `soc`: the SoC this model was build for (e.g. "rk3588")
- `tk_version`: Version of `rknn-toolkit2` (e.g. "2.3.0")
- **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
-## DeGirum
+## AXERA
-DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
+Hardware accelerated object detection is supported on the following SoCs:
-### Configuration
+- AX650N
+- AX8850N
-#### AI Server Inference
+This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
-Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
+See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
-```yaml
-degirum_detector:
- container_name: degirum
- image: degirum/aiserver:latest
- privileged: true
- ports:
- - "8778:8778"
-```
+:::info
-All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
-
-Once completed, changing the `config.yml` file is simple.
-
-```yaml
-degirum_detector:
- type: degirum
- location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
- zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
- token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
-```
-
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
-
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
- - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
-- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
-- A path to some model.json.
-
-```yaml
-model:
- path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
- width: 300 # width is in the model name as the first number in the "int"x"int" section
- height: 300 # height is in the model name as the second number in the "int"x"int" section
- input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### Local Inference
-
-It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
-
-1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
-2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
-3. Create a DeGirum detector in your `config.yml` file.
-
-```yaml
-degirum_detector:
- type: degirum
- location: "@local" # For accessing AI Hub devices and models
- zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
- token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
-```
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
- path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
- width: 300 # width is in the model name as the first number in the "int"x"int" section
- height: 300 # height is in the model name as the second number in the "int"x"int" section
- input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### AI Hub Cloud Inference
-
-If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
-
-1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
-2. Get an access token.
-3. Create a DeGirum detector in your `config.yml` file.
-
-```yaml
-degirum_detector:
- type: degirum
- location: "@cloud" # For accessing AI Hub devices and models
- zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
- token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).
-```
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
- path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
- width: 300 # width is in the model name as the first number in the "int"x"int" section
- height: 300 # height is in the model name as the second number in the "int"x"int" section
- input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-# Models
-
-Some model types are not included in Frigate by default.
-
-## Downloading Models
-
-Here are some tips for getting different model types
-
-### Downloading D-FINE Model
-
-D-FINE can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=s` in the first line to `s`, `m`, or `l` size.
-
-```sh
-docker build . --build-arg MODEL_SIZE=s --output . -f- <<'EOF'
-FROM python:3.11 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /dfine
-RUN git clone https://github.com/Peterande/D-FINE.git .
-RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
-# Create output directory and download checkpoint
-RUN mkdir -p output
-ARG MODEL_SIZE
-RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
-# Modify line 58 of export_onnx.py to change batch size to 1
-RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
-RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
-FROM scratch
-ARG MODEL_SIZE
-COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
-EOF
-```
-
-### Downloading DEIMv2 Model
-
-[DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
-
-- **HGNetv2** (smaller/faster): `atto`, `femto`, `pico`, `n`
-- **DINOv3** (larger/more accurate): `s`, `m`, `l`, `x`
-
-Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
-
-```sh
-docker build . --rm --build-arg BACKBONE=hgnetv2 --build-arg MODEL_SIZE=n --output . -f- <<'EOF'
-FROM python:3.11-slim AS build
-RUN apt-get update && apt-get install --no-install-recommends -y git libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /deimv2
-RUN git clone https://github.com/Intellindust-AI-Lab/DEIMv2.git .
-# Install CPU-only PyTorch first to avoid pulling CUDA variant
-RUN uv pip install --no-cache --system torch torchvision --index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache --system -r requirements.txt
-RUN uv pip install --no-cache --system onnx safetensors huggingface_hub
-RUN mkdir -p output
-ARG BACKBONE
-ARG MODEL_SIZE
-# Download from Hugging Face and convert safetensors to pth
-RUN python3 -c "\
-from huggingface_hub import hf_hub_download; \
-from safetensors.torch import load_file; \
-import torch; \
-backbone = '${BACKBONE}'.replace('hgnetv2','HGNetv2').replace('dinov3','DINOv3'); \
-size = '${MODEL_SIZE}'.upper(); \
-st = load_file(hf_hub_download('Intellindust/DEIMv2_' + backbone + '_' + size + '_COCO', 'model.safetensors')); \
-torch.save({'model': st}, 'output/deimv2.pth')"
-RUN sed -i "s/data = torch.rand(2/data = torch.rand(1/" tools/deployment/export_onnx.py
-# HuggingFace safetensors omits frozen constants that the model constructor initializes
-RUN sed -i "s/cfg.model.load_state_dict(state)/cfg.model.load_state_dict(state, strict=False)/" tools/deployment/export_onnx.py
-RUN python3 tools/deployment/export_onnx.py -c configs/deimv2/deimv2_${BACKBONE}_${MODEL_SIZE}_coco.yml -r output/deimv2.pth
-FROM scratch
-ARG BACKBONE
-ARG MODEL_SIZE
-COPY --from=build /deimv2/output/deimv2.onnx /deimv2_${BACKBONE}_${MODEL_SIZE}.onnx
-EOF
-```
-
-### Downloading RF-DETR Model
-
-RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
-
-```sh
-docker build . --build-arg MODEL_SIZE=Nano --rm --output . -f- <<'EOF'
-FROM python:3.12 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
-WORKDIR /rfdetr
-RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnx==1.19.1 transformers==4.57.6 onnxscript
-ARG MODEL_SIZE
-RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
-FROM scratch
-ARG MODEL_SIZE
-COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
-EOF
-```
-
-### Downloading YOLO-NAS Model
-
-You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) which can be run directly in [Google Colab](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
-
-:::warning
-
-The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
+The AXEngine detector downloads its default model from HuggingFace on first startup. Once cached, the model works fully offline. See [Network Requirements](/frigate/network_requirements#hardware-specific-detector-models) for details.
:::
-The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
+### Configuration {#configuration-axengine}
-### Downloading YOLO Models
+When configuring the AXEngine detector, you have to specify the model name.
-#### YOLOx
-
-YOLOx models can be downloaded [from the YOLOx repo](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime).
-
-#### YOLOv3, YOLOv4, and YOLOv7
-
-To export as ONNX:
-
-```sh
-git clone https://github.com/NateMeyer/tensorrt_demos
-cd tensorrt_demos/yolo
-./download_yolo.sh
-python3 yolo_to_onnx.py -m yolov7-320
-```
-
-#### YOLOv9 for Google Coral Support
-
-[Download the model](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite), bind mount the file into the container, and provide the path with `model.path`. Note that the linked model requires a 17-label [labelmap file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) that includes only 17 COCO classes.
-
-#### YOLOv9 for other detectors
-
-YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
-
-```sh
-docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
-FROM python:3.11 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
-WORKDIR /yolov9
-ADD https://github.com/WongKinYiu/yolov9.git .
-RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
-ARG MODEL_SIZE
-ARG IMG_SIZE
-ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
-RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
-RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
-FROM scratch
-ARG MODEL_SIZE
-ARG IMG_SIZE
-COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
-EOF
-```
+
diff --git a/docs/docs/configuration/object_filters.md b/docs/docs/configuration/object_filters.md
index 3f36086c01..b2e7fca319 100644
--- a/docs/docs/configuration/object_filters.md
+++ b/docs/docs/configuration/object_filters.md
@@ -3,11 +3,15 @@ id: object_filters
title: Filters
---
-There are several types of object filters that can be used to reduce false positive rates.
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+There are several types of object filters that can be used to reduce [false positive](/frigate/glossary#false-positive) rates.
## Object Scores
-For object filters in your configuration, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
+For object filters, any single detection below `min_score` will be ignored as a false positive. `threshold` is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when `min_score` is set to 0.6 and threshold is set to 0.85:
| Frame | Current Score | Score History | Computed Score | Detected Object |
| ----- | ------------- | --------------------------------- | -------------- | --------------- |
@@ -20,13 +24,59 @@ For object filters in your configuration, any single detection below `min_score`
In frame 2, the score is below the `min_score` value, so Frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the `threshold` is the object marked as a true positive. That happens in frame 4 in the example.
+The **top score** is the highest computed score the tracked object has ever reached during its lifetime. Because the computed score rises and falls as new frames come in, the top score can be thought of as the peak confidence Frigate had in the object. In Frigate's UI (such as the Tracking Details pane in Explore), you may see all three values:
+
+- **Score**: the raw detector score for that single frame.
+- **Computed Score**: the median of the most recent score history at that moment. This is the value compared against `threshold`.
+- **Top Score**: the highest computed score reached so far for the tracked object.
+
### Minimum Score
Any detection below `min_score` will be immediately thrown out and never tracked because it is considered a false positive. If `min_score` is too low then false positives may be detected and tracked which can confuse the object tracker and may lead to wasted resources. If `min_score` is too high then lower scoring true positives like objects that are further away or partially occluded may be thrown out which can also confuse the tracker and cause valid tracked objects to be lost or disjointed.
### Threshold
-`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create an tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
+`threshold` is used to determine that the object is a true positive. Once an object is detected with a score >= `threshold` object is considered a true positive. If `threshold` is too low then some higher scoring false positives may create a tracked object. If `threshold` is too high then true positive tracked objects may be missed due to the object never scoring high enough.
+
+## Configuring Object Scores
+
+
+
+
+Navigate to to set score filters globally.
+
+| Field | Description |
+| --------------------------------------- | ---------------------------------------------------------------- |
+| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking |
+| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |
+
+To override score filters for a specific camera, navigate to and select the camera.
+
+
+
+
+```yaml
+objects:
+ filters:
+ person:
+ min_score: 0.5
+ threshold: 0.7
+```
+
+To override at the camera level:
+
+```yaml
+cameras:
+ front_door:
+ objects:
+ filters:
+ person:
+ min_score: 0.5
+ threshold: 0.7
+```
+
+
+
## Object Shape
@@ -46,12 +96,56 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
:::
+### Configuring Shape Filters
+
+
+
+
+Navigate to to set shape filters globally.
+
+| Field | Description |
+| --------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Min Area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Max Area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box |
+| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box |
+
+To override shape filters for a specific camera, navigate to and select the camera.
+
+
+
+
+```yaml
+objects:
+ filters:
+ person:
+ min_area: 5000
+ max_area: 100000
+ min_ratio: 0.5
+ max_ratio: 2.0
+```
+
+To override at the camera level:
+
+```yaml
+cameras:
+ front_door:
+ objects:
+ filters:
+ person:
+ min_area: 5000
+ max_area: 100000
+```
+
+
+
+
## Other Tools
### Zones
-[Required zones](/configuration/zones.md) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
+[Required zones](/configuration/zones.md#restricting-alerts-and-detections-to-specific-zones) can be a great tool to reduce false positives that may be detected in the sky or other areas that are not of interest. The required zones will only create tracked objects for objects that enter the zone.
### Object Masks
-[Object Filter Masks](/configuration/masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape.
+[Object Filter Masks](/configuration/masks#object-filter-masks) are a last resort but can be useful when false positives are in the relatively same place but can not be filtered due to their size or shape. Object filter masks can be configured in .
diff --git a/docs/docs/configuration/objects.md b/docs/docs/configuration/objects.md
index 796d312581..e5a24ea081 100644
--- a/docs/docs/configuration/objects.md
+++ b/docs/docs/configuration/objects.md
@@ -3,6 +3,9 @@ id: objects
title: Available Objects
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
import labels from "../../../labelmap.txt";
Frigate includes the object labels listed below from the Google Coral test data.
@@ -10,7 +13,7 @@ Frigate includes the object labels listed below from the Google Coral test data.
Please note:
- `car` is listed twice because `truck` has been renamed to `car` by default. These object types are frequently confused.
-- `person` is the only tracked object by default. See the [full configuration reference](reference.md) for an example of expanding the list of tracked objects.
+- `person` is the only tracked object by default. To track additional objects, configure them in the objects settings.
+## Configuring Tracked Objects
+
+By default, Frigate only tracks `person`. To track additional object types, add them to the tracked objects list.
+
+
+
+
+1. Navigate to .
+ - Add the desired object types to the **Objects to track** list (e.g., `person`, `car`, `dog`)
+
+To override the tracked objects list for a specific camera:
+
+1. Navigate to .
+ - Add the desired object types to the **Objects to track** list
+
+
+
+
+```yaml
+objects:
+ track:
+ - person
+ - car
+ - dog
+```
+
+To override at the camera level:
+
+```yaml
+cameras:
+ front_door:
+ objects:
+ track:
+ - person
+ - car
+```
+
+
+
+
+## Filtering Objects
+
+Object filters help reduce false positives by constraining the size, shape, and confidence thresholds for each object type. Filters can be configured globally or per camera.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| --------------------------------------- | ------------------------------------------------------------------------ |
+| **Object filters > Person > Min Area** | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Max Area** | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box |
+| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box |
+| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking |
+| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive |
+
+To override filters for a specific camera, navigate to .
+
+
+
+
+```yaml
+objects:
+ filters:
+ person:
+ min_area: 5000
+ max_area: 100000
+ min_ratio: 0.5
+ max_ratio: 2.0
+ min_score: 0.5
+ threshold: 0.7
+```
+
+To override at the camera level:
+
+```yaml
+cameras:
+ front_door:
+ objects:
+ filters:
+ person:
+ min_area: 5000
+ threshold: 0.7
+```
+
+
+
+
+## Object Filter Masks
+
+Object filter masks prevent specific object types from being detected in certain areas of the camera frame. These masks check the bottom center of the bounding box. A global mask applies to all object types, while per-object masks apply only to the specified type.
+
+
+
+
+Navigate to and select a camera. Use the mask editor to draw object filter masks directly on the camera feed. Global object masks and per-object masks can both be configured from this view.
+
+
+
+
+```yaml
+objects:
+ # Global mask applied to all object types
+ mask:
+ mask1:
+ friendly_name: "Object filter mask area"
+ enabled: true
+ coordinates: "0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278"
+ # Per-object mask
+ filters:
+ person:
+ mask:
+ mask1:
+ friendly_name: "Person filter mask"
+ enabled: true
+ coordinates: "0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278"
+```
+
+
+
+
+:::note
+
+The global mask is combined with any object-specific mask. Both are checked based on the bottom center of the bounding box.
+
+:::
+
## Custom Models
Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:
@@ -26,4 +158,4 @@ Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use yo
- EdgeTPU Model: `/edgetpu_model.tflite`
- Labels: `/labelmap.txt`
-You also need to update the [model config](advanced.md#model) if they differ from the defaults.
+You also need to update the [model config](advanced/system.md#model) if they differ from the defaults.
diff --git a/docs/docs/configuration/profiles.md b/docs/docs/configuration/profiles.md
new file mode 100644
index 0000000000..87475d74b3
--- /dev/null
+++ b/docs/docs/configuration/profiles.md
@@ -0,0 +1,258 @@
+---
+id: profiles
+title: Profiles
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Profiles allow you to define named sets of camera configuration overrides that can be activated and deactivated at runtime without restarting Frigate. This is useful for scenarios like switching between "Home" and "Away" modes, daytime and nighttime configurations, or any situation where you want to quickly change how multiple cameras behave.
+
+## How Profiles Work
+
+Profiles operate as a two-level system:
+
+1. **Profile definitions** are declared at the top level of your config under `profiles`. Each definition has a machine name (the key) and a `friendly_name` for display in the UI.
+2. **Camera profile overrides** are declared under each camera's `profiles` section, keyed by the profile name. Only the settings you want to change need to be specified. Everything else is inherited from the camera's base configuration.
+
+When a profile is activated, Frigate merges each camera's profile overrides on top of its base config. When the profile is deactivated, all cameras revert to their original settings. Only one profile can be active at a time.
+
+:::info
+
+Profile changes are applied in-memory and take effect immediately. No restart is required. The active profile is persisted across Frigate restarts (stored in the `/config/.profiles` file).
+
+:::
+
+## Configuration
+
+The easiest way to define profiles is to use the Frigate UI. Profiles can also be configured manually in your configuration file.
+
+### Creating and Managing Profiles
+
+
+
+
+1. **Create a profile**: Navigate to . Click the **Add Profile** button, enter a name (and optionally a profile ID).
+2. **Configure overrides**: Navigate to a camera configuration section (e.g. Motion detection, Record, Notifications). In the top right, two buttons will appear - choose a camera and a profile from the profile selector to edit overrides for that camera and section. Only the fields you change will be stored as overrides. Fields that require a restart are hidden since profiles are applied at runtime. You can click the **Remove Profile Override** button to clear overrides.
+3. **Activate a profile**: Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to , then choose a profile in the Active Profile dropdown to activate it. The active profile is also shown in the status bar at the bottom of the screen on desktop browsers.
+4. **Delete a profile**: Navigate to , then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
+
+
+
+
+First, define your profiles at the top level of your Frigate config. Every profile name referenced by a camera must be defined here.
+
+```yaml
+profiles:
+ home:
+ friendly_name: Home
+ away:
+ friendly_name: Away
+ night:
+ friendly_name: Night Mode
+```
+
+Under each camera, add a `profiles` section with overrides for each profile. You only need to include the settings you want to change.
+
+```yaml
+cameras:
+ front_door:
+ ffmpeg:
+ inputs:
+ - path: rtsp://camera:554/stream
+ roles:
+ - detect
+ - record
+ detect:
+ enabled: true
+ record:
+ enabled: true
+ profiles:
+ away:
+ detect:
+ enabled: true
+ notifications:
+ enabled: true
+ objects:
+ track:
+ - person
+ - car
+ - package
+ review:
+ alerts:
+ labels:
+ - person
+ - car
+ - package
+ home:
+ detect:
+ enabled: true
+ notifications:
+ enabled: false
+ objects:
+ track:
+ - person
+```
+
+
+
+
+### Supported Override Sections
+
+The following camera configuration sections can be overridden in a profile:
+
+| Section | Description |
+| ------------------ | ----------------------------------------- |
+| `enabled` | Enable or disable the camera entirely |
+| `audio` | Audio detection settings |
+| `birdseye` | Birdseye view settings |
+| `detect` | Object detection settings |
+| `face_recognition` | Face recognition settings |
+| `lpr` | License plate recognition settings |
+| `motion` | Motion detection settings |
+| `notifications` | Notification settings |
+| `objects` | Object tracking and filter settings |
+| `record` | Recording settings |
+| `review` | Review alert and detection settings |
+| `snapshots` | Snapshot settings |
+| `zones` | Zone definitions (merged with base zones) |
+
+:::note
+
+Only the fields you explicitly set in a profile override are applied. All other fields retain their base configuration values. For masks and zones, profile zones **override** the camera's base masks and zones. If configuring profiles via YAML, you should not define masks or zones in profiles that are not defined in the base config.
+
+:::
+
+## Activating Profiles
+
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
+
+In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
+
+Activating or deactivating a profile clears any [runtime toggle overrides](/configuration/live#runtime-toggle-persistence) so the profile's settings aren't silently undone by a stale toggle from before the switch.
+
+## Example: Home / Away Setup
+
+A common use case is having different detection and notification settings based on whether you are home or away. This example below is for a system with two cameras, `front_door` and `indoor_cam`.
+
+
+
+
+1. Navigate to and create two profiles: **Home** and **Away**.
+2. From to the Camera configuration section in Settings, choose the **front_door** camera, and select the **Away** profile from the profile dropdown. Then, enable notifications from the Notifications pane, and set alert labels to `person` and `car` from the Review pane. Then, from the profile dropdown choose **Home** profile, then navigate to Notifications to disable notifications.
+3. For the **indoor_cam** camera, perform similar steps - configure the **Away** profile to enable the camera, detection, and recording. Configure the **Home** profile to disable the camera entirely for privacy.
+4. Activate the desired profile from or from the **Profiles** option in Frigate's main menu.
+
+
+
+
+```yaml
+profiles:
+ home:
+ friendly_name: Home
+ away:
+ friendly_name: Away
+
+cameras:
+ front_door:
+ ffmpeg:
+ inputs:
+ - path: rtsp://camera:554/stream
+ roles:
+ - detect
+ - record
+ detect:
+ enabled: true
+ record:
+ enabled: true
+ notifications:
+ enabled: false
+ profiles:
+ away:
+ notifications:
+ enabled: true
+ review:
+ alerts:
+ labels:
+ - person
+ - car
+ home:
+ notifications:
+ enabled: false
+
+ indoor_cam:
+ ffmpeg:
+ inputs:
+ - path: rtsp://camera:554/indoor
+ roles:
+ - detect
+ - record
+ detect:
+ enabled: false
+ record:
+ enabled: false
+ profiles:
+ away:
+ enabled: true
+ detect:
+ enabled: true
+ record:
+ enabled: true
+ home:
+ enabled: false
+```
+
+
+
+
+In this example:
+
+- **Away profile**: The front door camera enables notifications and tracks specific alert labels. The indoor camera is fully enabled with detection and recording.
+- **Home profile**: The front door camera disables notifications. The indoor camera is completely disabled for privacy.
+- **No profile active**: All cameras use their base configuration values.
+
+## FAQ
+
+### Can I define a zone or mask in a profile but not have it in the base config?
+
+No. Profiles are pure overrides. Every zone and mask defined under a profile must reference an entry that already exists on the base camera config. Configurations that introduce profile-only zones or masks are rejected at startup.
+
+If you want a zone or mask to be active only under a specific profile, define it on the base config with `enabled: false`, then enable it in that profile's overrides.
+
+### How do I revert a profile zone or mask override back to the base configuration?
+
+Delete the override. In the Frigate UI, edit the profile and use the "Revert override" action (the trash can icon) on the zone or mask. The base entry is left untouched, and once the override is removed the profile inherits the base values for that zone or mask.
+
+### Can multiple profiles be active at the same time?
+
+No. Only one profile can be active at a time. Activating a new profile automatically deactivates the current one.
+
+### What happens to my profile overrides if I delete a zone or mask from the base?
+
+When you delete a base zone or mask in the Frigate UI, any profile overrides for that entry are deleted automatically as part of the same operation. If you remove a base entry by editing your config file directly and leave a profile override behind, the config will fail validation at startup until the orphaned override is removed as well.
+
+### How do I make a YAML profile track no objects at all?
+
+Set the tracked object list explicitly to an empty list in the profile:
+
+```yaml
+cameras:
+ front_door:
+ profiles:
+ home:
+ objects:
+ track: []
+```
+
+Leaving the `objects` section empty (or omitting `track`) does not clear the list. Empty sections set no fields, so the profile inherits the full tracked object list from the base config, including anything set at the global level. The same applies to other lists, such as `audio.listen`.
+
+### Why are some settings missing when I configure a profile override?
+
+Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
+
+### Can I schedule profiles to be enabled or disabled at certain times?
+
+Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.
+
+If you prefer something lightweight, a simple script driven by a cron job that toggles profiles on a schedule works too.
diff --git a/docs/docs/configuration/record.md b/docs/docs/configuration/record.md
index e805cc2d7d..93d26d8801 100644
--- a/docs/docs/configuration/record.md
+++ b/docs/docs/configuration/record.md
@@ -3,9 +3,19 @@ id: record
title: Recording
---
-Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH//MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy in the config. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
-New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
+Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH//MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
+
+New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
+
+:::tip
+
+To keep a specific clip beyond your retention window, [export](/usage/exports) it rather than increasing retention for the whole camera. Exports are saved separately and are never removed by retention.
+
+:::
H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other browsers require recordings to be encoded with H264.
@@ -13,7 +23,23 @@ H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other br
### Most conservative: Ensure all video is saved
-For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion will be saved for 7 days. After 7 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
+For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following configuration will store all video for 3 days. After 3 days, only video containing motion will be saved for 7 days. After 7 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
+
+
+
+
+Navigate to .
+
+- Set **Enable recording** to on
+- Set **Continuous retention > Retention days** to `3`
+- Set **Motion retention > Retention days** to `7`
+- Set **Alert retention > Event retention > Retention days** to `30`
+- Set **Alert retention > Event retention > Retention mode** to `all`
+- Set **Detection retention > Event retention > Retention days** to `30`
+- Set **Detection retention > Event retention > Retention mode** to `all`
+
+
+
```yaml
record:
@@ -32,9 +58,27 @@ record:
mode: all
```
+
+
+
### Reduced storage: Only saving video when motion is detected
-In order to reduce storage requirements, you can adjust your config to only retain video where motion / activity was detected.
+To reduce storage requirements, configure recording to only retain video where motion or activity was detected.
+
+
+
+
+Navigate to .
+
+- Set **Enable recording** to on
+- Set **Motion retention > Retention days** to `3`
+- Set **Alert retention > Event retention > Retention days** to `30`
+- Set **Alert retention > Event retention > Retention mode** to `motion`
+- Set **Detection retention > Event retention > Retention days** to `30`
+- Set **Detection retention > Event retention > Retention mode** to `motion`
+
+
+
```yaml
record:
@@ -51,9 +95,25 @@ record:
mode: motion
```
+
+
+
### Minimum: Alerts only
-If you only want to retain video that occurs during activity caused by tracked object(s), this config will discard video unless an alert is ongoing.
+If you only want to retain video that occurs during activity caused by tracked object(s), this configuration will discard video unless an alert is ongoing.
+
+
+
+
+Navigate to .
+
+- Set **Enable recording** to on
+- Set **Continuous retention > Retention days** to `0`
+- Set **Alert retention > Event retention > Retention days** to `30`
+- Set **Alert retention > Event retention > Retention mode** to `motion`
+
+
+
```yaml
record:
@@ -66,9 +126,78 @@ record:
mode: motion
```
-## Will Frigate delete old recordings if my storage runs out?
+
+
-If there is less than an hour left of storage, the oldest hour of recordings will be deleted and a message will be printed in the Frigate logs. This emergency cleanup deletes the oldest recordings first regardless of retention settings to reclaim space as quickly as possible.
+## Pre-capture and Post-capture
+
+The `pre_capture` and `post_capture` settings control how many seconds of video are included before and after an alert or detection. These can be configured independently for alerts and detections, and can be set globally or overridden per camera.
+
+
+
+
+Navigate to for global defaults, or to override for a specific camera.
+
+| Field | Description |
+| ---------------------------------------------- | ---------------------------------------------------- |
+| **Alert retention > Pre-capture seconds** | Seconds of video to include before an alert event |
+| **Alert retention > Post-capture seconds** | Seconds of video to include after an alert event |
+| **Detection retention > Pre-capture seconds** | Seconds of video to include before a detection event |
+| **Detection retention > Post-capture seconds** | Seconds of video to include after a detection event |
+
+
+
+
+```yaml
+record:
+ enabled: True
+ alerts:
+ pre_capture: 5 # seconds before the alert to include
+ post_capture: 5 # seconds after the alert to include
+ detections:
+ pre_capture: 5 # seconds before the detection to include
+ post_capture: 5 # seconds after the detection to include
+```
+
+
+
+
+- **Default**: 5 seconds for both pre and post capture.
+- **Pre-capture maximum**: 60 seconds.
+- These settings apply per review category (alerts and detections), not per object type.
+
+### How pre/post capture interacts with retention mode
+
+The `pre_capture` and `post_capture` values define the **time window** around a review item, but only recording segments that also match the configured **retention mode** are actually kept on disk.
+
+- **`mode: all`**: Retains every segment within the capture window, regardless of whether motion was detected.
+- **`mode: motion`** (default): Only retains segments within the capture window that contain motion. This includes segments with active tracked objects, since object motion implies motion. Segments without any motion are discarded even if they fall within the pre/post capture range.
+- **`mode: active_objects`**: Only retains segments within the capture window where tracked objects were actively moving. Segments with general motion but no active objects are discarded.
+
+This means that with the default `motion` mode, you may see less footage than the configured pre/post capture duration if parts of the capture window had no motion.
+
+To guarantee the full pre/post capture duration is always retained:
+
+```yaml
+record:
+ enabled: True
+ alerts:
+ pre_capture: 10
+ post_capture: 10
+ retain:
+ days: 30
+ mode: all # retains all segments within the capture window
+```
+
+:::note
+
+Because recording segments are written in 10 second chunks, pre-capture timing depends on segment boundaries. The actual pre-capture footage may be slightly shorter or longer than the exact configured value.
+
+:::
+
+### Where to view pre/post capture footage
+
+Pre and post capture footage is included in the **recording timeline**, visible in the History view. Note that pre/post capture settings only affect which recording segments are **retained on disk**. They do not change the start and end points shown in the UI. The History view will still center on the review item's actual time range, but you can scrub backward and forward through the retained pre/post capture footage on the timeline. The Explore view shows object-specific clips that are trimmed to when the tracked object was actually visible, so pre/post capture time will not be reflected there.
## Configuring Recording Retention
@@ -82,7 +211,21 @@ Retention configs support decimals meaning they can be configured to retain `0.5
### Continuous and Motion Recording
-The number of days to retain continuous and motion recordings can be set via the following config where X is a number, by default continuous recording is disabled.
+The number of days to retain continuous and motion recordings can be configured. By default, continuous recording is disabled.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ----------------------------------------- | -------------------------------------------- |
+| **Enable recording** | Enable or disable recording for all cameras |
+| **Continuous retention > Retention days** | Number of days to keep continuous recordings |
+| **Motion retention > Retention days** | Number of days to keep motion recordings |
+
+
+
```yaml
record:
@@ -93,11 +236,28 @@ record:
days: 2 # <- number of days to keep motion recordings
```
-Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
+
+
+
+Continuous recording supports different retention modes [which are described below](#configuring-recording-retention).
### Object Recording
-The number of days to record review items can be specified for review items classified as alerts as well as tracked objects.
+The number of days to retain recordings for review items can be specified for items classified as alerts as well as tracked objects.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ---------------------------------------------------------- | ------------------------------------------- |
+| **Enable recording** | Enable or disable recording for all cameras |
+| **Alert retention > Event retention > Retention days** | Number of days to keep alert recordings |
+| **Detection retention > Event retention > Retention days** | Number of days to keep detection recordings |
+
+
+
```yaml
record:
@@ -110,9 +270,10 @@ record:
days: 10 # <- number of days to keep detections recordings
```
-This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
+
+
-**WARNING**: Recordings still must be enabled in the config. If a camera has recordings disabled in the config, enabling via the methods listed above will have no effect.
+This configuration will retain recording segments that overlap with alerts and detections for 10 days. Because multiple tracked objects can reference the same recording segments, this avoids storing duplicate footage for overlapping tracked objects and reduces overall storage needs.
## Can I have "continuous" recordings, but only at certain times?
@@ -122,25 +283,52 @@ Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only reco
Footage can be exported from Frigate by right-clicking (desktop) or long pressing (mobile) on a review item in the Review pane or by clicking the Export button in the History view. Exported footage is then organized and searchable through the Export view, accessible from the main navigation bar.
-### Time-lapse export
+### Custom export with FFmpeg arguments
-Time lapse exporting is available only via the [HTTP API](../integrations/api/export-recording-export-camera-name-start-start-time-end-end-time-post.api.mdx).
+For advanced use cases, the [custom export HTTP API](../integrations/api/export-recording-custom-export-custom-camera-name-start-start-time-end-end-time-post.api.mdx) lets you pass custom FFmpeg arguments when exporting a recording:
-When exporting a time-lapse the default speed-up is 25x with 30 FPS. This means that every 25 seconds of (real-time) recording is condensed into 1 second of time-lapse video (always without audio) with a smoothness of 30 FPS.
-
-To configure the speed-up factor, the frame rate and further custom settings, the configuration parameter `timelapse_args` can be used. The below configuration example would change the time-lapse speed to 60x (for fitting 1 hour of recording into 1 minute of time-lapse) with 25 FPS:
-
-```yaml {3-4}
-record:
- enabled: True
- export:
- timelapse_args: "-vf setpts=PTS/60 -r 25"
```
+POST /export/custom/{camera_name}/start/{start_time}/end/{end_time}
+```
+
+The request body accepts `ffmpeg_input_args` and `ffmpeg_output_args` to control encoding, frame rate, filters, and other FFmpeg options. If neither is provided, Frigate defaults to time-lapse output settings (25x speed, 30 FPS) with audio removed (`-an`). When providing your own `ffmpeg_input_args`, include `-an` if you want audio stripped from the export.
+
+The following example exports a time-lapse at 60x speed with 25 FPS:
+
+```json
+{
+ "name": "Front Door Time-lapse",
+ "ffmpeg_output_args": "-vf setpts=PTS/60 -r 25"
+}
+```
+
+#### CPU fallback
+
+If hardware acceleration is configured and the export fails (e.g., the GPU is unavailable), set `cpu_fallback: true` in the request body to automatically retry using software encoding.
+
+```json
+{
+ "name": "My Export",
+ "ffmpeg_output_args": "-c:v libx264 -crf 23",
+ "cpu_fallback": true
+}
+```
+
+:::note
+
+Non-admin users are restricted from using FFmpeg arguments that can access the filesystem (e.g., `-filter_complex`, file paths, and protocol references). Admin users have full control over FFmpeg arguments.
+
+:::
:::tip
-When using `hwaccel_args` globally hardware encoding is used for time lapse generation. The encoder determines its own behavior so the resulting file size may be undesirably large.
-To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (where `n` stands for the value of the quantisation parameter). The value can be adjusted to get an acceptable tradeoff between quality and file size for the given scenario.
+When `hwaccel_args` is configured, hardware encoding is used for exports. This can be overridden per camera (e.g., when camera resolution exceeds hardware encoder limits) by setting a camera-level `hwaccel_args`. Using an unrecognized value or empty string falls back to software encoding (libx264).
+
+:::
+
+:::tip
+
+To reduce output file size, add the FFmpeg parameter `-qp n` to `ffmpeg_output_args` (where `n` is the quantization parameter). Adjust the value to balance quality and file size for your scenario.
:::
@@ -148,19 +336,78 @@ To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (whe
Apple devices running the Safari browser may fail to playback h.265 recordings. The [apple compatibility option](../configuration/camera_specific.md#h265-cameras-via-safari) should be used to ensure seamless playback on Apple devices.
-## Syncing Recordings With Disk
+## Syncing Media Files With Disk
-In some cases the recordings files may be deleted but Frigate will not know this has happened. Recordings sync can be enabled which will tell Frigate to check the file system and delete any db entries for files which don't exist.
+Media files (event snapshots, event thumbnails, review thumbnails, previews, exports, and recordings) can become orphaned when database entries are deleted but the corresponding files remain on disk.
-```yaml
-record:
- sync_recordings: True
-```
+Normal operation may leave small numbers of orphaned files until Frigate's scheduled cleanup, but crashes, configuration changes, or upgrades may cause more orphaned files that Frigate does not clean up. This feature checks the file system for media files and removes any that are not referenced in the database.
-This feature is meant to fix variations in files, not completely delete entries in the database. If you delete all of your media, don't use `sync_recordings`, just stop Frigate, delete the `frigate.db` database, and restart.
+The Maintenance pane in the Frigate UI or an API endpoint `POST /api/media/sync` can be used to trigger a media sync. When using the API, a job ID is returned and the operation continues on the server. Status can be checked with the `/api/media/sync/status/{job_id}` endpoint.
+
+Setting `verbose: true` writes a detailed report of every orphaned file and database entry to `/config/media_sync/.txt`. For recordings, the report separates orphaned database entries (DB records whose files are missing from disk) from orphaned files (files on disk with no corresponding database record).
:::warning
-The sync operation uses considerable CPU resources and in most cases is not needed, only enable when necessary.
+This operation uses considerable CPU resources and includes a safety threshold that aborts if more than 50% of files would be deleted. Only run when necessary. If you set `force: true` the safety threshold will be bypassed; do not use `force` unless you are certain the deletions are intended.
:::
+
+## Understanding storage usage
+
+The storage usage Frigate reports will not exactly match what the operating system reports with `df` or `du`. This is expected, not a bug. The sections below explain how Frigate derives its storage figures and why they differ from the disk's own accounting.
+
+### How Frigate measures recording usage
+
+The **Recordings** value on the Storage Metrics page (), and the per-camera **Camera Storage** breakdown, is the sum of the recording segment sizes Frigate has written, taken from Frigate's database. It is **not** computed by a scan of the disk. Frigate tracks usage this way by design: repeatedly walking the entire drive to total its size would keep hard drives spun up and add unnecessary I/O.
+
+The disk **total** shown beside it, and the free-space figure Frigate uses to decide when to delete recordings, instead come from the operating system's report for the whole filesystem mounted at `/media/frigate`. As a result, the **Unused** value on the page is _total disk capacity minus Frigate's recordings_, not the drive's real free space, which will be lower whenever anything else is stored on the disk.
+
+### What counts toward usage, and why it won't match `df`
+
+Only **recording segments** (`/media/frigate/recordings`) are included in the recordings storage total. Plenty of other things consume real disk space but are **not** part of that number:
+
+- **Snapshots and thumbnails** (`/media/frigate/clips`): see [Snapshots](/configuration/snapshots). These are retained independently of recordings.
+- **Preview videos** and **review thumbnails** (also under `/media/frigate/clips`).
+- **Exports** (`/media/frigate/exports`): exports are never removed by retention.
+- **The database, downloaded detection models, and face / license plate training images** (stored under `/config`).
+- **Debug images from enrichments** (`/media/frigate/clips`): when enabled, License Plate Recognition's `debug_save_plates` and GenAI's `debug_save_thumbnails` save plate crops and request images for troubleshooting.
+
+These files are the usual explanation for an "other" or seemingly unaccounted bucket of space: it is real, it is Frigate's, and it simply isn't part of the _recordings_ total. They are also why comparing the **Recordings** figure to `df -h` always shows a gap: `df` additionally counts any non-Frigate data on the disk, filesystem overhead and reserved blocks (ext4 reserves ~5% for root by default, so a disk can read "full" before recordings approach the total), and recently deleted recordings whose space has not yet been reclaimed.
+
+:::tip
+
+The Storage page is not intended to be a system-wide disk monitor: it shows how much space _Frigate's recordings_ use. To see true disk usage, use `df -h` (free space) and `du -sh` (per-directory usage) on the host.
+
+:::
+
+### Free space and the `/media/frigate` mount
+
+Frigate reports the capacity and free space of whatever filesystem is actually mounted at `/media/frigate` **inside the container**. If an external drive or network share isn't truly mounted there (a missing `/etc/fstab` entry, a share that was offline when the container started, or a host that doesn't pass the path through), the container falls back to the host's OS disk, and Frigate will correctly report that smaller disk instead of the drive you intended.
+
+If the reported capacity doesn't match your drive, the mount is the place to look, not Frigate. Verify what is actually mounted from inside the container:
+
+```bash
+docker exec -it frigate df -h /media/frigate
+docker exec -it frigate mount | grep media
+```
+
+See the [storage mount layout](/frigate/installation#storage) for how the volumes are expected to be configured.
+
+### The `/tmp/cache` area is separate
+
+Recording segments are first written to `/tmp/cache`, a small, in-memory (`tmpfs`) area, before being checked and moved to `/media/frigate/recordings`. Because it is separate and small, `/tmp/cache` can fill up and produce `No space left on device` errors even when the recordings disk has plenty of room. They are different storage areas. See [Recordings troubleshooting](/troubleshooting/recordings) for diagnosing cache and slow-storage issues.
+
+### When the metrics don't match what's on disk
+
+Because usage is tracked in the database, deleting recording files directly on disk, or files left behind after an upgrade, will not update the reported usage, and can even push it above 100%. Frigate is unaware of files it didn't record and won't count or remove them automatically. Use [Syncing Media Files With Disk](#syncing-media-files-with-disk) to reconcile the database with what is actually on disk.
+
+## Will Frigate delete old recordings if my storage runs out?
+
+Yes. Frigate continuously checks the **free space of the disk** holding `/media/frigate/recordings`. This is different from adding up the size of every recording: free space is a single number the operating system already tracks, so Frigate can ask for it instantly without reading through your files or spinning up the disk, which is exactly why it relies on this check rather than scanning the drive. When less than roughly one hour of recording space remains (estimated from the current recording bitrate, **not** a fixed percentage), Frigate deletes the oldest recordings to reclaim space and logs a message. This emergency cleanup removes the oldest recordings first **regardless of retention settings**.
+
+Two consequences follow from this being based on whole-disk free space:
+
+- Because the check uses the disk's real free space, **anything** filling the drive, including non-Frigate files, can trigger deletion of your oldest recordings.
+- Cleanup can run while a meaningful percentage of the disk is still free (for example, with high bitrates or many cameras), because the threshold is "less than ~1 hour of recording headroom," not "X% full."
+
+Frequent emergency cleanups usually mean your configured retention exceeds what the disk can hold. Reduce your retention days so the normal retention cleanup keeps up and the emergency path rarely triggers.
diff --git a/docs/docs/configuration/restream.md b/docs/docs/configuration/restream.md
index 5955770a22..e428250ce0 100644
--- a/docs/docs/configuration/restream.md
+++ b/docs/docs/configuration/restream.md
@@ -3,11 +3,15 @@ id: restream
title: Restream
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
## RTSP
Frigate can restream your video feed as an RTSP feed for other applications such as Home Assistant to utilize it at `rtsp://:8554/`. Port 8554 must be open. [This allows you to use a video feed for detection in Frigate and Home Assistant live view at the same time without having to make two separate connections to the camera](#reduce-connections-to-camera). The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
-Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.10) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#configuration) for more advanced configurations and features.
+Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.14) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#configuration) for more advanced configurations and features.
:::note
@@ -52,6 +56,16 @@ Some cameras only support one active connection or you may just want to have a s
One connection is made to the camera. One for the restream, `detect` and `record` connect to the restream.
+Configure the go2rtc stream and point the camera inputs at the local restream.
+
+
+
+
+Navigate to and add stream entries for each camera. Then navigate to for each camera. For each input, choose **Restream (go2rtc)** and pick the matching stream from the dropdown. Frigate uses the local restream URL (`rtsp://127.0.0.1:8554/`) and the `preset-rtsp-restream` input args for that input automatically. (Choose **Manual input path** instead to type a URL directly.)
+
+
+
+
```yaml
go2rtc:
streams:
@@ -87,10 +101,21 @@ cameras:
- audio # <- only necessary if audio detection is enabled
```
+
+
+
### With Sub Stream
Two connections are made to the camera. One for the sub stream, one for the restream, `record` connects to the restream.
+
+
+
+Navigate to and add stream entries for each camera and its sub stream. Then navigate to for each camera and add separate inputs for the main and sub streams. Set each input's source to **Restream (go2rtc)** and pick the matching stream from the dropdown. Frigate uses the local restream URL and the `preset-rtsp-restream` input args for that input automatically.
+
+
+
+
```yaml
go2rtc:
streams:
@@ -138,6 +163,9 @@ cameras:
- detect
```
+
+
+
## Handling Complex Passwords
go2rtc expects URL-encoded passwords in the config, [urlencoder.org](https://urlencoder.org) can be used for this purpose.
@@ -169,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
To prevent this, you must configure two separate stream instances:
1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
-2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+2. A second stream instance with no `#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
Configuration example:
@@ -187,6 +215,8 @@ In this configuration:
- `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
- `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
+Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
+
## Security: Restricted Stream Sources
For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.
@@ -208,7 +238,7 @@ Enabling arbitrary exec sources allows execution of arbitrary commands through g
## Advanced Restream Configurations
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
:::warning
diff --git a/docs/docs/configuration/review.md b/docs/docs/configuration/review.md
index d8769749b8..14360769c3 100644
--- a/docs/docs/configuration/review.md
+++ b/docs/docs/configuration/review.md
@@ -3,6 +3,10 @@ id: review
title: Review
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
The Review page of the Frigate UI is for quickly reviewing historical footage of interest from your cameras. _Review items_ are indicated on a vertical timeline and displayed as a grid of previews - bandwidth-optimized, low frame rate, low resolution videos. Hovering over or swiping a preview plays the video and marks it as reviewed. If more in-depth analysis is required, the preview can be clicked/tapped and the full frame rate, full resolution recording is displayed.
Review items are filterable by date, object type, and camera.
@@ -19,11 +23,11 @@ In 0.14 and later, all of that is bundled into a single review item which starts
## Alerts and Detections
-Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
+Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring [required zones](/configuration/zones#restricting-alerts-and-detections-to-specific-zones) for them.
:::note
-Alerts and detections categorize the tracked objects in review items, but Frigate must first detect those objects with your configured object detector (Coral, OpenVINO, etc). By default, the object tracker only detects `person`. Setting `labels` for `alerts` and `detections` does not automatically enable detection of new objects. To detect more than `person`, you should add the following to your config:
+Alerts and detections categorize the tracked objects in review items, but Frigate must first detect those objects with your configured object detector (Coral, OpenVINO, etc). By default, the object tracker only detects `person`. Setting `labels` for `alerts` and `detections` does not automatically enable detection of new objects. To detect more than `person`, you should add more labels via or and select your camera. Alternatively, add the following to your config:
```yaml
objects:
@@ -38,7 +42,17 @@ See the [objects documentation](objects.md) for the list of objects that Frigate
## Restricting alerts to specific labels
-By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:
+By default a review item will only be marked as an alert if a person or car is detected. Configure the alert labels to include any object or audio label.
+
+
+
+
+Navigate to or and select your camera.
+
+Expand **Alerts config** and configure which labels and zones should generate alerts.
+
+
+
```yaml
# can be overridden at the camera level
@@ -52,10 +66,23 @@ review:
- speech
```
+
+
+
## Restricting detections to specific labels
By default all detections that do not qualify as an alert qualify as a detection. However, detections can further be filtered to only include certain labels or certain zones.
+
+
+
+Navigate to or and select your camera.
+
+Expand **Detections config** and configure which labels should qualify as detections.
+
+
+
+
```yaml
# can be overridden at the camera level
review:
@@ -65,11 +92,23 @@ review:
- dog
```
+
+
+
## Excluding a camera from alerts or detections
-To exclude a specific camera from alerts or detections, simply provide an empty list to the alerts or detections field _at the camera level_.
+To exclude a specific camera from alerts or detections, provide an empty list to the alerts or detections labels field at the camera level.
-For example, to exclude objects on the camera _gatecamera_ from any detections, include this in your config:
+For example, to exclude objects on the camera _gatecamera_ from any detections:
+
+
+
+
+1. Navigate to and select the **gatecamera** camera.
+ - Expand **Detections config** and turn off all of the object label switches.
+
+
+
```yaml {3-5}
cameras:
@@ -79,6 +118,34 @@ cameras:
labels: []
```
+
+
+
+## Categorizing manual events
+
+Events created with the [create manual event API](../integrations/api/create-event-events-camera-name-label-create-post.api.mdx) are categorized with the same label lists, using the label from the request path:
+
+1. If alerts are enabled and the label is listed in `review -> alerts -> labels`, the review item is an alert.
+2. Otherwise, if detections are enabled and the label is listed in `review -> detections -> labels`, the review item is a detection.
+3. If the label is in neither list, the review item is an alert, or no review item is created if alerts are disabled.
+
+This means manual events are alerts unless you explicitly list their label as a detection label. For example, to have PIR sensors create detections instead of alerts, post to `/api/events/front_door/pir_sensor/create` with the following config:
+
+```yaml {5-7}
+cameras:
+ front_door:
+ review:
+ detections:
+ labels:
+ - pir_sensor
+```
+
+:::note
+
+Required zones do not apply to manual events, since they are created through the API rather than by the object tracker. Setting `review -> alerts -> labels` to an empty list also does not stop manual events from becoming alerts, as a label in neither list still falls back to an alert.
+
+:::
+
## Restricting review items to specific zones
By default a review item will be created if any `review -> alerts -> labels` and `review -> detections -> labels` are detected anywhere in the camera frame. You will likely want to configure review items to only be created when the object enters an area of interest, [see the zone docs for more information](./zones.md#restricting-alerts-and-detections-to-specific-zones)
@@ -88,3 +155,7 @@ By default a review item will be created if any `review -> alerts -> labels` and
Because zones don't apply to audio, audio labels will always be marked as a detection by default.
:::
+
+## Reviewing Motion
+
+The Review page can also surface periods of motion that didn't produce a tracked object, and lets you search past recordings for motion in a region you draw. See [Reviewing Motion](/usage/review#reviewing-motion) in the Usage docs for how to use **Motion Previews** and **Motion Search**, and [Tuning Motion Detection](motion_detection.md) for configuring the underlying motion detector.
diff --git a/docs/docs/configuration/semantic_search.md b/docs/docs/configuration/semantic_search.md
index 19346454bd..30a60d505f 100644
--- a/docs/docs/configuration/semantic_search.md
+++ b/docs/docs/configuration/semantic_search.md
@@ -3,12 +3,22 @@ id: semantic_search
title: Semantic Search
---
-Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_, numerical vector representations, for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.
Frigate uses models from [Jina AI](https://huggingface.co/jinaai) to create and save embeddings to Frigate's database. All of this runs locally.
Semantic Search is accessed via the _Explore_ view in the Frigate UI.
+:::info
+
+Semantic search requires a one-time internet connection to download embedding models from HuggingFace. Once cached, models work fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+
+:::
+
## Minimum System Requirements
Semantic Search works by running a large AI model locally on your system. Small or underpowered systems like a Raspberry Pi will not run Semantic Search reliably or at all.
@@ -19,7 +29,17 @@ For best performance, 16GB or more of RAM and a dedicated GPU are recommended.
## Configuration
-Semantic Search is disabled by default, and must be enabled in your config file or in the UI's Enrichments Settings page before it can be used. Semantic Search is a global configuration setting.
+Semantic Search is disabled by default and must be enabled before it can be used. Semantic Search is a global configuration setting.
+
+
+
+
+Navigate to .
+
+- Set **Enable semantic search** to on
+
+
+
```yaml
semantic_search:
@@ -27,6 +47,9 @@ semantic_search:
reindex: False
```
+
+
+
:::tip
The embeddings database can be re-indexed from the existing tracked objects in your database by pressing the "Reindex" button in the Enrichments Settings in the UI or by adding `reindex: True` to your `semantic_search` configuration and restarting Frigate. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing.
@@ -41,7 +64,20 @@ The [V1 model from Jina](https://huggingface.co/jinaai/jina-clip-v1) has a visio
The V1 text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the object description docs](/configuration/genai/objects.md) for more information on how to automatically generate tracked object descriptions.
-Differently weighted versions of the Jina models are available and can be selected by setting the `model_size` config option as `small` or `large`:
+Differently weighted versions of the Jina models are available and can be selected by setting the model size.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------------------------------ | -------------------------------------------------------------------------- |
+| **Semantic search model or GenAI provider name** | Select `jinav1` to use the Jina AI CLIP V1 model |
+| **Model size** | `small` (quantized, CPU-friendly) or `large` (full model, GPU-accelerated) |
+
+
+
```yaml
semantic_search:
@@ -50,6 +86,9 @@ semantic_search:
model_size: small
```
+
+
+
- Configuring the `large` model employs the full Jina model and will automatically run on the GPU if applicable.
- Configuring the `small` model employs a quantized version of the Jina model that uses less RAM and runs on CPU with a very negligible difference in embedding quality.
@@ -59,7 +98,20 @@ Frigate also supports the [V2 model from Jina](https://huggingface.co/jinaai/jin
V2 offers only a 3% performance improvement over V1 in both text-image and text-text retrieval tasks, an upgrade that is unlikely to yield noticeable real-world benefits. Additionally, V2 has _significantly_ higher RAM and GPU requirements, leading to increased inference time and memory usage. If you plan to use V2, ensure your system has ample RAM and a discrete GPU. CPU inference (with the `small` model) using V2 is not recommended.
-To use the V2 model, update the `model` parameter in your config:
+To use the V2 model, set the model to `jinav2`.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------------------------------ | ----------------------------------------------------- |
+| **Semantic search model or GenAI provider name** | Select `jinav2` to use the Jina AI CLIP V2 model |
+| **Model size** | `large` is recommended for V2 (requires discrete GPU) |
+
+
+
```yaml
semantic_search:
@@ -68,6 +120,9 @@ semantic_search:
model_size: large
```
+
+
+
For most users, especially native English speakers, the V1 model remains the recommended choice.
:::note
@@ -76,10 +131,74 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
:::
+### GenAI Provider
+
+Frigate can use a GenAI provider for semantic search embeddings when that provider has the `embeddings` role. Currently, only **llama.cpp** supports multimodal embeddings (both text and images).
+
+To use llama.cpp for semantic search:
+
+1. Configure a GenAI provider with `embeddings` in its `roles`.
+2. Set the semantic search model to the GenAI config key (e.g. `default`).
+3. Start the llama.cpp server with `--embeddings` and `--mmproj` for image support.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------------------------------ | ---------------------------------------------------------------------------------------------- |
+| **Semantic search model or GenAI provider name** | Set to the GenAI config key (e.g. `default`) to use a configured GenAI provider for embeddings |
+
+The GenAI provider must also be configured with the `embeddings` role under .
+
+
+
+
+```yaml
+genai:
+ default:
+ provider: llamacpp
+ base_url: http://localhost:8080
+ model: your-model-name
+ roles:
+ - embeddings
+ - descriptions
+ - chat
+
+semantic_search:
+ enabled: True
+ model: default
+```
+
+
+
+
+The llama.cpp server must be started with `--embeddings` for the embeddings API, and a multi-modal embeddings model. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for details.
+
+:::note
+
+Switching between Jina models and a GenAI provider requires reindexing. Embeddings from different backends are incompatible.
+
+:::
+
### GPU Acceleration
The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
+
+
+
+Navigate to .
+
+| Field | Description |
+| -------------- | ---------------------------------------------------------------------- |
+| **Model size** | Set to `large` to enable GPU acceleration |
+| **Device** | (Optional) Specify a GPU device index in a multi-GPU system (e.g. `0`) |
+
+
+
+
```yaml
semantic_search:
enabled: True
@@ -88,6 +207,9 @@ semantic_search:
device: 0
```
+
+
+
:::info
If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU will be detected and used automatically.
@@ -100,16 +222,11 @@ See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_
## Usage and Best Practices
-1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
-2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
-3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the the ones without them are more relevant.
-4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
-5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
-6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
+For tips on getting the best results from Semantic Search (choosing between thumbnail and description search, phrasing queries effectively, and combining search with the other Explore filters), see [Usage and best practices](/usage/explore#usage-and-best-practices) in the Usage docs.
## Triggers
-Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes a specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
+Triggers utilize Semantic Search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
:::note
@@ -119,16 +236,15 @@ Semantic Search must be enabled to use Triggers.
### Configuration
-Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires - `notification`, `sub_label`, and `attribute`.
+Triggers are defined within the `semantic_search` configuration for each camera. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires - `notification`, `sub_label`, and `attribute`.
Triggers are best configured through the Frigate UI.
#### Managing Triggers in the UI
-1. Navigate to the **Settings** page and select the **Triggers** tab.
-2. Choose a camera from the dropdown menu to view or manage its triggers.
-3. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
-4. In the **Create Trigger** wizard:
+1. Navigate to and select a camera from the dropdown menu.
+2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
+3. In the **Create Trigger** wizard:
- Enter a **Name** for the trigger (e.g., "Red Car Alert").
- Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
- Select the **Type** (`Thumbnail` or `Description`).
@@ -139,14 +255,14 @@ Triggers are best configured through the Frigate UI.
If native webpush notifications are enabled, check the `Send Notification` box to send a notification.
Check the `Add Sub Label` box to add the trigger's friendly name as a sub label to any triggering tracked objects.
Check the `Add Attribute` box to add the trigger's internal ID (e.g., "red_car_alert") to a data attribute on the tracked object that can be processed via the API or MQTT.
-5. Save the trigger to update the configuration and store the embedding in the database.
+4. Save the trigger to update the configuration and store the embedding in the database.
When a trigger fires, the UI highlights the trigger with a blue dot for 3 seconds for easy identification. Additionally, the UI will show the last date/time and tracked object ID that activated your trigger. The last triggered timestamp is not saved to the database or persisted through restarts of Frigate.
### Usage and Best Practices
1. **Thumbnail Triggers**: Select a representative image (event ID) from the Explore page that closely matches the object you want to detect. For best results, choose images where the object is prominent and fills most of the frame.
-2. **Description Triggers**: Write concise, specific text descriptions (e.g., "Person in a red jacket") that align with the tracked object’s description. Avoid vague terms to improve matching accuracy.
+2. **Description Triggers**: Write concise, specific text descriptions (e.g., "Person in a red jacket") that align with the tracked object's description. Avoid vague terms to improve matching accuracy.
3. **Threshold Tuning**: Adjust the threshold to balance sensitivity and specificity. A higher threshold (e.g., 0.8) requires closer matches, reducing false positives but potentially missing similar objects. A lower threshold (e.g., 0.6) is more inclusive but may trigger more often.
4. **Using Explore**: Use the context menu or right-click / long-press on a tracked object in the Grid View in Explore to quickly add a trigger based on the tracked object's thumbnail.
5. **Editing triggers**: For the best experience, triggers should be edited via the UI. However, Frigate will ensure triggers edited in the config will be synced with triggers created and edited in the UI.
@@ -161,6 +277,6 @@ When a trigger fires, the UI highlights the trigger with a blue dot for 3 second
#### Why can't I create a trigger on thumbnails for some text, like "person with a blue shirt" and have it trigger when a person with a blue shirt is detected?
-TL;DR: Text-to-image triggers aren’t supported because CLIP can confuse similar images and give inconsistent scores, making automation unreliable. The same word–image pair can give different scores and the score ranges can be too close together to set a clear cutoff.
+TL;DR: Text-to-image triggers aren't supported because CLIP can confuse similar images and give inconsistent scores, making automation unreliable. The same word-image pair can give different scores and the score ranges can be too close together to set a clear cutoff.
-Text-to-image triggers are not supported due to fundamental limitations of CLIP-based similarity search. While CLIP works well for exploratory, manual queries, it is unreliable for automated triggers based on a threshold. Issues include embedding drift (the same text–image pair can yield different cosine distances over time), lack of true semantic grounding (visually similar but incorrect matches), and unstable thresholding (distance distributions are dataset-dependent and often too tightly clustered to separate relevant from irrelevant results). Instead, it is recommended to set up a workflow with thumbnail triggers: first use text search to manually select 3–5 representative reference tracked objects, then configure thumbnail triggers based on that visual similarity. This provides robust automation without the semantic ambiguity of text to image matching.
+Text-to-image triggers are not supported due to fundamental limitations of CLIP-based similarity search. While CLIP works well for exploratory, manual queries, it is unreliable for automated triggers based on a threshold. Issues include embedding drift (the same text-image pair can yield different cosine distances over time), lack of true semantic grounding (visually similar but incorrect matches), and unstable thresholding (distance distributions are dataset-dependent and often too tightly clustered to separate relevant from irrelevant results). Instead, it is recommended to set up a workflow with thumbnail triggers: first use text search to manually select 3-5 representative reference tracked objects, then configure thumbnail triggers based on that visual similarity. This provides robust automation without the semantic ambiguity of text to image matching.
diff --git a/docs/docs/configuration/snapshots.md b/docs/docs/configuration/snapshots.md
index 01c034a040..b2aba9463f 100644
--- a/docs/docs/configuration/snapshots.md
+++ b/docs/docs/configuration/snapshots.md
@@ -3,31 +3,146 @@ id: snapshots
title: Snapshots
---
-Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `-.jpg`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
-Snapshots are accessible in the UI in the Explore pane. This allows for quick submission to the Frigate+ service.
+A snapshot is a single still image that captures a tracked object at its best moment: the clearest frame Frigate saw while following that object across the scene. Unlike a [recording](./record.md), which is continuous video, a snapshot is one representative image saved per tracked object once tracking ends.
-To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones)
+When snapshots are enabled, Frigate saves one image to `/media/frigate/clips` for each tracked object, named `--clean.webp`. A clean image is always stored without any annotations (no timestamp, bounding boxes, or cropping) so you have an unmodified copy of the original frame. Annotations like bounding boxes and timestamps are applied on demand when a snapshot is requested [via the HTTP API](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx). See [Rendering](#rendering) below.
-Snapshots sent via MQTT are configured in the [config file](/configuration) under `cameras -> your_camera -> mqtt`
+A few things to keep in mind:
+
+- Snapshots are saved per tracked object, so a camera with no detected objects produces no snapshots even if recording is enabled.
+- Snapshots and recordings are configured and retained independently. Enabling one does not enable the other.
+- Snapshots are accessible in the UI in the Explore pane, which allows for quick submission to the Frigate+ service.
+- To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones).
+- Snapshots sent via MQTT are configured separately under the camera MQTT settings, not here.
+
+## Enabling Snapshots
+
+Enable snapshot saving and configure the default settings that apply to all cameras.
+
+
+
+
+Navigate to .
+
+- Set **Enable snapshots** to on
+
+
+
+
+```yaml
+snapshots:
+ enabled: True
+```
+
+
+
+
+To override snapshot settings for a specific camera:
+
+
+
+
+Navigate to and select your camera.
+
+- Set **Enable snapshots** to on
+
+
+
+
+```yaml
+cameras:
+ front_door:
+ snapshots:
+ enabled: True
+```
+
+
+
+
+## Snapshot Options
+
+Configure how snapshots are rendered and stored. These settings control the defaults applied when snapshots are requested via the API.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| ------------------------ | ------------------------------------------------------------------------------ |
+| **Enable snapshots** | Enable or disable saving snapshots for tracked objects |
+| **Timestamp overlay** | Overlay a timestamp on snapshots from API |
+| **Bounding box overlay** | Draw bounding boxes for tracked objects on snapshots from API |
+| **Crop snapshot** | Crop snapshots from API to the detected object's bounding box |
+| **Snapshot height** | Height in pixels to resize snapshots to; leave empty to preserve original size |
+| **Snapshot quality** | Encode quality for saved snapshots (0-100) |
+| **Required zones** | Zones an object must enter for a snapshot to be saved |
+
+
+
+
+```yaml
+snapshots:
+ enabled: True
+ timestamp: False
+ bounding_box: True
+ crop: False
+ height: 175
+ required_zones: []
+ quality: 60
+```
+
+
+
+
+## Snapshot Retention
+
+Configure how long snapshots are retained on disk. Per-object retention overrides allow different retention periods for specific object types.
+
+
+
+
+Navigate to .
+
+| Field | Description |
+| -------------------------------------------------- | ----------------------------------------------------------------------------------- |
+| **Snapshot retention > Default retention** | Number of days to retain snapshots (default: 10) |
+| **Snapshot retention > Object retention > Person** | Per-object overrides for retention days (e.g., keep `person` snapshots for 15 days) |
+
+
+
+
+```yaml
+snapshots:
+ enabled: True
+ retain:
+ default: 10
+ objects:
+ person: 15
+```
+
+
+
## Frame Selection
-Frigate does not save every frame — it picks a single "best" frame for each tracked object and uses it for both the snapshot and clean copy. As the object is tracked across frames, Frigate continuously evaluates whether the current frame is better than the previous best based on detection confidence, object size, and the presence of key attributes like faces or license plates. Frames where the object touches the edge of the frame are deprioritized. The snapshot is written to disk once tracking ends using whichever frame was determined to be the best.
+Frigate does not save every frame. It picks a single "best" frame for each tracked object based on detection confidence, object size, and the presence of key attributes like faces or license plates. Frames where the object touches the edge of the frame are deprioritized. That best frame is written to disk once tracking ends.
-MQTT snapshots are published more frequently — each time a better thumbnail frame is found during tracking, or when the current best image is older than `best_image_timeout` (default: 60s). These use their own annotation settings configured under `cameras -> your_camera -> mqtt`.
+MQTT snapshots are published more frequently: each time a better thumbnail frame is found during tracking, or when the current best image is older than `best_image_timeout` (default: 60s). These use their own annotation settings configured under the camera MQTT settings.
-## Clean Copy
+## Rendering
-Frigate can produce up to two snapshot files per event, each used in different places:
+Frigate stores a single clean snapshot on disk:
-| Version | File | Annotations | Used by |
-| --- | --- | --- | --- |
-| **Regular snapshot** | `-.jpg` | Respects your `timestamp`, `bounding_box`, `crop`, and `height` settings | API (`/api/events//snapshot.jpg`), MQTT (`/
If you have a USB Coral, you will need to add a detectors section to your config.
@@ -216,7 +254,9 @@ If you have a USB Coral, you will need to add a detectors section to your config
Use USB Coral detector
-`docker-compose.yml` (after modifying, you will need to run `docker compose up -d` to apply changes)
+:::note
+
+You need to pass the USB Coral device to the Docker container. Add the following to your `docker-compose.yml` and run `docker compose up -d`:
```yaml {4-6}
services:
@@ -228,6 +268,16 @@ services:
...
```
+:::
+
+
+
+
+Navigate to and add a detector with **Type** `EdgeTPU` and **Device** `usb`.
+
+
+
+
```yaml {3-6,11-12}
mqtt: ...
@@ -244,17 +294,20 @@ cameras:
...
```
+
+
+
More details on available detectors can be found [here](../configuration/object_detectors.md).
-Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they will need to be added according to the [configuration file reference](../configuration/reference.md).
+Restart Frigate and you should start seeing detections for `person`. If you want to track other objects, they can be configured in or via the [configuration file reference](../configuration/advanced/reference.md).
### Step 5: Setup motion masks
-Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. To do this, navigate to the camera in the UI, select "Debug" at the top, and enable "Motion boxes" in the options below the video feed. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.
+Now that you have optimized your configuration for decoding the video stream, you will want to check to see where to implement motion masks. Click on the camera from the main dashboard, then select the gear icon in the top right, enable the [Debug view](/usage/live#the-single-camera-view), and finally enable the switch for Motion Boxes. Watch for areas that continuously trigger unwanted motion to be detected. Common areas to mask include camera timestamps and trees that frequently blow in the wind. The goal is to avoid wasting object detection cycles looking at these areas.
-Now that you know where you need to mask, use the "Mask & Zone creator" in the options pane to generate the coordinates needed for your config file. More information about masks can be found [here](../configuration/masks.md).
+Use the mask editor to draw polygon masks directly on the camera feed. Navigate to and set up a motion mask over the area. More information about masks can be found [here](../configuration/masks.md).
:::warning
@@ -262,7 +315,7 @@ Note that motion masks should not be used to mark out areas where you do not wan
:::
-Your configuration should look similar to this now.
+If you are using YAML to configure Frigate instead of the UI, your configuration should look similar to this now:
```yaml {16-18}
mqtt:
@@ -282,14 +335,24 @@ cameras:
- detect
motion:
mask:
- - 0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432
+ motion_area:
+ friendly_name: "Motion mask"
+ enabled: true
+ coordinates: "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
```
### Step 6: Enable recordings
In order to review activity in the Frigate UI, recordings need to be enabled.
-To enable recording video, add the `record` role to a stream and enable it in the config. If record is disabled in the config, it won't be possible to enable it in the UI.
+
+
+
+1. If you have separate streams for detect and record, navigate to , select your camera, and add a second input with the `record` role pointing to your high-resolution stream
+2. Navigate to (or for a specific camera) and set **Enable recording** to on
+
+
+
```yaml {16-17}
mqtt: ...
@@ -312,6 +375,9 @@ cameras:
motion: ...
```
+
+
+
If you don't have separate streams for detect and record, you would just add the record role to the list on the first input.
:::note
@@ -322,21 +388,20 @@ If you only plan to use Frigate for recording, it is still recommended to define
:::
-By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/reference.md).
+By default, Frigate will retain video of all tracked objects for 10 days. The full set of options for recording can be found [here](../configuration/advanced/reference.md).
### Step 7: Complete config
At this point you have a complete config with basic functionality.
-- View [common configuration examples](../configuration/index.md#common-configuration-examples) for a list of common configuration examples.
-- View [full config reference](../configuration/reference.md) for a complete list of configuration options.
+- View [common configuration examples](../configuration/config.md#common-configuration-examples) for a list of common configuration examples.
+- View [full config reference](../configuration/advanced/reference.md) for a complete list of configuration options.
### Follow up
Now that you have a working install, you can use the following documentation for additional features:
-1. [Configuring go2rtc](configuring_go2rtc.md) - Additional live view options and RTSP relay
-2. [Zones](../configuration/zones.md)
-3. [Review](../configuration/review.md)
-4. [Masks](../configuration/masks.md)
-5. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
+1. [Zones](../configuration/zones.md)
+2. [Review](../configuration/review.md)
+3. [Masks](../configuration/masks.md)
+4. [Home Assistant Integration](../integrations/home-assistant.md) - Integrate with Home Assistant
diff --git a/docs/docs/guides/reverse_proxy.md b/docs/docs/guides/reverse_proxy.md
index 5edfb5c60d..bf33bcc278 100644
--- a/docs/docs/guides/reverse_proxy.md
+++ b/docs/docs/guides/reverse_proxy.md
@@ -10,13 +10,14 @@ A reverse proxy is typically needed if you want to set up Frigate on a custom UR
Before setting up a reverse proxy, check if any of the built-in functionality in Frigate suits your needs:
|Topic|Docs|
|-|-|
-|TLS|Please see the `tls` [configuration option](../configuration/tls.md)|
+|TLS|Please see the `tls` [configuration option](../configuration/tls.md)|
|Authentication|Please see the [authentication](../configuration/authentication.md) documentation|
-|IPv6|[Enabling IPv6](../configuration/advanced.md#enabling-ipv6)
+|IPv6|[Enabling IPv6](../configuration/advanced/system.md#enabling-ipv6)
-**Note about TLS**
-When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."
+**Note about TLS**
+When using a reverse proxy, the TLS session is usually terminated at the proxy, sending the internal request over plain HTTP. If this is the desired behavior, TLS must first be disabled in Frigate, or you will encounter an HTTP 400 error: "The plain HTTP request was sent to HTTPS port."
To disable TLS, set the following in your Frigate configuration:
+
```yml
tls:
enabled: false
@@ -24,18 +25,26 @@ tls:
:::warning
A reverse proxy can be used to secure access to an internal web server, but the user will be entirely reliant on the steps they have taken. You must ensure you are following security best practices.
-This page does not attempt to outline the specific steps needed to secure your internal website.
+This page does not attempt to outline the specific steps needed to secure your internal website.
Please use your own knowledge to assess and vet the reverse proxy software before you install anything on your system.
:::
+## WebSocket support
+
+Frigate relies on WebSockets for real-time communication between the browser and the backend. Features such as camera controls (enabling/disabling a camera, audio, detect, recordings, and other toggles), live stream playback, and other live-updating parts of the UI will not function correctly if WebSocket connections are not proxied.
+
+Your reverse proxy must be configured to forward the `Upgrade` and `Connection` headers so that WebSocket connections can be established. Each proxy example below already includes the directives needed to do this, but if you are adapting your own configuration, ensure these headers are passed through.
+
+Note that some proxies disable WebSocket support by default. For example, Nginx Proxy Manager has a "Websockets Support" toggle that must be enabled.
+
## Proxies
There are many solutions available to implement reverse proxies and the community is invited to help out documenting others through a contribution to this page.
-* [Apache2](#apache2-reverse-proxy)
-* [Nginx](#nginx-reverse-proxy)
-* [Traefik](#traefik-reverse-proxy)
-* [Caddy](#caddy-reverse-proxy)
+- [Apache2](#apache2-reverse-proxy)
+- [Nginx](#nginx-reverse-proxy)
+- [Traefik](#traefik-reverse-proxy)
+- [Caddy](#caddy-reverse-proxy)
## Apache2 Reverse Proxy
@@ -159,7 +168,7 @@ The settings below enabled connection upgrade, sets up logging (optional) and pr
## Traefik Reverse Proxy
-This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.
+This example shows how to add a `label` to the Frigate Docker compose file, enabling Traefik to automatically discover your Frigate instance.
Before using the example below, you must first set up Traefik with the [Docker provider](https://doc.traefik.io/traefik/providers/docker/)
```yml
@@ -203,7 +212,7 @@ This example shows Frigate running under a subdomain with logging and a tls cert
}
frigate.YOUR_DOMAIN.TLD {
- reverse_proxy http://localhost:8971
+ reverse_proxy http://localhost:8971
import tls
import logging frigate.YOUR_DOMAIN.TLD
}
diff --git a/docs/docs/integrations/home-assistant.md b/docs/docs/integrations/home-assistant.md
index 5b9c014377..f5ca05f1a4 100644
--- a/docs/docs/integrations/home-assistant.md
+++ b/docs/docs/integrations/home-assistant.md
@@ -9,6 +9,8 @@ The best way to integrate with Home Assistant is to use the [official integratio
### Preparation
+Frigate itself must be installed and running before setting up the integration. See the [installation documentation](../frigate/installation.md) for details.
+
The Frigate integration requires the `mqtt` integration to be installed and
manually configured first.
@@ -122,7 +124,7 @@ Use `http://:8971` as the URL for the integration so that aut
The above URL assumes you have [disabled TLS](../configuration/tls).
By default, TLS is enabled and Frigate will be using a self-signed certificate. HomeAssistant will fail to connect HTTPS to port 8971 since it fails to verify the self-signed certificate.
-Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be acessible with a valid certificate.
+Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be accessible with a valid certificate.
:::
@@ -195,7 +197,7 @@ For clips to be castable to media devices, audio is required and may need to be
## Camera API
-To disable a camera dynamically
+To turn a camera off (pauses Frigate's processing of the stream; does not persist across Frigate restarts; see [Camera state](/configuration/live#camera-state)):
```
action: camera.turn_off
@@ -204,7 +206,7 @@ target:
entity_id: camera.back_deck_cam # your Frigate camera entity ID
```
-To enable a camera that has been disabled dynamically
+To turn a camera back on:
```
action: camera.turn_on
@@ -213,6 +215,12 @@ target:
entity_id: camera.back_deck_cam # your Frigate camera entity ID
```
+:::note
+
+These actions toggle Frigate's runtime On/Off state. To permanently disable a camera, set its status to **Disabled** in **Settings → Camera Management** in the Frigate UI.
+
+:::
+
## Notification API
Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.
@@ -273,7 +281,7 @@ For advanced usecases, this behavior can be changed with the [RTSP URL
template](#options) option. When set, this string will override the default stream
address that is derived from the default behavior described above. This option supports
[jinja2 templates](https://jinja.palletsprojects.com/) and has the `camera` dict
-variables from [Frigate API](../integrations/api)
+variables from [Frigate API](/integrations/api/frigate-http-api)
available for the template. Note that no Home Assistant state is available to the
template, only the camera dict from Frigate.
diff --git a/docs/docs/integrations/homekit.md b/docs/docs/integrations/homekit.md
index 5954af41ce..a4988fd38f 100644
--- a/docs/docs/integrations/homekit.md
+++ b/docs/docs/integrations/homekit.md
@@ -3,35 +3,100 @@ id: homekit
title: HomeKit
---
-Frigate cameras can be integrated with Apple HomeKit through go2rtc. This allows you to view your camera streams directly in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
+Frigate cameras can be exported to Apple HomeKit through go2rtc. Each exported camera appears as an accessory in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
## Overview
-HomeKit integration is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server to expose your cameras to HomeKit.
+Exporting cameras is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server, so your camera is published to HomeKit as an accessory in its own right.
-## Setup
+:::note
-All HomeKit configuration and pairing should be done through the **go2rtc WebUI**.
+This is the opposite of importing a HomeKit camera. go2rtc can also pair with an existing HomeKit camera (Aqara, Eve, Eufy, and similar) and use it as a stream source, which is what the `add` page of the go2rtc WebUI is for. That page discovers HomeKit accessories on your network and will not list your Frigate cameras. It is not used for exporting.
-### Accessing the go2rtc WebUI
-
-The go2rtc WebUI is available at:
-
-```
-http://:1984
-```
-
-Replace `` with the IP address or hostname of your Frigate server.
-
-### Pairing Cameras
-
-1. Navigate to the go2rtc WebUI at `http://:1984`
-2. Use the `add` section to add a new camera to HomeKit
-3. Follow the on-screen instructions to generate pairing codes for your cameras
+:::
## Requirements
-- Frigate must be accessible on your local network using host network_mode
-- Your iOS device must be on the same network as Frigate
-- Port 1984 must be accessible for the go2rtc WebUI
-- For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc)
+- Frigate must be running with `network_mode: host` so that HomeKit can discover your cameras over mDNS
+- Your Apple device must be on the same network as Frigate
+- Port 1984 must be accessible so you can reach the go2rtc WebUI
+
+HomeKit also places strict limits on the stream itself. go2rtc passes your stream through without resizing or re-encoding it, so the stream you export must already meet these requirements:
+
+- **Video:** H.264 at 1920x1080, 1280x720, or 320x240
+- **Audio:** Opus, mono, 16 kHz
+
+A camera's full resolution stream usually does not qualify. See [Exporting a compatible stream](#exporting-a-compatible-stream) below.
+
+## Configuration
+
+HomeKit settings are stored in `/config/go2rtc_homekit.yml`. This is a separate file from your Frigate config, because go2rtc needs to write your pairings back to it when you pair a device.
+
+Edit it using the go2rtc config editor, which writes to that file directly:
+
+```
+http://:1984/editor.html
+```
+
+Replace `` with the IP address or hostname of your Frigate server. The editor will be empty until you add a HomeKit section, since this file holds only your HomeKit settings and not the rest of your go2rtc config.
+
+:::warning
+
+Do not put the `homekit:` section in the `go2rtc:` section of your Frigate config.
+
+Frigate regenerates that config on every startup, so go2rtc cannot save your pairings to it. Pairing will appear to succeed and then fail after the next restart with `PairVerify with unknown client_id`. If the section exists in both places, your saved pairings are erased on every restart.
+
+:::
+
+Add an entry for each camera you want to export. The key must match the name of a go2rtc stream, and the pin must be 8 digits. This is the number the Home app calls the setup code:
+
+```yaml
+homekit:
+ front_door:
+ name: Front Door
+ pin: "12345678"
+```
+
+If the key does not match a go2rtc stream, go2rtc logs `[homekit] missing stream:` at startup and the camera will not appear in the Home app.
+
+:::note
+
+go2rtc derives each accessory's HomeKit identity from this key, so renaming it later means the camera appears as a new accessory and has to be paired again. Settle on the name before you pair.
+
+:::
+
+Frigate keeps only the `homekit:` section of this file when it starts, so do not store streams or other go2rtc settings in it.
+
+### Exporting a compatible stream
+
+If a camera's stream does not meet the requirements listed above, define a scaled restream in your Frigate config and point HomeKit at that stream instead of the original:
+
+```yaml
+go2rtc:
+ streams:
+ front_door:
+ - rtsp://user:password@192.168.1.50:554/stream
+ front_door_homekit:
+ - "ffmpeg:front_door#video=h264#width=1280#height=720#audio=opus/16000"
+```
+
+```yaml
+# /config/go2rtc_homekit.yml
+homekit:
+ front_door_homekit:
+ name: Front Door
+ pin: "12345678"
+```
+
+Add `#hardware=cuda`, `#hardware=vaapi`, or the appropriate value for your system to transcode using your GPU. Note that NVENC cannot encode H.264 wider than 4096 pixels, so very wide streams must be scaled down as shown above rather than only re-encoded.
+
+## Pairing Cameras
+
+1. Restart Frigate after adding the `homekit:` section
+2. In the Apple Home app, choose **Add Accessory**, then **More options** to enter a code manually
+3. Select your camera and enter the pin you configured as the setup code
+4. Confirm that a `pairings:` list now appears under the camera in `/config/go2rtc_homekit.yml`
+
+Pairings are saved back to that file automatically. If step 4 shows no `pairings:` list, check the Frigate log for `[homekit] can't save`, which means the `homekit:` section is missing from `/config/go2rtc_homekit.yml`.
+
+For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc).
diff --git a/docs/docs/integrations/mqtt.md b/docs/docs/integrations/mqtt.md
index 66775a473c..a7a8740bd5 100644
--- a/docs/docs/integrations/mqtt.md
+++ b/docs/docs/integrations/mqtt.md
@@ -5,13 +5,20 @@ title: MQTT
These are the MQTT messages generated by Frigate. The default topic_prefix is `frigate`, but can be changed in the config file.
+:::info
+
+MQTT requires a network connection to your broker. This is typically local, but will require internet if using a cloud-hosted MQTT broker. See [Network Requirements](/frigate/network_requirements#mqtt) for details.
+
+:::
+
## General Frigate Topics
### `frigate/available`
Designed to be used as an availability topic with Home Assistant. Possible message are:
-"online": published when Frigate is running (on startup)
-"offline": published after Frigate has stopped
+"online": published once Frigate is running and has published its initial state. Note that this is published on every connection to the broker, so it is republished if the broker restarts or the connection drops and recovers, without Frigate itself restarting.
+"stopped": published when Frigate is stopped normally
+"offline": published automatically by the MQTT broker if Frigate disconnects unexpectedly (via MQTT Will Message)
### `frigate/restart`
@@ -159,7 +166,8 @@ Published when a license plate is recognized on a car object. See the [License P
"plate": "123ABC",
"score": 0.95,
"camera": "driveway_cam",
- "timestamp": 1607123958.748393
+ "timestamp": 1607123958.748393,
+ "plate_box": [917, 487, 1029, 529] // box coordinates of the detected license plate in the frame
}
```
@@ -272,11 +280,21 @@ Same data available at `/api/stats` published at a configurable interval.
### `frigate/camera_activity`
-Returns data about each camera, its current features, and if it is detecting motion, objects, etc. Can be triggered by publising to `frigate/onConnect`
+Returns data about each camera, its current features, and if it is detecting motion, objects, etc. Can be triggered by publishing to `frigate/onConnect`
+
+### `frigate/profile/set`
+
+Topic to activate or deactivate a [profile](/configuration/profiles). Publish a profile name to activate it, or `none` to deactivate the current profile.
+
+### `frigate/profile/state`
+
+Topic with the currently active profile name. Published value is the profile name or `none` if no profile is active. This topic is retained.
### `frigate/notifications/set`
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications on and off for all cameras. Expected values are `ON` and `OFF`.
+
+Only available when notifications are enabled in the config. Not persisted across Frigate restarts.
### `frigate/notifications/state`
@@ -290,7 +308,9 @@ Publishes the current health status of each role that is enabled (`audio`, `dete
- `online`: Stream is running and being processed
- `offline`: Stream is offline and is being restarted
-- `disabled`: Camera is currently disabled
+- `disabled`: Camera is currently turned off (either at runtime via the `enabled/set` topic, or persistently via the configuration file). See [Camera state](/configuration/live#camera-state) for the distinction.
+
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
### `frigate//`
@@ -352,15 +372,15 @@ The published value is the detected state class name (e.g., `open`, `closed`, `o
### `frigate//enabled/set`
-Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn Frigate's processing of a camera on or off at runtime. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)). To permanently change the configured value, use **Settings → Global configuration → Camera management** in the Frigate UI. See [Camera state](/configuration/live#camera-state) for the difference between turning a camera off and disabling it.
### `frigate//enabled/state`
-Topic with current state of processing for a camera. Published values are `ON` and `OFF`.
+Topic with current runtime state of processing for a camera. Published values are `ON` and `OFF`.
### `frigate//detect/set`
-Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn object detection for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
### `frigate//detect/state`
@@ -368,15 +388,27 @@ Topic with current state of object detection for a camera. Published values are
### `frigate//audio/set`
-Topic to turn audio detection for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn audio detection for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
### `frigate//audio/state`
Topic with current state of audio detection for a camera. Published values are `ON` and `OFF`.
+### `frigate//audio_transcription/set`
+
+Topic to turn [live audio transcription](/configuration/audio_detectors#live-transcription) for a camera on and off. Expected values are `ON` and `OFF`. Transcribed text is published to `frigate//audio/transcription`.
+
+`ON` is ignored unless audio transcription is enabled in the config for the camera. Unlike the other camera toggles, this one is not persisted across Frigate restarts.
+
+**NOTE:** Requires audio detection and transcription to be enabled
+
+### `frigate//audio_transcription/state`
+
+Topic with current state of live audio transcription for a camera. Published values are `ON` and `OFF`.
+
### `frigate//recordings/set`
-Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
### `frigate//recordings/state`
@@ -384,7 +416,7 @@ Topic with current state of recordings for a camera. Published values are `ON` a
### `frigate//snapshots/set`
-Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`.
+Topic to turn snapshots for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
### `frigate//snapshots/state`
@@ -429,6 +461,30 @@ Topic to adjust motion contour area for a camera. Expected value is an integer.
Topic with current motion contour area for a camera. Published value is an integer.
+### `frigate//motion_mask//set`
+
+Topic to turn a specific motion mask for a camera on and off. Expected values are `ON` and `OFF`.
+
+### `frigate//motion_mask//state`
+
+Topic with current state of a specific motion mask for a camera. Published values are `ON` and `OFF`.
+
+### `frigate//object_mask//set`
+
+Topic to turn a specific object mask for a camera on and off. Expected values are `ON` and `OFF`.
+
+### `frigate//object_mask//state`
+
+Topic with current state of a specific object mask for a camera. Published values are `ON` and `OFF`.
+
+### `frigate//zone//set`
+
+Topic to turn a specific zone for a camera on and off. Expected values are `ON` and `OFF`.
+
+### `frigate//zone//state`
+
+Topic with current state of a specific zone for a camera. Published values are `ON` and `OFF`.
+
### `frigate//review_status`
Topic with current activity status of the camera. Possible values are `NONE`, `DETECTION`, or `ALERT`.
@@ -516,16 +572,20 @@ Topic with current state of the Birdseye mode for a camera. Published values are
### `frigate//notifications/set`
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications for a camera on and off. Expected values are `ON` and `OFF`.
+
+`ON` is ignored unless notifications are enabled in the config for the camera. This is not persisted across Frigate restarts. It is the same control the UI labels **Suspend until restart**.
### `frigate//notifications/state`
-Topic with current state of notifications. Published values are `ON` and `OFF`.
+Topic with current state of notifications. Published values are `ON` and `OFF`. This is the authoritative topic for whether a camera will notify.
### `frigate//notifications/suspend`
-Topic to suspend notifications for a certain number of minutes. Expected value is an integer.
+Topic to suspend notifications for a certain number of minutes. Expected value is an integer. Separate from `notifications/set`: it does not change `notifications/state`, and is ignored while notifications are off.
### `frigate//notifications/suspended`
-Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if notifications are not suspended.
+Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if there is no timed suspension.
+
+`0` does not mean notifications are enabled: `notifications/set` `OFF` clears the timed suspension, so this publishes `0` while `notifications/state` is `OFF`.
diff --git a/docs/docs/integrations/plus.md b/docs/docs/integrations/plus.md
index aa3d78df5b..949a9f49be 100644
--- a/docs/docs/integrations/plus.md
+++ b/docs/docs/integrations/plus.md
@@ -3,8 +3,16 @@ id: plus
title: Frigate+
---
+import NavPath from "@site/src/components/NavPath";
+
For more information about how to use Frigate+ to improve your model, see the [Frigate+ docs](/plus/).
+:::info
+
+Frigate+ requires an active internet connection to communicate with `https://api.frigate.video` for model downloads, image uploads, and annotations. See [Network Requirements](/frigate/network_requirements#frigate) for details.
+
+:::
+
## Setup
### Create an account
@@ -51,7 +59,7 @@ You can view all of your submitted images at [https://plus.frigate.video](https:
Once you have [requested your first model](../plus/first_model.md) and gotten your own model ID, it can be used with a special model path. No other information needs to be configured for Frigate+ models because it fetches the remaining config from Frigate+ automatically.
-You can either choose the new model from the Frigate+ pane in the Settings page of the Frigate UI, or manually set the model at the root level in your config:
+You can either choose the new model from the pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
```yaml
detectors: ...
diff --git a/docs/docs/integrations/third_party_extensions.md b/docs/docs/integrations/third_party_extensions.md
index e0fe835633..64b174a042 100644
--- a/docs/docs/integrations/third_party_extensions.md
+++ b/docs/docs/integrations/third_party_extensions.md
@@ -17,9 +17,13 @@ Please use your own knowledge to assess and vet them before you install anything
The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant dashboard card with deep Frigate integration.
+## [cctvQL](https://github.com/arunrajiah/cctvql)
+
+[cctvQL](https://github.com/arunrajiah/cctvql) is a natural language query layer for Frigate and other CCTV systems. It connects to Frigate's REST API and MQTT broker to let you ask conversational questions about cameras and events (e.g. "Was there motion at the front door last night?"), with support for real-time event streaming, anomaly detection, PTZ control, alert rules, and a Home Assistant custom component.
+
## [Double Take](https://github.com/skrashevich/double-take)
-[Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
+[Double Take](https://github.com/skrashevich/double-take) provides a unified UI and API for processing and training images for facial recognition.
It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
@@ -27,6 +31,10 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
[Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
+## [Frigate Notify Alert](https://github.com/Sysoev86/frigate-notify-alert)
+
+[Frigate Notify Alert](https://github.com/Sysoev86/frigate-notify-alert) sends Frigate events to Telegram as a photo + video media group. It supports multiple camera groups (each notifying its own chat), optional zone filtering (notify only when an object enters a chosen zone), and in-chat buttons to pause notifications for a set time. Works with standalone Frigate over MQTT; Home Assistant not required.
+
## [Frigate Snap-Sync](https://github.com/thequantumphysicist/frigate-snap-sync/)
[Frigate Snap-Sync](https://github.com/thequantumphysicist/frigate-snap-sync/) is a program that works in tandem with Frigate. It responds to Frigate when a snapshot or a review is made (and more can be added), and uploads them to one or more remote server(s) of your choice.
@@ -35,13 +43,17 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
[Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.
+## [kiosk-monitor](https://github.com/extremeshok/kiosk-monitor)
+
+[kiosk-monitor](https://github.com/extremeshok/kiosk-monitor) is a Raspberry Pi watchdog that runs Chromium fullscreen on a Frigate dashboard (optionally with VLC on a second monitor for an RTSP camera stream), auto-restarts on frozen screens or unreachable URLs, and ships a Birdseye-aware Chromium helper that auto-sizes the grid to the display.
+
## [Periscope](https://github.com/maksz42/periscope)
[Periscope](https://github.com/maksz42/periscope) is a lightweight Android app that turns old devices into live viewers for Frigate. It works on Android 2.2 and above, including Android TV. It supports authentication and HTTPS.
## [Scrypted - Frigate bridge plugin](https://github.com/apocaliss92/scrypted-frigate-bridge)
-[Scrypted - Frigate bridge](https://github.com/apocaliss92/scrypted-frigate-bridge) is an plugin that allows to ingest Frigate detections, motion, videoclips on Scrypted as well as provide templates to export rebroadcast configurations on Frigate.
+[Scrypted - Frigate bridge](https://github.com/apocaliss92/scrypted-frigate-bridge) is a plugin that allows you to ingest Frigate detections, motion, videoclips on Scrypted as well as provide templates to export rebroadcast configurations on Frigate.
## [Strix](https://github.com/eduard256/Strix)
diff --git a/docs/docs/plus/annotating.md b/docs/docs/plus/annotating.md
index dc8e571be4..3725ab5c2c 100644
--- a/docs/docs/plus/annotating.md
+++ b/docs/docs/plus/annotating.md
@@ -19,7 +19,7 @@ For the best results, follow these guidelines. You may also want to review the d
## AI suggested labels
-If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for you camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.
+If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for your camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.
diff --git a/docs/docs/plus/faq.md b/docs/docs/plus/faq.md
index 2bcebf1d4b..54b07fb8a4 100644
--- a/docs/docs/plus/faq.md
+++ b/docs/docs/plus/faq.md
@@ -31,10 +31,9 @@ Note that professional installers are fine under standard subscriptions when eac
### Why can't I submit images to Frigate+?
-If you've configured your API key and the Frigate+ Settings page in the UI shows that the key is active, you need to ensure that you've enabled both snapshots and `clean_copy` snapshots for the cameras you'd like to submit images for. Note that `clean_copy` is enabled by default when snapshots are enabled.
+If you've configured your API key and the Frigate+ Settings page in the UI shows that the key is active, you need to ensure that snapshots are enabled for the cameras you'd like to submit images for.
```yaml
snapshots:
enabled: true
- clean_copy: true
```
diff --git a/docs/docs/plus/first_model.md b/docs/docs/plus/first_model.md
index e9523f6b98..98095554b5 100644
--- a/docs/docs/plus/first_model.md
+++ b/docs/docs/plus/first_model.md
@@ -3,6 +3,10 @@ id: first_model
title: Requesting your first model
---
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
## Step 1: Upload and annotate your images
Before requesting your first model, you will need to upload and verify at least 10 images to Frigate+. The more images you upload, annotate, and verify the better your results will be. Most users start to see very good results once they have at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. Refer to the [integration docs](../integrations/plus.md#generate-an-api-key) for instructions on how to easily submit images to Frigate+ directly from Frigate.
@@ -16,13 +20,21 @@ For more detailed recommendations, you can refer to the docs on [annotating](./a
Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). If you are unsure which type to request, you can test the base model for each version from the "Base Models" tab. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
-## Step 3: Set your model id in the config
+## Step 3: Set your model
You will receive an email notification when your Frigate+ model is ready.
Models available in Frigate+ can be used with a special model path. No other information needs to be configured because it fetches the remaining config from Frigate+ automatically.
+
+
+
+Navigate to . In the **Detection Model** section, choose the **Frigate+** tab. Select your new Frigate+ model from the **Available Frigate+ models** dropdown, then click **Save**. Restart Frigate to apply the change.
+
+
+
+
```yaml
detectors: ...
@@ -30,22 +42,46 @@ model:
path: plus://
```
-:::note
-
-Model IDs are not secret values and can be shared freely. Access to your model is protected by your API key.
-
-:::
-
:::tip
When setting the plus model id, all other fields should be removed as these are configured automatically with the Frigate+ model config
:::
+
+
+
+:::note
+
+Model IDs are not secret values and can be shared freely. Access to your model is protected by your API key.
+
+:::
+
## Step 4: Adjust your object filters for higher scores
Frigate+ models generally have much higher scores than the default model provided in Frigate. You will likely need to increase your `threshold` and `min_score` values. Here is an example of how these values can be refined, but you should expect these to evolve as your model improves. For more information about how `threshold` and `min_score` are related, see the docs on [object filters](../configuration/object_filters.md#object-scores).
+
+
+
+Navigate to . Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
+
+| Object | Min Score | Threshold |
+| ----------------- | --------- | --------- |
+| **dog** | .7 | .9 |
+| **cat** | .65 | .8 |
+| **face** | .7 | |
+| **package** | .65 | .9 |
+| **license_plate** | .6 | |
+| **amazon** | .75 | |
+| **ups** | .75 | |
+| **fedex** | .75 | |
+| **person** | .65 | .85 |
+| **car** | .65 | .85 |
+
+
+
+
```yaml
objects:
filters:
@@ -75,3 +111,6 @@ objects:
min_score: .65
threshold: .85
```
+
+
+
diff --git a/docs/docs/troubleshooting/common_errors.md b/docs/docs/troubleshooting/common_errors.md
new file mode 100644
index 0000000000..6edb6577f1
--- /dev/null
+++ b/docs/docs/troubleshooting/common_errors.md
@@ -0,0 +1,240 @@
+---
+id: common_errors
+title: Common Error Messages
+---
+
+import FaqItem from "@site/src/components/FaqItem";
+
+This page is an index of error messages you might see in Frigate's logs, what each one means, and where to go next. It is organized by the kind of problem, not by which component logged the message.
+
+Two things to know before you start:
+
+- **Many of these messages come from FFmpeg, go2rtc, GPU drivers, or the operating system, not from Frigate itself.** Frigate captures and re-logs their output, so the log level shown in the Frigate UI does not always reflect the original severity.
+- **Wrapped errors put the real cause on the next line.** When Frigate logs a generic message like `Error occurred when attempting to maintain recording cache`, the actual exception is logged immediately after it. When a camera's FFmpeg process exits, Frigate logs `The following ffmpeg logs include the last 100 lines prior to exit` and dumps that camera's FFmpeg output. Always read those lines, they are where the answer usually is.
+
+## Camera connection and streams
+
+
+
+These are FFmpeg errors about reaching the camera (or the go2rtc restream). `Connection refused` and `No route to host` mean nothing is listening at that address or the host is unreachable; `401 Unauthorized` is wrong credentials; `404 Not Found` is a wrong stream path (or a `restream` input pointing at a go2rtc stream name that does not exist). A camera that has hit its concurrent-connection limit can also return `refused` or `401` on a URL that works in VLC.
+
+See [go2rtc troubleshooting](/troubleshooting/go2rtc#1-read-the-go2rtc-logs) for how to isolate the stream.
+
+
+
+
+
+FFmpeg is running but has stopped delivering video for 20 seconds, so Frigate's camera watchdog restarts it. The stream connected at least once, then went quiet: a camera reboot, a network drop, the camera evicting the connection, or a stalled decoder. If it repeats on a loop, the stream is unstable.
+
+
+
+
+
+The detect FFmpeg process exited on its own. This message is only the notification; the cause is in the 100 FFmpeg log lines Frigate dumps right after it (look for a `Failed to sync surface`, `Connection refused`, codec, or audio error in that block). Related watchdog messages include ` exceeded fps limit`, which means the camera is delivering frames faster than `detect.fps` (usually a camera whose real frame rate differs from what is configured).
+
+
+
+
+
+These are FFmpeg messages indicating the camera sent packets with out-of-order timestamps, either on the video or the audio stream. Timestamp jitter like this is common with WiFi cameras and restreamed or proxied sources; other causes are a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps. A sustained flood of these messages usually precedes the stream stalling and the watchdog restarting FFmpeg.
+
+In most cases, the fix is to improve the network, reduce system resource usage, or switch to non-WiFi cameras. In general, WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+
+On the video stream, this can affect recordings: because they are copied without re-encoding, FFmpeg cannot fix the timestamps, and the segment muxer often splits early, producing one-second segments and a cache backlog. See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
+
+On the audio stream, the messages can come from the output's audio encoding. If the audio stream is the problem, it may help to have go2rtc transcode it by adding `#audio=aac` to the camera's go2rtc stream to produce clean timestamps for everything consuming the restream.
+
+
+
+
+
+An FFmpeg message meaning RTP packets arrived out of sequence, which almost always means the stream is using UDP transport. Frigate's RTSP presets force TCP, so seeing this points at a custom `input_args`, `preset-rtsp-udp`, or a go2rtc source that is not using TCP. Switch to TCP unless your camera is [UDP-only](/configuration/camera_specific#udp-only-cameras).
+
+
+
+
+
+FFmpeg decoder messages meaning the received video bitstream was incomplete or damaged. A few of these at every stream start are normal (the decoder connected before the first keyframe) and Frigate discards them. A continuous stream of them means real packet loss, from Wi-Fi or a saturated link, an overloaded camera, or an FFmpeg restart loop caused by another problem. Fix the underlying instability rather than the message.
+
+
+
+
+
+An FFmpeg message meaning it probed the stream but never saw enough decodable video to determine the frame size, often because the probe window ended before the first keyframe on a long-GOP stream, or because the stream is not delivering usable video. If it is a Reolink HTTP stream, use `preset-http-reolink`, which raises the probe size for exactly this case.
+
+
+
+## Recording
+
+
+
+Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
+
+See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
+
+
+
+
+
+A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
+
+See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
+
+
+
+
+
+Some camera audio codecs (G.711 variants such as `pcm_alaw` and `pcm_mulaw`) cannot be stored in an MP4 container, so segments never finalize even though live view works.
+
+See [Recordings: incompatible audio codec](/troubleshooting/recordings#incompatible-audio-codec-recordings-silently-fail-to-save) for the FFmpeg preset that transcodes the audio to AAC.
+
+
+
+
+
+A generic wrapper; the real exception is on the next log line. Frequently it is `[Errno 28] No space left on device` or `[Errno 17] File exists` on a network share.
+
+See [Recordings cache warnings and errors](/troubleshooting/recordings#i-see-the-message-error--error-occurred-when-attempting-to-maintain-recording-cache), which covers this message and the common `Errno` cases.
+
+
+
+## Hardware acceleration
+
+
+
+A VAAPI/QSV hardware frame-sync failure between FFmpeg and the GPU driver, not a Frigate bug. It usually appears when the detect stream is being scaled or decoded on the GPU.
+
+See [GPU: Failed to download frame: -5](/troubleshooting/gpu#failed-to-download-frame--5), which lists the fixes in order (switch VAAPI/QSV preset, change `LIBVA_DRIVER_NAME`, use an H.264 substream, match detect resolution and fps to the stream).
+
+
+
+
+
+Both mean the GPU ran out of decode surfaces: `No decoder surfaces left` is NVIDIA NVDEC, `Can't allocate a surface` is Intel QSV. This is surface-pool exhaustion, typically from too many concurrent hardware-decoded cameras on one GPU (consumer NVIDIA cards have a driver-enforced limit on simultaneous decode sessions). Reduce the number of cameras decoding on that GPU, decode some on the CPU, or move to hardware without the session cap.
+
+
+
+
+
+This comes from the NVIDIA container runtime while starting the container, not from Frigate, and the container never starts. The NVIDIA driver is not loaded on the host. Confirm `nvidia-smi` works on the host itself (not inside the container) before troubleshooting Frigate. In a VM or LXC, the driver must be available inside the guest. See [Hardware: Nvidia GPU](/configuration/hardware_acceleration_video).
+
+
+
+## Detectors and models
+
+
+
+The process was killed by the CPU for executing an unsupported instruction. There are two distinct causes in Frigate:
+
+- **A Coral EdgeTPU** on a newer kernel with an outdated gasket driver. See [EdgeTPU: Illegal instruction](/troubleshooting/edgetpu#attempting-to-load-tpu-as-pci--fatal-python-error-illegal-instruction).
+- **A CPU without AVX/AVX2**, when enabling semantic search, face recognition, license plate recognition, classification, or audio transcription. These features use libraries compiled with AVX and crash immediately on CPUs that lack it (commonly Intel Celeron/Pentium before the 2020 Tiger Lake generation). See the [CPU requirements](/frigate/planning_setup#cpu).
+
+
+
+
+
+ONNX Runtime could not parse the model file. The file exists but its contents are not a valid ONNX model, usually a corrupted or interrupted download in `model_cache`, or the wrong file pointed at by `model.path`. Delete the cached model file so Frigate re-downloads it, and confirm `model.path` points at an actual `.onnx` model. See [ONNX detector configuration](/configuration/object_detectors#onnx).
+
+
+
+
+
+ONNX Runtime CUDA errors. `999` (`cudaErrorUnknown`) is a general, unrecoverable CUDA context failure, usually a driver/runtime version mismatch between the host and the container or a GPU in a bad state. `901` is a CUDA-graph capture error, which points at a custom model whose operations are not capture-safe. For `999`, align the host driver with the container's CUDA version and confirm the GPU is healthy.
+
+
+
+
+
+OpenVINO could not find the configured device (usually `GPU` or `NPU`). Most often the `/dev/dri` render node is not passed into the container, or the wrong render node is mapped when an iGPU and a discrete GPU coexist.
+
+See [GPU: no supported devices found](/troubleshooting/gpu#cant-get-optimization_capabilities-property-as-no-supported-devices-found).
+
+
+
+## Memory and storage
+
+
+
+Frigate ran out of shared memory (`/dev/shm`). The container's `shm_size` is too small for the number and resolution of your detect streams, or you added cameras after startup without increasing it.
+
+See [Calculating required shm-size](/frigate/installation#calculating-required-shm-size). If you cannot increase `shm_size`, lowering the `SHM_MAX_FRAMES` environment variable reduces how many frames Frigate buffers per camera.
+
+
+
+
+
+A filesystem is full: the recordings volume (`/media/frigate`), the cache tmpfs (`/tmp/cache`), or `/dev/shm`. Check which one, and note that inode exhaustion can produce this while `df -h` still shows free space.
+
+See [Recordings: No space left on device](/troubleshooting/recordings#i-see-the-message-error--error-occurred-when-attempting-to-maintain-recording-cache).
+
+
+
+
+
+A silent exit is usually the host or container out-of-memory killer. Because `/dev/shm` and `/tmp/cache` are memory-backed, they count against the container's memory limit, so aggressive shm or cache sizing can trigger it. Give the container more memory, or reduce shm/cache sizing, and check the host's OOM messages (`dmesg`).
+
+
+
+## Database
+
+
+
+SQLite could not acquire the write lock. Frigate's timeout already scales with camera count, so under normal local-disk operation this essentially only happens when the database is on a network share (SMB/NFS), where file locking is unreliable, or when two instances point at the same file.
+
+See [Database is locked](/troubleshooting/faqs#error-database-is-locked).
+
+
+
+
+
+The SQLite database file is corrupted, typically after hard power loss, a network-share database, or a filesystem with unsafe write semantics. Frigate does not repair it automatically, but the database can usually be recovered by hand.
+
+**Stop Frigate first**, then work on the database file directly (by default `/config/frigate.db`). Start by checking what is actually wrong:
+
+```bash
+sqlite3 frigate.db "PRAGMA integrity_check;"
+```
+
+If the only problems reported are index-related (lines such as `row 14 missing from index recordings_path` or `non-unique entry in index ...`), rebuilding the indexes is usually enough and is the least destructive fix:
+
+```bash
+sqlite3 frigate.db "REINDEX;"
+```
+
+If the integrity check reports page or byte-level corruption instead (for example `Multiple uses for byte 2706 of page 142272`), dump the readable contents into a new database:
+
+```bash
+# dump what can still be read
+sqlite3 frigate.db .dump > frigate.dump
+
+# keep the corrupt file, then rebuild from the dump
+mv frigate.db frigate.db.bak
+cat frigate.dump | sqlite3 frigate.db
+
+# confirm the rebuilt database is clean, this should print "ok"
+sqlite3 frigate.db "PRAGMA integrity_check;"
+```
+
+Rows stored in the corrupted pages cannot be recovered, so expect to lose some tracked objects, review items, or thumbnails. Recordings themselves are files on disk and are not affected.
+
+As a last resort, stop Frigate, delete `frigate.db`, and restart. Frigate recreates it, but existing recordings lose all of their metadata. If a `backup.db` exists next to your database, Frigate wrote it before the last schema migration and restoring it recovers everything up to that point.
+
+Repeat corruption usually points at the underlying storage: move the database off a network share, and on Raspberry Pi check power delivery and the SD card or SSD.
+
+
+
+## Startup and web access
+
+
+
+When your config fails validation at startup, Frigate prints the validation errors (with line numbers), then starts in **safe mode**: a minimal configuration with no cameras and MQTT disabled, so the UI stays reachable. In safe mode the only available page is the Config Editor, which shows the validation errors so you can fix them, then save and restart. Note that recording retention and storage cleanup do **not** run while in safe mode, so do not leave a low-disk system sitting in it.
+
+`Unable to start Frigate in safe mode` means even the minimal config failed, which points at an error in your `auth`, `proxy`, or `database` section, or a config file that is not valid YAML at all. Safe mode is not sticky; fix the config and restart and Frigate returns to normal.
+
+
+
+
+
+The web server is up but the Frigate backend (port 5001) is not answering yet. By far the most common reason is that the page was loaded during startup: the API binds last, after database migrations (which can take minutes on a large database), model downloads, and process startup, while the web server is already serving. Wait for startup to finish. If it persists, the backend has failed to start, and the reason is earlier in the logs. This also explains a `connection refused to 127.0.0.1:5001` seen while loading `/ws`, because every authenticated request first makes an auth subrequest to that port.
+
+
diff --git a/docs/docs/troubleshooting/cpu.md b/docs/docs/troubleshooting/cpu.md
index a9f449ad88..50a38a61a7 100644
--- a/docs/docs/troubleshooting/cpu.md
+++ b/docs/docs/troubleshooting/cpu.md
@@ -3,7 +3,31 @@ id: cpu
title: High CPU Usage
---
-High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+High CPU usage can impact Frigate's performance and responsiveness. This guide explains how to interpret the CPU values Frigate reports and outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+
+## Understanding Frigate's Reported CPU Usage
+
+Frigate's CPU percentages often look much higher than what the host reports. Usually both numbers are correct and are simply measured against different denominators, so confirm you actually have a problem before tuning anything.
+
+### Per-process values are relative to a single core
+
+The values Frigate reports for FFmpeg, capture, detect, detector, and other processes follow the same convention as `top`: 100% means one CPU core is fully saturated, not that the whole system is saturated. A multithreaded process such as FFmpeg can legitimately report well over 100%.
+
+Host and hypervisor tools instead report a percentage of the machine's total capacity across all cores. This includes `docker stats`, the `htop` summary, the Proxmox summary graph, the Unraid dashboard, Synology Resource Monitor, and Home Assistant's system monitor sensors. To reconcile the two:
+
+```
+host percentage ≈ (sum of Frigate's process percentages) / (number of cores)
+```
+
+On a 4 core system, an FFmpeg process reporting 100% is consuming one quarter of the machine, so the host will show roughly 25 to 30% once the remaining Frigate processes are included. That same 100% on a 16 core system is about 6%. Frigate's own warning thresholds use the per-core convention as well, so an FFmpeg process is flagged at 20% of a single core, not 20% of the system.
+
+### Instantaneous samples and averages measure different things
+
+Frigate collects stats every 15 seconds, and the `cpu` value covers only the interval since the previous collection. The `cpu_average` value in the stats API and MQTT payload is the average across the entire life of the process, and it is what the high CPU usage warnings are based on. Host dashboards generally plot data averaged over a longer window, so a single Frigate sample can show a peak that a host graph never displays. A process that has just started, such as FFmpeg after a camera reconnect, reports 0 until it has been sampled twice.
+
+### The system-wide value depends on what the container can see
+
+The system CPU value is read from `/proc/stat`. Under Docker that file belongs to the host, so the value covers the entire machine including workloads unrelated to Frigate, and it will not match `docker stats` for the Frigate container. Under an LXC container, lxcfs virtualizes `/proc/stat` and the value reflects only the cores assigned to the container. In a virtual machine, the guest sees only its assigned vCPUs while the hypervisor divides by every physical thread on the node, so guest and host percentages will not agree even when both are accurate.
## 1. Hardware Acceleration for Video Decoding
@@ -24,7 +48,7 @@ Video decoding is one of the most CPU-intensive tasks in Frigate. While an AI ac
### Configuration
-Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/reference) as described in the [getting started guide](../guides/getting_started).
+Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/advanced/reference) as described in the [getting started guide](../guides/getting_started).
### Troubleshooting Hardware Acceleration
@@ -44,7 +68,7 @@ Choosing the right detector for your hardware is the single most important facto
### Understanding Detector Performance
-Frigate uses motion detection as a first-line check before running expensive object detection, as explained in the [motion detection documentation](../configuration/motion_detection). When motion is detected, Frigate creates a "region" (the green boxes in the debug viewer) and sends it to the detector. The detector's inference speed determines how many detections per second your system can handle.
+Frigate uses motion detection as a first-line check before running expensive object detection, as explained in the [motion detection documentation](../configuration/motion_detection). When motion is detected, Frigate creates a "region" (the green boxes in the [debug viewer](/usage/live#the-single-camera-view)) and sends it to the detector. The detector's inference speed determines how many detections per second your system can handle.
**Calculating Detector Capacity:** Your detector has a finite capacity measured in detections per second. With an inference speed of 10ms, your detector can handle approximately 100 detections per second (1000ms / 10ms = 100).If your cameras collectively require more than this capacity, you'll experience delays, missed detections, or the system will fall behind.
@@ -58,7 +82,6 @@ When a single detector cannot keep up with your camera count, some detector type
For detailed instructions on configuring multiple detectors, see the [Object Detectors documentation](../configuration/object_detectors).
-
**When to add a second detector:**
- Skipped FPS is consistently > 0 even during normal activity
@@ -70,4 +93,22 @@ The model you use significantly impacts detector performance. Frigate provides d
**Model Size Trade-offs:**
- Smaller models (320x320): Faster inference, Frigate is specifically optimized for a 320x320 size model.
-- Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
\ No newline at end of file
+- Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
+
+For more detail on picking the right size, see [Choosing a model size](../configuration/object_detectors.md#choosing-a-model-size).
+
+## 3. Reducing Detector CPU Usage
+
+**Priority: High**
+
+The **Detector CPU Usage** metric measures the CPU spent converting frames into the tensor format the model expects and post-processing the model's output. It does not include inference, so this value can be high even when you've configured a GPU, NPU, or Coral for object detection.
+
+This metric scales with how many detections per second Frigate runs and how expensive each one is to prepare. Tuning [motion detection](../configuration/motion_detection) is usually the first recommendation to reduce the number of detections. Additionally, you can:
+
+- **Lower `detect -> fps`.** 5 is the recommended value for nearly all cameras. Running at 10 doubles the frames eligible for detection and is one of the largest contributors to this metric.
+- **Use a 320x320 model.** A 640x640 model has 4 times as many pixels to transpose, convert, and copy on every inference.
+- **Prefer a model that takes integer input.** Models configured with `input_dtype: float` require each frame to be converted to float32 and normalized on the CPU first. Models taking `int` input, such as the tflite models used by the Edge TPU, skip that step.
+- **Do not match the detect resolution to the model resolution.** The detect stream should match your camera's aspect ratio, for example `1280x720`, not the model's input size. Frigate crops and scales regions of motion itself, so an oversized detect stream only adds work.
+- **Tune stationary object behavior.** Objects that never settle into a stationary state are re-detected continuously. Raising `detect -> stationary -> interval` reduces how often detection runs on objects that are already parked. See [stationary objects](../configuration/stationary_objects).
+
+Adding [more detector instances](#multiple-detector-instances) spreads this work across more CPU cores, but does not reduce the total CPU used.
diff --git a/docs/docs/troubleshooting/dummy-camera.md b/docs/docs/troubleshooting/dummy-camera.md
index 89495844d0..3f13b1e190 100644
--- a/docs/docs/troubleshooting/dummy-camera.md
+++ b/docs/docs/troubleshooting/dummy-camera.md
@@ -3,17 +3,87 @@ id: dummy-camera
title: Analyzing Object Detection
---
-When investigating object detection or tracking problems, it can be helpful to replay an exported video as a temporary "dummy" camera. This lets you reproduce issues locally, iterate on configuration (detections, zones, enrichment settings), and capture logs and clips for analysis.
+import NavPath from "@site/src/components/NavPath";
-## When to use
+Frigate provides several tools for investigating object detection and tracking behavior: reviewing recorded detections through the UI, using the built-in Debug Replay feature, and manually setting up a dummy camera for advanced scenarios.
-- Replaying an exported clip to reproduce incorrect detections
-- Testing configuration changes (model settings, trackers, filters) against a known clip
-- Gathering deterministic logs and recordings for debugging or issue reports
+## Reviewing Detections in the UI
-## Example Config
+Before setting up a replay, you can often diagnose detection issues by reviewing existing recordings directly in the Frigate UI.
-Place the clip you want to replay in a location accessible to Frigate (for example `/media/frigate/` or the repository `debug/` folder when developing). Then add a temporary camera to your `config/config.yml` like this:
+### Detail View (History)
+
+The **Detail Stream** view in History shows recorded video with detection overlays (bounding boxes, path points, and zone highlights) drawn on top. Select a review item to see its tracked objects and lifecycle events. Clicking a lifecycle event seeks the video to that point so you can see exactly what the detector saw.
+
+### Tracking Details (Explore)
+
+In **Explore**, clicking a thumbnail opens the **Tracking Details** pane, which shows the full lifecycle of a single tracked object: every detection, zone entry/exit, and attribute change. The video plays back with the bounding box overlaid, letting you step through the object's entire lifecycle.
+
+### Annotation Offset
+
+Both views support an **Annotation Offset** setting (`detect.annotation_offset` in your camera config) that shifts the detection overlay in time relative to the recorded video. This compensates for the timing drift between the `detect` and `record` pipelines.
+
+These streams use fundamentally different clocks with different buffering and latency characteristics, so the detection data and the recorded video are never perfectly synchronized. The annotation offset shifts the overlay to visually align the bounding boxes with the objects in the recorded video.
+
+#### Why the offset varies between clips
+
+The base timing drift between detect and record is roughly constant for a given camera, so a single offset value works well on average. However, you may notice the alignment is not pixel-perfect in every clip. This is normal and caused by several factors:
+
+- **Keyframe-constrained seeking**: When the browser seeks to a timestamp, it can only land on the nearest keyframe. Each recording segment has keyframes at different positions relative to the detection timestamps, so the same offset may land slightly early in one clip and slightly late in another.
+- **Segment boundary trimming**: When a recording range starts mid-segment, the video is trimmed to the requested start point. This trim may not align with a keyframe, shifting the effective reference point.
+- **Capture-time jitter**: Network buffering, camera buffer flushes, and ffmpeg's own buffering mean the system-clock timestamp and the corresponding recorded frame are not always offset by exactly the same amount.
+
+The per-clip variation is typically quite low and is mostly an artifact of keyframe granularity rather than a change in the true drift. A "perfect" alignment would require per-frame, keyframe-aware offset compensation, which is not practical. Treat the annotation offset as a best-effort average for your camera.
+
+## Debug Replay
+
+Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
+
+The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
+
+Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
+
+### When to use
+
+- Reproducing a detection or tracking issue from a specific time range
+- Testing configuration changes (model settings, zones, filters, motion) against a known clip
+- Gathering logs and debug overlays for a bug report
+
+:::note
+
+Only one replay session can be active at a time. If a session is already running, you will be prompted to navigate to it or stop it first.
+
+:::
+
+### Starting Debug Replay
+
+Debug Replay can be started from several places in the UI. The starting point determines the time range that gets replayed.
+
+- **History: Actions menu.** Navigate to , open the **Actions** menu in the toolbar, and choose **Debug Replay**. From here you can pick a preset (**Last 1 Minute**, **Last 5 Minutes**), select a range directly on the timeline with **From Timeline**, or enter exact start and end times with **Custom**. This is the most flexible option and the best choice when you want to add padding around a detection. On mobile, the same options appear in the Actions drawer.
+- **History: Detail Stream event menu.** While viewing a review item in the Detail Stream, open the menu on a tracked object's event card and choose **Debug Replay**. The replay range is set automatically to that object's start and end times.
+- **Explore: search result menu.** From an Explore card, open the kebab menu and choose **Debug Replay**. The range is taken from the tracked object's lifecycle.
+- **Explore: Tracking Details Actions menu.** Open a tracked object's **Tracking Details** dialog, then choose **Debug Replay** from the Actions menu. Same automatic range as the search result menu.
+- **Exports: export card menu.** From , open the menu on an export and choose **Debug Replay** to loop the exported clip through the detection pipeline for the camera it was exported from.
+
+The Detail Stream, Explore, and Exports entry points use the underlying recording or export's bounds with a small amount of padding. This can be convenient for quick checks, but if a detection is short or you want extra "settle" time for motion and the detector, start the replay from the History Actions menu instead and widen the range manually.
+
+### Variables to consider
+
+- The replay will not always produce identical results to the original run. Different frames may be selected on replay, which can change detections and tracking.
+- Motion detection depends on the exact frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
+- Object detection is not fully deterministic: models and post-processing can yield slightly different results across runs.
+- In cases where a detection is short and a replay may only be a small number of frames, it is recommended to manually add some padding before and after the detection so that the motion and object detectors have time to settle into the scene. Rather than starting Debug Replay from Explore, navigate to History for your camera, choose Debug Replay from the Actions menu, and click the "From Timeline" or "Custom" option.
+- The replay camera inherits the source camera's zones. Any automations that trigger on those zone names will fire for the replay camera as well. This can be helpful when debugging zone behavior, but may be unexpected. You can add a condition on the source camera's name in your automation if you want to exclude replay triggers.
+
+Treat the replay as a close approximation rather than an exact reproduction. Run multiple loops and examine the debug overlays and logs to understand the behavior.
+
+## Manual Dummy Camera
+
+For advanced scenarios (such as testing with a clip from a different source, debugging ffmpeg behavior, or running a clip through a completely custom configuration), you can set up a dummy camera manually.
+
+### Example config
+
+Place the clip you want to replay in a location accessible to Frigate (for example `/media/frigate/` or the repository `debug/` folder when developing). Then add a temporary camera to your `config/config.yml`:
```yaml
cameras:
@@ -32,29 +102,21 @@ cameras:
enabled: false
```
-- `-re -stream_loop -1` tells `ffmpeg` to play the file in realtime and loop indefinitely, which is useful for long debugging sessions.
-- `-fflags +genpts` helps generate presentation timestamps when they are missing in the file.
+- `-re -stream_loop -1` tells ffmpeg to play the file in real time and loop indefinitely.
+- `-fflags +genpts` generates presentation timestamps when they are missing in the file.
-## Steps
+### Steps
1. Export or copy the clip you want to replay to the Frigate host (e.g., `/media/frigate/` or `debug/clips/`). Depending on what you are looking to debug, it is often helpful to add some "pre-capture" time (where the tracked object is not yet visible) to the clip when exporting.
2. Add the temporary camera to `config/config.yml` (example above). Use a unique name such as `test` or `replay_camera` so it's easy to remove later.
- If you're debugging a specific camera, copy the settings from that camera (frame rate, model/enrichment settings, zones, etc.) into the temporary camera so the replay closely matches the original environment. Leave `record` and `snapshots` disabled unless you are specifically debugging recording or snapshot behavior.
3. Restart Frigate.
-4. Observe the Debug view in the UI and logs as the clip is replayed. Watch detections, zones, or any feature you're looking to debug, and note any errors in the logs to reproduce the issue.
+4. Observe the [Debug view](/usage/live#the-single-camera-view) in the UI and logs as the clip is replayed. Watch detections, zones, or any feature you're looking to debug, and note any errors in the logs to reproduce the issue.
5. Iterate on camera or enrichment settings (model, fps, zones, filters) and re-check the replay until the behavior is resolved.
6. Remove the temporary camera from your config after debugging to avoid spurious telemetry or recordings.
-## Variables to consider in object tracking
+### Troubleshooting
-- The exported video will not always line up exactly with how it originally ran through Frigate (or even with the last loop). Different frames may be used on replay, which can change detections and tracking.
-- Motion detection depends on the frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
-- Object detection is not deterministic: models and post-processing can yield different results across runs, so you may not get identical detections or track IDs every time.
-
-When debugging, treat the replay as a close approximation rather than a byte-for-byte replay. Capture multiple runs, enable recording if helpful, and examine logs and saved event clips to understand variability.
-
-## Troubleshooting
-
-- No video: verify the path is correct and accessible from the Frigate process/container.
-- FFmpeg errors: check the log output for ffmpeg-specific flags and adjust `input_args` accordingly for your file/container. You may also need to disable hardware acceleration (`hwaccel_args: ""`) for the dummy camera.
-- No detections: confirm the camera `roles` include `detect`, and model/detector configuration is enabled.
+- **No video**: verify the file path is correct and accessible from the Frigate process/container.
+- **FFmpeg errors**: check the log output and adjust `input_args` for your file format. You may also need to disable hardware acceleration (`hwaccel_args: ""`) for the dummy camera.
+- **No detections**: confirm the camera `roles` include `detect` and that the model/detector configuration is enabled.
diff --git a/docs/docs/troubleshooting/edgetpu.md b/docs/docs/troubleshooting/edgetpu.md
index 4ee25afd0f..bc50ae5040 100644
--- a/docs/docs/troubleshooting/edgetpu.md
+++ b/docs/docs/troubleshooting/edgetpu.md
@@ -12,7 +12,7 @@ There are many possible causes for a USB coral not being detected and some are O
:::tip
-Using `lsusb` or checking the hardware page in HA OS will show as `1a6e:089a Global Unichip Corp.` until Frigate runs an inferance using the coral. So don't worry about the identification until after Frigate has attempted to detect the coral.
+Using `lsusb` or checking the hardware page in HA OS will show as `1a6e:089a Global Unichip Corp.` until Frigate runs an inference using the coral. So don't worry about the identification until after Frigate has attempted to detect the coral.
:::
@@ -43,13 +43,13 @@ Some users have reported that this older device runs an older kernel causing iss
3. Start the docker container with Coral TPU enabled in the config
4. The TPU would be detected but a few moments later it would disconnect.
5. While leaving the TPU device plugged in, restart the NAS using the reboot command in the UI. Do NOT unplug the NAS/power it off etc.
-6. Open the control panel - info scree. The coral TPU will now be recognised as a USB Device - google inc
+6. Open the control panel - info screen. The coral TPU will now be recognized as a USB Device - google inc
7. Start the frigate container. Everything should work now!
### QNAP NAS
-QNAP NAS devices, such as the TS-253A, may use connected Coral TPU devices if [QuMagie](https://www.qnap.com/en/software/qumagie) is installed along with its QNAP AI Core extension. If any of the features—`facial recognition`, `object recognition`, or `similar photo recognition`—are enabled, Container Station applications such as `Frigate` or `CodeProject.AI Server` will be unable to initialize the TPU device in use.
-To allow the Coral TPU device to be discovered, the you must either:
+QNAP NAS devices, such as the TS-253A, may use connected Coral TPU devices if [QuMagie](https://www.qnap.com/en/software/qumagie) is installed along with its QNAP AI Core extension. If any of the features (`facial recognition`, `object recognition`, or `similar photo recognition`) are enabled, Container Station applications such as `Frigate` or `CodeProject.AI Server` will be unable to initialize the TPU device in use.
+To allow the Coral TPU device to be discovered, you must either:
1. [Disable the AI recognition features in QuMagie](https://docs.qnap.com/application/qumagie/2.x/en-us/configuring-qnap-ai-core-settings-FB13CE03.html),
2. Remove the QNAP AI Core extension or
@@ -76,7 +76,7 @@ This is an issue due to outdated gasket driver when being used with new linux ke
### Not detected on Raspberry Pi5
-A kernel update to the RPi5 means an upate to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
+A kernel update to the RPi5 means an update to config.txt is required, see [the raspberry pi forum for more info](https://forums.raspberrypi.com/viewtopic.php?t=363682&sid=cb59b026a412f0dc041595951273a9ca&start=25)
Specifically, add the following to config.txt
@@ -87,7 +87,7 @@ dtoverlay=pcie-32bit-dma-pi5
## Only One PCIe Coral Is Detected With Coral Dual EdgeTPU
-Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has it's own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.
+Coral Dual EdgeTPU is one card with two identical TPU cores. Each core has its own PCIe interface and motherboard needs to have two PCIe busses on the m.2 slot to make them both work.
E-key slot implemented to full m.2 electromechanical specification has two PCIe busses. Most motherboard manufacturers implement only one PCIe bus in m.2 E-key connector (this is why only one TPU is working). Some SBCs can have only USB bus on m.2 connector, ie none of TPUs will work.
diff --git a/docs/docs/troubleshooting/faqs.md b/docs/docs/troubleshooting/faqs.md
index bba4a1c187..ae29f6c9eb 100644
--- a/docs/docs/troubleshooting/faqs.md
+++ b/docs/docs/troubleshooting/faqs.md
@@ -29,7 +29,7 @@ You can open `chrome://media-internals/` in another tab and then try to playback
### What do I do if my cameras sub stream is not good enough?
-Frigate generally [recommends cameras with configurable sub streams](/frigate/hardware.md). However, if your camera does not have a sub stream that a suitable resolution, the main stream can be resized.
+Frigate generally [recommends cameras with configurable sub streams](/frigate/hardware.md). However, if your camera does not have a sub stream that is a suitable resolution, the main stream can be resized.
To do this efficiently the following setup is required:
@@ -39,6 +39,20 @@ To do this efficiently the following setup is required:
When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.
+### How can I rotate my camera's video feed?
+
+Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
+
+If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
+
+```yaml
+go2rtc:
+ streams:
+ my_camera: "ffmpeg:rtsp://user:password@192.168.1.10:554/stream#video=h264#hardware#rotate=90"
+```
+
+Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
+
### My mjpeg stream or snapshots look green and crazy
This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -49,13 +63,13 @@ This almost always means that the width/height defined for your camera are not c
These messages in the logs are expected in certain situations. Frigate checks the integrity of the recordings before storing. Occasionally these cached files will be invalid and cleaned up automatically.
-### "On connect called"
+### "MQTT connected" repeats in the logs
-If you see repeated "On connect called" messages in your logs, check for another instance of Frigate. This happens when multiple Frigate containers are trying to connect to MQTT with the same `client_id`.
+If you see repeated "MQTT connected" messages in your logs, check for another instance of Frigate. This happens when multiple Frigate containers are trying to connect to MQTT with the same `client_id`.
### Error: Database Is Locked
-SQLite does not work well on a network share, if the `/media` folder is mapped to a network share then [this guide](../configuration/advanced.md#database) should be used to move the database to a location on the internal drive.
+SQLite does not work well on a network share, if the `/media` folder is mapped to a network share then [this guide](../configuration/advanced/system.md#database) should be used to move the database to a location on the internal drive.
### Unable to publish to MQTT: client is not connected
@@ -65,9 +79,17 @@ This is because Frigate does not run in host mode so localhost points to the Fri
### How do I know if my camera is offline
-A camera being offline can be detected via MQTT or /api/stats, the camera_fps for any offline camera will be 0.
+Frigate publishes a per-role health status to [`frigate//status/`](/integrations/mqtt#frigatecamera_namestatusrole), where `` is each enabled role on the camera (`detect`, `record`, and `audio`). The published value is one of:
-Also, Home Assistant will mark any offline camera as being unavailable when the camera is offline.
+- `online`: Frigate's process for that role is running normally
+- `offline`: the process is down and Frigate is restarting it
+- `disabled`: the camera is turned off, either at runtime or in the configuration file
+
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
+Because the status is per role, a camera whose substream is fine but whose recording stream has dropped will report `online` for `detect` and `offline` for `record`. The status is republished whenever it changes.
+
+You can also detect an offline camera through `/api/stats`, where `camera_fps` will be 0.
### How can I view the Frigate log files without using the Web UI?
@@ -111,18 +133,51 @@ TCP ensures that all data packets arrive in the correct order. This is crucial f
You can still configure Frigate to use UDP by using ffmpeg input args or the preset `preset-rtsp-udp`. See the [ffmpeg presets](/configuration/ffmpeg_presets) documentation.
-### Why does Frigate keep creating new events for my parked car?
+### Frigate is slow to start up with a "probing detect stream" message in the logs
-Stationary tracking is designed to _prevent_ this — a parked car should stay one tracked object and not generate new events. If you're getting repeated events for the same car, it's likely that Frigate is losing the tracked object and re-detecting it as a new one.
+When `detect.width` and `detect.height` are not set, Frigate probes each camera's detect stream on startup (and when saving the config) to auto-detect its resolution. For RTSP streams Frigate probes with ffprobe and automatically retries over TCP if UDP doesn't respond, with a 5 second timeout per attempt. A camera that cannot be reached over either transport will add up to ~10 seconds to startup before Frigate falls through with default dimensions, which may show up as width `0` and height `0` in Camera Probe Info under System Metrics.
-Open one of the events in Explore → **Tracking Details**. If the detection scores are low (< 70% or so), the model isn't confident the parked car is a car. This is common with the free [COCO-trained](https://cocodataset.org/#explore) object detection models on steep/top-down angles, partially occluded cars, foliage, or low-light footage. When detections fall below `min_score` for too many frames the tracker loses the object, and the next confident frame creates a brand new one.
+To skip the probe entirely and make startup instant, set `detect.width` and `detect.height` explicitly in your camera config:
+
+```yaml
+cameras:
+ my_camera:
+ detect:
+ width: 1280
+ height: 720
+```
+
+### What is the `version` key in my config file?
+
+`version` records the config format that your config was last migrated to. On startup Frigate compares it against the format the running version expects, and if it is older it copies your config to `/config/backup_config.yaml`, rewrites it to the new format, and updates `version` as the final step. A config with no `version` key is assumed to predate 0.14 and is migrated from there.
+
+Frigate manages this key for you, so do not set or edit it. Raising it makes Frigate skip migrations your config still needs, and lowering it re-runs migrations against config that has already been converted. Either can leave you with a config that no longer validates.
+
+### Why does Frigate keep creating new tracked objects for my parked car?
+
+Stationary tracking is designed to _prevent_ this: a parked car should remain a single tracked object rather than generating new ones. If you're repeatedly getting new tracked objects for the same car, it's likely that Frigate is losing the object and re-detecting it as a new one.
+
+Open one of the tracked objects in Explore → **Tracking Details**. If the detection scores are low (< 70% or so), the model isn't confident the parked car is a car. This is common with the free [COCO-trained](https://cocodataset.org/#explore) object detection models on steep/top-down angles, partially occluded cars, foliage, or low-light footage. When detections fall below `min_score` for too many frames the tracker loses the object, and the next confident frame creates a brand new one.
What helps:
-- **Improve the view** — even a small angle change that gets more of the car visible could lift scores enough to stabilize tracking.
-- **Use a more accurate model** — switching from `mobiledet` to `yolov9`, or stepping up to a larger variant like `yolov9-s` over `yolov9-t`, can help (at the cost of inference time, and still on the COCO dataset). The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup.
-- **Don't set `detect -> stationary -> max_frames` for `car`** — it artificially ends tracking and forces re-detection as a new object. See [Stationary Objects](../configuration/stationary_objects.md).
-- **Restrict alerts to the areas you care about** with `required_zones` — see [Zones](../configuration/zones.md#restricting-alerts-and-detections-to-specific-zones). Make sure those zones use the default `loitering_time: 0` unless you specifically want the review item to stay open until the car leaves.
+- **Improve the view**: even a small angle change that gets more of the car visible could lift scores enough to stabilize tracking.
+- **Use a more accurate model**: switching from `mobiledet` to `yolov9`, or stepping up to a larger variant like `yolov9-s` over `yolov9-t`, can help (at the cost of inference time, and still on the COCO dataset). The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup.
+- **Don't set `detect -> stationary -> max_frames` for `car`**: it artificially ends tracking and forces re-detection as a new object. See [Stationary Objects](../configuration/stationary_objects.md).
+- **Restrict alerts to the areas you care about** with `required_zones`. See [Zones](../configuration/zones.md#restricting-alerts-and-detections-to-specific-zones). Make sure those zones use the default `loitering_time: 0` unless you specifically want the review item to stay open until the car leaves.
- **Filter impossible locations** with [object filter masks](../configuration/masks.md#object-filter-masks) if cars are being detected on rooftops, treetops, etc.
-See [Object Filters](../configuration/object_filters.md) for more on tuning `min_score` and `threshold` — note that raising them too high will make this exact problem worse.
+See [Object Filters](../configuration/object_filters.md) for more on tuning `min_score` and `threshold`. Note that raising them too high will make this exact problem worse.
+
+### How do I correct Frigate when it detects something as the wrong object?
+
+Frigate's object detection relies on a machine learning [model](../frigate/glossary.md#model), and the free [COCO-trained](https://cocodataset.org/#explore) models that ship with Frigate can misidentify objects in scenes they weren't trained on. There are two ways to handle this, depending on whether you want to _teach_ the model or just _suppress_ the bad result.
+
+**Train or fine-tune a model with your own images.** The most durable fix is to improve the model itself. The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. Some tools are freely available, and [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup. When Frigate mislabels something, open the tracked object in Explore, select the **Snapshot** tab, and use **Submit to Frigate+** to send the example with the correct label (or mark it as a [false positive](../frigate/glossary.md#false-positive)). Once you've submitted examples and [requested a model](../plus/first_model.md), the retrained model will be more accurate for your cameras. See [Submitting examples to Frigate+](../integrations/plus.md#submit-examples) for the full workflow.
+
+**Suppress the misidentification with filters.** You can use filters to stop a specific false positive from being tracked:
+
+- Tune `min_score` / `threshold`, or add `min_area` / `max_area` / `min_ratio` / `max_ratio` filters. See [Object Filters](../configuration/object_filters.md).
+- If the false positive is always in the same fixed spot (like a statue or mailbox that reads as a person), add an [object filter mask](../configuration/masks.md#object-filter-masks) over that location.
+
+Filters and masks only hide the incorrect result - they don't teach Frigate what the object actually is. For that, fine-tune your own model or use Frigate+.
diff --git a/docs/docs/troubleshooting/go2rtc.md b/docs/docs/troubleshooting/go2rtc.md
new file mode 100644
index 0000000000..00ecd6f46e
--- /dev/null
+++ b/docs/docs/troubleshooting/go2rtc.md
@@ -0,0 +1,237 @@
+---
+id: go2rtc
+title: Troubleshooting go2rtc
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+This page covers common problems with the bundled [go2rtc](/configuration/go2rtc) and how to resolve them, whether your cameras were added with the setup wizard or configured by hand.
+
+When a stream won't play or behaves oddly, the most important first step is to figure out **where** in the pipeline it breaks. Frigate's live view is a chain (_camera → go2rtc → your browser_), and each stage fails for different reasons. Work through the checks below in order, then jump to the matching problem category.
+
+## Start by isolating the problem
+
+### 1. Read the go2rtc logs
+
+Access the go2rtc logs in the Frigate UI under in the sidebar (select the **go2rtc** tab). If go2rtc cannot connect to your camera you will usually see a clear error here: `401 Unauthorized` (bad or incorrectly encoded credentials), `Connection refused` / `timeout` (wrong IP, port, or the camera is at its connection limit), or `404 Not Found` (wrong RTSP path, or the referenced stream name does not exist).
+
+### 2. Test the stream in the go2rtc web interface
+
+If the logs look clean, open go2rtc's own web interface on port `1984`. This is the single most useful diagnostic, because it takes Frigate's UI out of the equation entirely.
+
+- If using Frigate through Home Assistant, enable the web interface at port `1984` (it is disabled by default, see [Home Assistant ports](#home-assistant-and-port-access)).
+- If using Docker, forward port `1984` before accessing the web interface.
+
+Open the stream page for your camera (`http://:1984/stream.html?src=back`) and try each player link:
+
+- **If nothing plays here**, the problem is between the camera and go2rtc (codec, credentials, or transport), _not_ your browser. Fix it at the source before touching anything in Frigate.
+- **If a player works here but Frigate's live view does not**, the problem is browser/codec related. Compare the **MSE** and **WebRTC** links. Frigate prefers MSE and only attempts WebRTC when MSE fails (or for two-way talk). If `mode=mse` plays but `mode=webrtc` does not, you have a [WebRTC codec problem](#webrtc-and-two-way-talk); if neither plays, your browser cannot decode the codec (commonly H.265, see [H.265 / HEVC cameras](#h265--hevc-cameras)).
+
+### 3. Inspect the negotiated codecs
+
+You can view detailed stream info, including the exact video and audio codecs go2rtc negotiated with the camera, at `http://frigate_ip:5000/api/go2rtc/streams` (or `http://frigate_ip:5000/api/go2rtc/streams/back` for a single camera). This is the authoritative answer to "what is my camera actually sending?" and is far more reliable than guessing from the camera's web UI. It also shows whether the audio track is `sendonly`/`recvonly`, which matters for [two-way talk](#webrtc-and-two-way-talk).
+
+### 4. Fix the codec with the FFmpeg module
+
+If the camera plays in go2rtc but not in your browser, the video or audio codec is unsupported. Browsers can reliably play **H.264** video and **AAC** audio; many cannot play H.265/HEVC, and some camera audio (G.711/PCM, MJPEG containers, etc.) is not playable at all. The fix is to have go2rtc re-encode the stream on demand using its FFmpeg module.
+
+In the Frigate UI this is the **Use compatibility mode (ffmpeg)** toggle on a stream source; in YAML it is the `ffmpeg:` prefix on the source URL.
+
+
+
+
+1. Navigate to and expand your camera's stream.
+2. On the source you want to convert, click the **Use compatibility mode (ffmpeg)** button (the sliders icon next to the URL). This routes the source through go2rtc's FFmpeg module and reveals the transcoding options.
+3. Set **Video** to **Transcode to H.264** if your browser can't play the camera's video codec (e.g. H.265). Leave it on **Copy** to pass the video through untouched. This is much cheaper and should be your default whenever only the audio needs converting.
+4. Set **Audio** to **Transcode to AAC** (for MSE) or **Transcode to Opus** (for WebRTC) if the camera's audio codec is unsupported. Leave it on **Copy** to keep the original, or **Exclude** to drop audio entirely.
+5. When transcoding **video**, set **Hardware acceleration** to **Automatic (recommended)** so the encode runs on your GPU instead of the CPU. See [hardware-accelerated transcoding](#hardware-accelerated-transcoding-with-ffmpeg-8) for an important FFmpeg 8 caveat.
+6. **Save** the section, then reload the live view.
+
+
+
+
+```yaml
+go2rtc:
+ streams:
+ back:
+ - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+ # transcode video to H.264 on the GPU; only needed if the browser can't play the source codec
+ - "ffmpeg:back#video=h264#hardware"
+```
+
+To convert audio only (leaving video untouched), or to convert both:
+
+```yaml
+go2rtc:
+ streams:
+ back:
+ - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
+ - "ffmpeg:back#audio=aac" # audio only, preferred when the video already plays
+ # or, to convert both video and audio:
+ # - "ffmpeg:back#video=h264#audio=aac#hardware"
+```
+
+
+
+
+:::warning
+
+The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+
+A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
+
+:::
+
+Transcoding video is resource intensive. Always prefer `#video=copy` (the **Copy** option) and only convert the track that is actually unsupported. If you must transcode video and have no hardware encoder available, the built-in jsmpeg view may be the better option.
+
+## Live view is black, buffering, or stuck in "low-bandwidth mode"
+
+When the live view shows a black screen, spins forever, or repeatedly drops to the lower-quality jsmpeg player ("low-bandwidth mode"), the stream almost always contains something the browser cannot decode over MSE, usually H.265 video or a non-AAC audio track. Confirm this in the go2rtc web UI (port `1984`): if MSE won't play there, Frigate can't play it either, since it uses the same pipeline.
+
+The fix is to produce an **H.264 + AAC** stream, either by changing your camera's firmware codecs or by transcoding in go2rtc (see [Fix the codec with the FFmpeg module](#4-fix-the-codec-with-the-ffmpeg-module)). A few other things worth checking:
+
+- **Set the camera's I-frame (keyframe) interval to match its frame rate** (or "1x" on Reolink), and avoid "smart"/"+" codecs like _H.264+_ or _H.265+_. A long keyframe interval delays the first decodable frame past Frigate's startup timeout, which forces the fallback to jsmpeg. See [camera settings recommendations](/configuration/live#camera-settings-recommendations).
+- **A spinner that never clears, even though video plays in VLC**, is often an unplayable _audio_ track stalling playback. Drop or transcode the audio (see below).
+- **Remote/VPN viewing that buffers** while the LAN is fine is usually latency/jitter exceeding MSE's startup buffer. Set up [WebRTC](/configuration/live#webrtc-extra-configuration), which drops late frames instead of buffering.
+
+The general live-view behavior (smart streaming, the MSE → WebRTC → jsmpeg fallback chain, and how to read browser console errors) is documented in detail in the [Live view FAQ](/configuration/live#live-view-faq).
+
+## H.265 / HEVC cameras
+
+H.265/HEVC playback in the browser is unreliable and version-dependent. WebRTC does not support H.265 on some browsers, and MSE/HEVC support varies by browser, OS, and whether a hardware decoder is present. An H.265 stream that plays fine in VLC, the go2rtc web UI, and Frigate's recordings can still be blank in a live view.
+
+For dependable live viewing, use **H.264** for the stream the live view consumes:
+
+- Point the live view at the camera's H.264 **substream** and keep the H.265 main stream for recording only, or
+- Transcode H.265 → H.264 in go2rtc with the FFmpeg module and `#hardware` (software HEVC transcoding is very CPU heavy).
+
+Treat browser HEVC playback as best-effort. See also [H.265 cameras via Safari](/configuration/camera_specific#h265-cameras-via-safari).
+
+## No audio in Live view
+
+Live view audio has strict codec requirements that differ by player: **MSE requires AAC, PCMA, or PCMU**, and **WebRTC requires Opus, PCMA, or PCMU**. Many cameras default to a codec outside these sets (or to PCM/G.711), so the player loads video only and no audio control appears.
+
+The most robust approach is to provide both an AAC track (for MSE) and an Opus track (for WebRTC) on the same stream by transcoding audio with the FFmpeg module while copying the video:
+
+
+
+
+1. Navigate to and expand the camera's stream.
+2. Add a second **Source** that references the stream by name (e.g. the URL `ffmpeg:back`), enable **Use compatibility mode (ffmpeg)**, and set **Audio** to **Transcode to Opus** for WebRTC support.
+3. Keep the original source as **Source 1** so MSE can use the camera's AAC (or transcode the first source's audio to AAC if the camera doesn't provide it).
+4. **Save** the section.
+
+
+
+
+```yaml
+go2rtc:
+ streams:
+ back:
+ - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2 # video + AAC for MSE
+ - "ffmpeg:back#audio=opus" # adds an Opus track for WebRTC
+```
+
+If the camera's native audio isn't AAC either, transcode both:
+
+```yaml
+go2rtc:
+ streams:
+ back:
+ - "ffmpeg:rtsp://user:password@10.0.10.10:554/live0#video=copy#audio=aac" # video copy + AAC for MSE
+ - "ffmpeg:back#audio=opus" # Opus for WebRTC
+```
+
+
+
+
+Setting the camera firmware to AAC (and H.264) avoids transcoding entirely and is always preferable when the camera supports it. For more detail and examples, see [Audio Support](/configuration/live#audio-support).
+
+## WebRTC and two-way talk
+
+WebRTC is only attempted when MSE fails or when using a camera's two-way talk feature; the "All Cameras" dashboard never uses it. When it doesn't work, the cause is almost always one of:
+
+- **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
+- **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
+- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+
+## High CPU usage
+
+If go2rtc is using a lot of CPU, it is almost always transcoding in software. An FFmpeg source with a codec modifier like `#video=h264` or `#audio=aac` but **no** `#hardware` re-encodes on the CPU. (Frigate's `ffmpeg.hwaccel_args` only applies to Frigate's own detect/record processes. It does _not_ accelerate go2rtc's transcodes.)
+
+To keep CPU usage down:
+
+- Only transcode the track that is genuinely unsupported, and use `#video=copy` to pass video through untouched whenever possible.
+- When you must transcode video, always add `#hardware` (the **Automatic** hardware option in the UI) so the encode runs on the GPU. Note the [FFmpeg 8 device requirement](#hardware-accelerated-transcoding-with-ffmpeg-8) below.
+- Don't restream a high-resolution main stream just to feed the live view: even with `#video=copy`, muxing a 4K/8MP+ stream is inherently expensive. Use the camera's lower-resolution substream for live and detect, and let Frigate pull the main stream directly for recording.
+
+## Connection, authentication, and complex passwords
+
+If go2rtc logs `401 Unauthorized` for a URL that works in VLC, the password almost certainly contains reserved URL characters. **Frigate URL-encodes passwords for its own `cameras.ffmpeg.inputs`, but it does not touch what you write under `go2rtc.streams`**: go2rtc parses that URL itself. You must URL-encode special characters yourself in the `go2rtc.streams` section (`@` → `%40`, `#` → `%23`, `?` → `%3F`, `%` → `%25`, etc.).
+
+Note the asymmetry: under `cameras.ffmpeg.inputs` you should use the **raw** password (Frigate encodes it for you). Pre-encoding it there causes a double-encode and fails. See [Handling Complex Passwords](/configuration/restream#handling-complex-passwords).
+
+Repeated `401`/`Connection refused` errors can also mean the camera hit its **concurrent connection limit** or triggered a login lockout. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) means the camera only ever sees one connection from go2rtc.
+
+## Stream names must match everywhere
+
+A surprising number of "the better live options aren't available" or `404 Not Found` problems come down to a name mismatch. The same string must be used consistently:
+
+- the **go2rtc stream key** (`go2rtc.streams.`),
+- any `ffmpeg:#…` source that references it,
+- the camera's restream input path (`rtsp://127.0.0.1:8554/`), and
+- the camera name itself (so Frigate auto-maps it for MSE/WebRTC), or an explicit `live -> streams` mapping pointing at the go2rtc stream **name** (never a path).
+
+If you rename or remove a go2rtc stream while experimenting and the live stream selector then shows a blank entry, clear your browser's site data for the Frigate URL. The selected stream is cached per-device in local storage.
+
+## Camera-specific behavior
+
+Several camera brands have well-known quirks with go2rtc. Rather than repeat them here, see the [camera-specific configuration](/configuration/camera_specific) page, which covers them in detail. The highlights:
+
+- **Reolink**: RTSP is unreliable on many models; the **http-flv** stream through the FFmpeg module is recommended, and you must enable HTTP/RTMP in the camera and **reboot** it. 6MP+ models stream H.265 over http-flv-enhanced, which requires FFmpeg 8.0. See [Reolink Cameras](/configuration/camera_specific#reolink-cameras).
+- **TP-Link Tapo**: use go2rtc's native `tapo://` source for stability and two-way audio; a stale RTSP credential can often be revived by clicking play once in the go2rtc web UI.
+- **Ubiquiti/UniFi Protect**: use the `rtspx://` scheme (not `rtsps://…?enableSrtp`).
+- **Amcrest/Dahua**: use the `/cam/realmonitor?channel=1&subtype=N` scheme, where `subtype=0` is the main stream. See [Amcrest & Dahua](/configuration/camera_specific#amcrest--dahua).
+
+## Non-RTSP sources and the FFmpeg module
+
+go2rtc's native zero-copy handling only supports well-formed RTSP H.264/H.265. Anything else (MJPEG, HTTP/HTTP-FLV, RTMP, or unusual codecs) must be handed to the FFmpeg module by prefixing the source with `ffmpeg:`. This is also necessary for some camera streams to be parsed at all, at the cost of slightly slower startup. MJPEG and other non-H.264 sources additionally need `#video=h264` (with `#hardware`) before they can be used for the `record`, `detect`, or restream roles. See [MJPEG Cameras](/configuration/camera_specific#mjpeg-cameras) for a complete example.
+
+## Hardware-accelerated transcoding with FFmpeg 8
+
+Frigate 0.18 ships **FFmpeg 8.0** as the default, and FFmpeg 8 is stricter about hardware-accelerated filtering than earlier versions. Whenever go2rtc transcodes video with hardware acceleration (any source using `#hardware`, `#hardware=vaapi`, or the **Automatic** hardware option in the UI), it builds a filter chain that uploads frames to the GPU with the `hwupload` filter. FFmpeg 8 now refuses to do this unless it is told **which device** to use. Earlier versions selected one automatically. The result is that an otherwise-working transcode fails to start, the live view never loads, and go2rtc logs:
+
+```
+[hwupload] A hardware device reference is required to upload frames to.
+[AVFilterGraph] Error initializing filters
+Error opening output files: Invalid argument
+```
+
+The fix is to tell go2rtc's bundled FFmpeg which hardware device to use via the `go2rtc -> ffmpeg -> global` option. For **VAAPI**-based acceleration (which covers most Intel and AMD GPUs, and is what go2rtc selects automatically on that hardware), point it at your render device:
+
+```yaml
+go2rtc:
+ ffmpeg:
+ global: "-vaapi_device /dev/dri/renderD128"
+ streams:
+ back:
+ - "ffmpeg:rtsp://user:password@10.0.10.10:554/live0#video=h264#hardware"
+```
+
+`/dev/dri/renderD128` is the usual render node; on a system with more than one GPU you may need `renderD129` (or higher), and the device must be passed into the container (e.g. `devices: - /dev/dri:/dev/dri` in Docker Compose).
+
+If you use a **different hardware acceleration backend**, you will likely need to specify its device in the same way, using the option that matches that backend instead of `-vaapi_device`. See the [go2rtc FFmpeg source documentation](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#source-ffmpeg) and the upstream report ([go2rtc issue #1984](https://github.com/AlexxIT/go2rtc/issues/1984)) for background and other examples.
+
+:::tip
+
+If you don't transcode in go2rtc with hardware acceleration, this does not affect you. If you want to avoid the change entirely, you can pin Frigate (and the go2rtc it bundles) back to FFmpeg 7.0 by setting `ffmpeg -> path: "7.0"` in your config.
+
+:::
+
+## Home Assistant and port access
+
+When running Frigate as a Home Assistant App, the go2rtc API (port `1984`), the RTSP restream (port `8554`), and WebRTC (port `8555`) are **disabled and hidden by default**. To use them (for example to reach the go2rtc web interface for troubleshooting, or to open a go2rtc stream externally in an app like VLC), go to , click **Show disabled ports**, enable the port you need, and save. Use the host's IP address rather than an mDNS name like `homeassistant.local`.
+
+If live view works in the Frigate UI but not in Home Assistant, the most common cause is the go2rtc stream name not matching the camera name: name the primary go2rtc stream exactly like the camera, or add a `live -> streams` mapping, so the integration can resolve the restream.
diff --git a/docs/docs/troubleshooting/gpu.md b/docs/docs/troubleshooting/gpu.md
index 6399f92d8b..a39386bfa9 100644
--- a/docs/docs/troubleshooting/gpu.md
+++ b/docs/docs/troubleshooting/gpu.md
@@ -10,4 +10,47 @@ title: GPU Errors
Some users have reported issues using some Intel iGPUs with OpenVINO, where the GPU would not be detected. This error can be caused by various problems, so it is important to ensure the configuration is setup correctly. Some solutions users have noted:
- In some cases users have noted that an HDMI dummy plug was necessary to be plugged into the motherboard's HDMI port.
-- When mixing an Intel iGPU with Nvidia GPU, the devices can be mixed up between `/dev/dri/renderD128` and `/dev/dri/renderD129` so it is important to confirm the correct device, or map the entire `/dev/dri` directory into the Frigate container.
\ No newline at end of file
+- When mixing an Intel iGPU with Nvidia GPU, the devices can be mixed up between `/dev/dri/renderD128` and `/dev/dri/renderD129` so it is important to confirm the correct device, or map the entire `/dev/dri` directory into the Frigate container.
+
+## Intel/AMD GPU
+
+### Hardware acceleration is not being used
+
+For VAAPI or QSV to work, the GPU's render device must be passed through to the Frigate container. Intel and AMD GPUs expose this as a render node under `/dev/dri`, usually `/dev/dri/renderD128`. If it is not passed through, hardware acceleration is unavailable: ffmpeg fails to initialize it (for example `Failed to open the drm device` or `No VA display found for device`) and GPU usage stays at zero while CPU usage remains high.
+
+Pass the render device through when starting the container. With `docker compose`:
+
+```yaml
+services:
+ frigate:
+ devices:
+ - /dev/dri/renderD128:/dev/dri/renderD128 # Intel / AMD GPU, update for your hardware
+```
+
+Or with `docker run`, add `--device /dev/dri/renderD128`. See the [installation docs](/frigate/installation) for a complete example.
+
+If it still isn't working after passing the device through:
+
+- **Confirm the render node exists and is the correct one.** Run `ls /dev/dri` on the host. You should see one or more `renderD12X` entries. Systems with more than one GPU (an Intel iGPU plus a discrete GPU) can expose both `/dev/dri/renderD128` and `/dev/dri/renderD129`, and the numbering is not guaranteed. Pass through the correct node, or map the entire directory (`/dev/dri:/dev/dri`, or `--device /dev/dri`) so all render nodes are available.
+- **Check device permissions.** The Frigate process must be able to access the render node. This is usually automatic when the container runs as root (the default), but nested setups such as an unprivileged Proxmox/LXC container often require making the device accessible on the host (for example, a world-readable render node) or running the container privileged. Note that running Frigate inside an LXC is not officially supported. See the [installation docs](/frigate/installation#proxmox) for details.
+
+### Failed to download frame: -5
+
+When using VAAPI or QSV hardware acceleration, ffmpeg may crash and restart periodically with a signature like this in the `ffmpeg..detect` log:
+
+```
+[AVHWFramesContext @ 0x...] Failed to sync surface ... (operation failed).
+[hwdownload @ 0x...] Failed to download frame: -5.
+[vf#0:0 @ 0x...] Error while filtering: Input/output error
+[vf#0:0 @ 0x...] Task finished with error code: -5 (Input/output error)
+[frigate.video] : Unable to read frames from ffmpeg process.
+```
+
+This is a hardware frame synchronization failure between ffmpeg and the GPU driver, not a Frigate bug. It comes from how a specific camera stream interacts with the GPU's decode and scaling path, so it is highly dependent on your hardware, driver, and stream. Frigate's automatic hardware acceleration detection is a best-guess effort, so the fix is usually to tune the configuration for your specific hardware and camera. The solutions below are ordered from most to least likely to help:
+
+- **Switch between the VAAPI and QSV presets.** On Intel Gen 12 and newer iGPUs, `preset-intel-qsv-h264` / `preset-intel-qsv-h265` is often more stable than the auto-detected `preset-vaapi`. See the [hardware acceleration docs](/configuration/hardware_acceleration_video.md#intel-based-cpus) for the recommended preset for your Intel generation.
+- **Try a different VAAPI driver.** The default driver is `iHD`. On older Intel CPUs, `LIBVA_DRIVER_NAME=i965` can be more stable; on AMD GPUs use `LIBVA_DRIVER_NAME=radeonsi`. See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md#intel-based-cpus) for how to set the driver.
+- **Use a codec that decodes more reliably.** H.265/HEVC streams may trigger this error far more often than H.264 depending on your CPU generation. If your camera exposes a separate sub-stream, assign an H.264 stream to the `detect` role. Cameras that output full-range YUV (for example some Hikvision models) are especially prone to it.
+- **Match the detect resolution to the stream resolution.** When the `detect` resolution differs from the stream, Frigate inserts a GPU scaling filter (`scale_vaapi`), which is where these surface-sync failures can often originate. Set the `detect` `width` and `height` to match the exact resolution of the stream assigned the `detect` role.
+- **Match the detect `fps` to the camera stream.** Aggressively dropping frames (for example `detect` `fps: 1` on a stream that runs at 15 fps) can cause timing mismatches in the GPU's frame buffer. Lower the sub-stream's frame rate on the camera itself instead of dropping most frames in Frigate.
+- **Fall back to software decoding.** If none of the above resolve it, remove the preset for that camera (`hwaccel_args: []`). Hardware decoding is only an optimization. On a capable CPU, software-decoding a low-resolution sub-stream is inexpensive and gives a stable detect pipeline.
diff --git a/docs/docs/troubleshooting/recordings.md b/docs/docs/troubleshooting/recordings.md
index b1f180a82d..04b0240557 100644
--- a/docs/docs/troubleshooting/recordings.md
+++ b/docs/docs/troubleshooting/recordings.md
@@ -3,17 +3,13 @@ id: recordings
title: Recordings Errors
---
-## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?
+import FaqItem from "@site/src/components/FaqItem";
-You'll want to:
+## Why are my recordings not working? (empty Recordings, "No recordings found for this time")
-- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
-- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
-- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+If Frigate shows live video but the History view is empty, or you see "No recordings found for this time", the cause is almost always in one of the three categories below. Segments are first written to the RAM cache and are only moved to disk if they match a retention policy _and_ the camera's `record` stream is producing valid, storable video. Work through the categories in order: retention configuration is by far the most common cause.
-## I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
-
-This error can be caused by a number of different issues. The first step in troubleshooting is to enable debug logging for recording. This will enable logging showing how long it takes for recordings to be moved from RAM cache to the disk.
+Before diving in, enable debug logging for the recording maintainer so you can see whether segments are being written to disk at all:
```yaml
logger:
@@ -21,31 +17,284 @@ logger:
frigate.record.maintainer: debug
```
-This will include logs like:
+A healthy camera logs lines like `Copied /media/frigate/recordings/{segment_path} in 0.2 seconds`. If you never see these, no segments are reaching disk, which points at the camera/stream or storage sections below.
+
+### Retention configuration issues
+
+
+
+This is the single most common cause. Setting `record.enabled: True` on its own does **not** keep any footage: **continuous recording is disabled by default**, and segments in the cache are only moved to disk if they match a configured retention policy. You must configure at least one of `continuous`, `motion`, `alerts`, or `detections` retention.
+
+To store all video (the most conservative option), configure continuous retention:
+
+```yaml
+record:
+ enabled: True
+ continuous:
+ days: 3 # keep all footage for 3 days
+```
+
+See [Recording](/configuration/record) for the full set of common configurations, including reduced-storage and alerts-only setups.
+
+
+
+
+
+If you only configured `motion`, `alerts`, or `detections` retention (with no `continuous`), Frigate keeps footage selectively based on the retention `mode`:
+
+- **`mode: motion`** (the default) only retains segments that contain motion. If your [motion masks](/configuration/motion_detection) cover the areas where activity happens, or your motion sensitivity is too low, nothing will be retained even though recording is "on".
+- **`mode: active_objects`** only retains segments where a tracked object was actively moving.
+- **`mode: all`** retains every segment in the window.
+
+If you expected continuous footage but only configured motion/event retention, add a `continuous` retention period as shown above. To verify motion is actually being detected, watch the motion boxes in the debug view or the Motion Tuner in the UI.
+
+
+
+
+
+`alerts` and `detections` retention only keep footage that overlaps a tracked object, so they depend on object detection running:
+
+- **Detection must be enabled.** If `detect: enabled: False`, no alerts or detections are ever created, so alert/detection retention keeps nothing. (Continuous and motion retention still work with detection disabled.)
+- **The object must be supported by your model.** If you track an object your model doesn't support (for example `deer` or `license_plate` on the default model), Frigate never detects it and never records for it. Check your logs for warnings such as `... is configured to track ['deer'] objects, which are not supported by the current model` and remove unsupported objects or switch to a model (e.g. [Frigate+](/plus/)) that includes them.
+
+
+
+
+
+Configuration keys change between major versions. The old `clips` config, for example, has not existed for a long time. If you copied a config from an old blog post or video, verify every key against the current [reference config](/configuration/advanced/reference).
+
+
+
+### Camera and stream issues
+
+
+
+Frigate stores recordings in an MP4 container, and some camera audio codecs (most commonly `pcm_alaw`, `pcm_mulaw`, or other G.711 variants) **cannot be placed in an MP4 container**. When this happens, ffmpeg fails to write the segment and no recording is saved, even though the live view works fine. This is a frequent cause on Tapo, TP-Link VIGI, and some Reolink cameras.
+
+Transcode the audio to AAC (or drop it entirely) using the appropriate [ffmpeg preset](/configuration/ffmpeg_presets):
+
+```yaml
+cameras:
+ your_camera:
+ ffmpeg:
+ output_args:
+ record: preset-record-generic-audio-aac # transcode audio to AAC
+ # or preset-record-generic to record with no audio
+```
+
+
+
+
+
+A message like `No new recording segments were created for in the last 120s` means ffmpeg cannot read the `record` stream. To diagnose:
+
+- Confirm a stream is actually assigned the `record` role in your camera's `ffmpeg.inputs`.
+- Open the go2rtc web interface on port `1984` and click each stream to confirm it plays. go2rtc errors such as `wrong response on DESCRIBE` or `start from CONN state` indicate the camera connection is failing.
+- Test the exact RTSP URL (with the correct path, port, and credentials) in VLC or `ffplay`.
+- If you restream through go2rtc, make sure the `record` input path points at the correct go2rtc stream name. Copying a config between cameras without updating the stream name is a common mistake.
+
+
+
+### Storage and mounting issues
+
+
+
+If the recordings volume (`/media/frigate`) points at the wrong location, isn't writable, or a network/encrypted mount failed to mount at boot, Frigate cannot save recordings, or it silently writes to the boot drive and then purges aggressively because the drive appears far smaller than expected.
+
+- Compare the host's real capacity (`df -h`) against what the **Storage** page in the Frigate UI reports. A mismatch (for example Frigate reporting ~220 GB when your storage drive is 4 TB) means the bind mount is resolving to the wrong filesystem.
+- Verify the host path in your Docker `volumes` mapping (`- /your/storage:/media/frigate`) exists and is writable by the container.
+- For a mount that may fail intermittently, protecting the mount point with `chattr +i` on an empty directory forces Frigate to error out (rather than silently writing to the boot drive) when the mount is missing.
+- Check `dmesg` and system logs for filesystem or I/O errors around the time recordings disappeared.
+
+If recordings _are_ being written but the copy is too slow to keep up, see the ["Unable to keep up with recording segments"](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) question below.
+
+
+
+## Recordings won't play back
+
+
+
+When a recording refuses to play in the Frigate UI and you see an error like `Failed to play recordings (error 3): PIPELINE_ERROR_DECODE`, the message is coming from **your browser**, not from Frigate. `PIPELINE_ERROR_DECODE` is emitted exclusively by the media pipeline in **Chromium-based browsers** (Chrome, Edge, Brave, Vivaldi, Opera, Arc, and the Android WebView used by many in-app browsers) when the browser cannot decode a video or audio packet in the recording. WebKit browsers (Safari) report the same underlying problem with a different message, usually `Media failed to decode` or `DECODER_ERROR_NOT_SUPPORTED`.
+
+Frigate copies the `record` stream to disk **without re-encoding it**, so the browser must decode exactly what your camera produced, and Chromium's decoder is far stricter about malformed or nonstandard media than VLC or ffmpeg.
+
+:::warning
+
+The same recording playing perfectly in VLC, decoding cleanly with `ffprobe`/`ffmpeg`, or having a valid MP4 container does **not** mean the browser can decode it. VLC and ffmpeg are much more tolerant of codec quirks and damaged packets than a browser's media pipeline, so a "valid" file can still trigger `PIPELINE_ERROR_DECODE`. This is outside of Frigate's control, because Frigate never modifies the recording stream.
+
+:::
+
+#### Step 1: Confirm it is a browser issue
+
+Open the same recording in **Firefox** or **Safari**. Firefox and Safari both use a different media engine and cannot produce `PIPELINE_ERROR_DECODE`, so if playback works there you have confirmed a client-side codec or decoder problem rather than a bad recording. Switching browsers is a workaround, not a fix; the remaining steps address the root cause so that Chromium browsers work too.
+
+#### Step 2: Rule out H.265 / HEVC
+
+Browser support for H.265 (HEVC) is limited and depends on the operating system, GPU, hardware acceleration, and browser version, which makes it the most common cause of this error. Options, in order of reliability:
+
+- **Record H.264 instead.** Configure the camera's `record`/main stream to output H.264, the most compatible codec across all browsers. See [camera settings recommendations](/configuration/live#camera-settings-recommendations).
+- **Transcode to H.264 with go2rtc.** If you must keep HEVC on the camera, have go2rtc re-encode the recording stream. This increases CPU usage; add `#hardware` to use the GPU where available:
+
+ ```yaml
+ go2rtc:
+ streams:
+ your_camera:
+ # transcode video to h264 and audio to aac; #hardware uses the GPU if available
+ - "ffmpeg:rtsp://user:password@CAMERA_IP:554/stream#video=h264#audio=aac#hardware"
+ cameras:
+ your_camera:
+ ffmpeg:
+ inputs:
+ - path: rtsp://127.0.0.1:8554/your_camera
+ input_args: preset-rtsp-restream
+ roles:
+ - record
+ ```
+
+ The `#video=h264` parameter only takes effect with the `ffmpeg:` source module; adding it to a plain `rtsp://` go2rtc source does nothing.
+
+- **Keep HEVC but improve compatibility.** If your browser and OS do support HEVC, set [`apple_compatibility`](/configuration/camera_specific#h265-cameras-via-safari) on the camera. Some players (Safari and other clients) require a specific HEVC stream format that this option corrects:
+
+ ```yaml
+ cameras:
+ your_camera:
+ ffmpeg:
+ apple_compatibility: true
+ ```
+
+ You may also need to enable HEVC and hardware decoding in the browser itself (for example, Chrome's Settings → System → "Use hardware acceleration when available"). HEVC hardware support varies widely by GPU, OS, and browser version.
+
+#### Step 3: Clean up damaged packets from the camera
+
+If the error is **intermittent** (the same recording plays after a page refresh, or fails only after playing for a while), the camera is most likely emitting occasional corrupt or malformed packets. Some camera models are more prone to this than others. Routing the stream through go2rtc's `ffmpeg` module often "cleans up" the stream enough for the browser to decode it, even without changing the codec:
+
+```yaml
+go2rtc:
+ streams:
+ your_camera:
+ - "ffmpeg:rtsp://user:password@CAMERA_IP:554/stream#video=h264#audio=aac"
+```
+
+#### Step 4: Fix incompatible or corrupt audio
+
+Audio is one of the most common culprits, and a decode failure on the audio track fails the whole recording. Make sure the camera outputs **AAC** audio, transcode the audio to AAC with go2rtc (`#audio=aac`), or drop audio entirely. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save) for a preset-based way to do this.
+
+#### Step 5: Avoid "smart" / "+" codecs and check the keyframe interval
+
+- Disable any **"Smart Codec"**, **"H.264+"**, or **"H.265+"** feature in the camera. These nonstandard modes drop keyframes and change encoding parameters mid-stream, producing exactly the kind of packets a browser refuses to decode. (They also cause [short recording segments](#segments-are-only-1-second-long).)
+- Set the camera's **I-frame (keyframe) interval equal to the frame rate** (for example `20` for a 20 fps stream). Long keyframe intervals slow the start of playback and make decode errors more likely.
+
+#### Step 6: Consider bitrate and the client hardware
+
+The browser decodes the video locally, so a stream that is too demanding can fail on one device while playing on another:
+
+- A **very high bitrate or resolution** (for example a 4K/8MP HEVC main stream) can overwhelm a low-power tablet, phone, or SBC and stall the decoder. Test the same recording on a desktop; if it plays there, lower the camera's bitrate or record a lower-resolution profile.
+- Errors that name the client's GPU decoder, such as `VaapiVideoDecoder: failed Initialize()ing the frame pool`, indicate a browser hardware-decode problem. Toggling the browser's "Use hardware acceleration" setting (on or off) often resolves these.
+
+
+
+
+
+Frigate copies the `record` stream directly without re-encoding, so playback depends on your browser supporting the camera's codec. H265/HEVC recordings may not be playable in some browsers. If recordings appear as audio-only or a black screen, your camera is likely sending a codec your browser can't decode. Configure the camera to output **H264** for maximum compatibility.
+
+If playback instead fails with an explicit `PIPELINE_ERROR_DECODE` or `Media failed to decode` error, see [Recordings won't play back with "PIPELINE_ERROR_DECODE"](#pipeline-error-decode) above.
+
+
+
+## Recording cache warnings and errors
+
+
+
+If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parameters mid-stream, corrupt timestamps cause segments to be split far too frequently and fill the cache. This produces the "Too many unprocessed recording segments" warning. See [that question below](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) for the full diagnosis.
+
+
+
+
+
+Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
+
+- `Invalid or missing video stream in segment . Discarding.` The segment holds no video, or could not be read at all.
+- `Failed to probe corrupt segment ` followed by `Discarding a corrupt recording segment: `. The segment was read, but its length could not be determined.
+- `Discarding a corrupt recording segment: ` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
+
+For each one, the camera watchdog also logs `Invalid recording segment detected for at `.
+
+:::warning
+
+This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
+
+:::
+
+#### Start with the camera and the network
+
+- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
+- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
+- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
+
+Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
+
+#### If the camera and network check out
+
+- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
+- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
+- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
+
+
+
+
+
+When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
+
+- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
+- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
+
+The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
+
+
+
+
+
+This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
+
+#### Step 1: Enable recording debug logging
+
+The first step is to measure how long each segment takes to move from the RAM cache to disk. Enable debug logging for the recording maintainer:
+
+```yaml
+logger:
+ logs:
+ frigate.record.maintainer: debug
+```
+
+This adds log lines showing the copy duration for each segment:
```
DEBUG : Copied /media/frigate/recordings/{segment_path} in 0.2 seconds.
```
-It is important to let this run until the errors begin to happen, to confirm that there is not a slow down in the disk at the time of the error.
+Let this run until the warnings begin to appear, so you can confirm whether the disk is actually slowing down at the moment the error occurs.
-#### Copy Times > 1 second
+#### Step 2: Interpret the copy times
-If the storage is too slow to keep up with the recordings then the maintainer will fall behind and purge the oldest recordings to ensure the cache does not fill up causing a crash. In this case it is important to diagnose why the copy times are slow.
+The copy duration tells you which direction to investigate:
-##### Check RAM, swap, cache utilization, and disk utilization
+- **Consistently longer than ~1 second**: your storage cannot keep up with the incoming recordings. Continue with Steps 3–5 to diagnose the slow storage.
+- **Consistently well under 1 second**: storage is fast enough, and the problem is more likely CPU or resource contention. Skip to Step 6.
-If CPU, RAM, disk throughput, or bus I/O is insufficient, nothing inside frigate will help. It is important to review each aspect of available system resources.
+#### Step 3: Check RAM, swap, cache, and disk utilization
-On linux, some helpful tools/commands in diagnosing would be:
+If CPU, RAM, disk throughput, or bus I/O is insufficient, nothing inside Frigate will help. Review each aspect of available system resources while the warnings are occurring.
-- docker stats
-- htop
-- iotop -o
-- iostat -sxy --human 1 1
-- vmstat 1
+On Linux, some helpful tools/commands for diagnosing this are:
-On modern linux kernels, the system will utilize some swap if enabled. Setting vm.swappiness=1 no longer means that the kernel will only swap in order to avoid OOM. To prevent any swapping inside a container, set allocations memory and memory+swap to be the same and disable swapping by setting the following docker/podman run parameters:
+- `docker stats`
+- `htop`
+- `iotop -o`
+- `iostat -sxy --human 1 1`
+- `vmstat 1`
+
+On modern Linux kernels, the system will use some swap if it is enabled. Setting `vm.swappiness=1` no longer means the kernel will only swap in order to avoid OOM. To prevent any swapping inside the container, set the memory and memory+swap allocations to the same value and disable swapping by setting the following docker/podman run parameters:
**Docker Compose example**
@@ -67,16 +316,175 @@ services:
--memory= --memory-swap= --memory-swappiness=0
```
-NOTE: These are hard-limits for the container, be sure there is enough headroom above what is shown by `docker stats` for your container. It will immediately halt if it hits ``. In general, running all cache and tmp filespace in RAM is preferable to disk I/O where possible.
+NOTE: These are hard limits for the container, so be sure there is enough headroom above what `docker stats` shows for your container. It will immediately halt if it hits ``. In general, keeping all cache and tmp filespace in RAM is preferable to disk I/O where possible.
-##### Check Storage Type
+#### Step 4: Check your storage type
-Mounting a network share is a popular option for storing Recordings, but this can lead to reduced copy times and cause problems. Some users have found that using `NFS` instead of `SMB` considerably decreased the copy times and fixed the issue. It is also important to ensure that the network connection between the device running Frigate and the network share is stable and fast.
+Mounting a network share is a popular option for storing recordings, but it can lead to reduced copy times and cause problems. Some users have found that using `NFS` instead of `SMB` considerably decreased copy times and fixed the issue. It is also important to ensure that the network connection between the device running Frigate and the network share is stable and fast. A saturated or unreliable link will stall copies.
-##### Check mount options
+#### Step 5: Check your mount options
-Some users found that mounting a drive via `fstab` with the `sync` option caused dramatically reduce performance and led to this issue. Using `async` instead greatly reduced copy times.
+Some users found that mounting a drive via `fstab` with the `sync` option dramatically reduced performance and led to this issue. Using `async` instead greatly reduced copy times.
-#### Copy Times < 1 second
+#### Step 6: Rule out CPU load
-If the storage is working quickly then this error may be caused by CPU load on the machine being too high for Frigate to have the resources to keep up. Try temporarily shutting down other services to see if the issue improves.
+If the copy times are consistently under 1 second but you still see the warning, the machine's CPU load is likely too high for Frigate to have the resources to keep up. Try temporarily shutting down other services, and any resource-intensive Frigate features, to see if the issue improves.
+
+
+
+
+
+This warning means that the detect stream for the affected camera has fallen behind or stopped processing frames. Frigate's recording cache holds segments waiting to be analyzed by the detector. When more than 6 segments pile up without being processed, Frigate discards the oldest ones to prevent the cache from filling up.
+
+:::warning
+
+This error is a **symptom**, not the root cause. The actual cause is always logged **before** these messages start appearing. You must review the full logs from Frigate startup through the first occurrence of this warning to identify the real issue.
+
+:::
+
+#### Step 1: Get the full logs
+
+Collect complete Frigate logs from startup through the first occurrence of the error. Look for errors or warnings that appear **before** the "Too many unprocessed" messages begin. That is where the root cause will be found.
+
+#### Step 2: Check the cache directory
+
+Exec into the Frigate container and inspect the recording cache:
+
+```
+docker exec -it frigate ls -la /tmp/cache
+```
+
+Each camera should have a small number of `.mp4` segment files. If one camera has significantly more files than others, that camera is the source of the problem. A problem with a single camera can cascade and cause all cameras to show this error.
+
+#### Step 3: Verify segment duration
+
+Recording segments should be approximately 10 seconds long. Run `ffprobe` on segments in the cache to check:
+
+```
+docker exec -it frigate ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1 /tmp/cache/@.mp4
+```
+
+If segments are only ~1 second instead of ~10 seconds, the camera is sending corrupt timestamp data, causing segments to be split too frequently and filling the cache 10x faster than expected.
+
+**Common causes of short segments:**
+
+- **"Smart Codec" or "Smart+" enabled on the camera**: These features dynamically change encoding parameters mid-stream, which corrupts timestamps. Disable them in your camera's settings.
+- **Changing codec, bitrate, or resolution mid-stream**: Any encoding changes during an active stream can cause unpredictable segment splitting.
+- **Camera firmware bugs**: Check for firmware updates from your camera manufacturer.
+
+:::tip
+
+You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera Probe Info** dialog (the info icon on the System → Metrics → Cameras page) and check the **Keyframe analysis** section. It probes the record stream and flags sparse or variable keyframes, which is what smart/"+" codecs (H.264+/H.265+) and long keyframe intervals produce.
+
+:::
+
+#### Step 4: Check for a stuck detector
+
+If the detect stream is not processing frames, segments will accumulate. Common causes:
+
+- **Detection resolution too high**: Use a substream for detection, not the full resolution main stream.
+- **Detection FPS too high**: 5 fps is the recommended maximum for detection.
+- **Model too large**: Use smaller model variants (e.g., YOLO `s` or `t` size, not `e` or `x`). Use 320x320 input size rather than 640x640 unless you have a powerful dedicated detector.
+- **Virtualization**: Running Frigate in a VM (especially Proxmox) can cause the detector to hang or stall. This is a known issue with GPU/TPU passthrough in virtualized environments and is not something Frigate can fix. Running Frigate in Docker on bare metal is recommended.
+
+#### Step 5: Check for GPU hangs
+
+On the host machine, check `dmesg` for GPU-related errors:
+
+```
+dmesg | grep -i -E "gpu|drm|reset|hang"
+```
+
+Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
+
+#### Step 6: Verify hardware acceleration configuration
+
+An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
+
+- After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
+- For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
+- Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
+
+#### Step 7: Verify go2rtc stream configuration
+
+Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
+
+#### Step 8: Check system resources
+
+If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
+
+- **CPU usage**: An overloaded CPU can prevent the detector from keeping up.
+- **RAM and swap**: Excessive swapping dramatically slows all I/O operations.
+- **Disk I/O**: Use `iotop` or `iostat` to check for saturation.
+- **Storage space**: Verify you have free space on the Frigate storage volume (check the Storage page in the Frigate UI).
+
+Try temporarily disabling resource-intensive features like `genai` and `face_recognition` to see if the issue resolves. This can help isolate whether the detector is being starved of resources.
+
+
+
+
+
+This message means the recording maintainer hit an error while moving segments from the cache to disk. It is a **generic wrapper**: the actual cause is always logged on the **very next line**. Frigate usually recovers and keeps running, but any affected segments are lost, so it is worth resolving.
+
+:::warning
+
+Always read the line immediately following this message. `Error occurred when attempting to maintain recording cache` on its own tells you nothing; the exception on the next line (for example `[Errno 28] No space left on device` or `[Errno 17] File exists`) is the real problem.
+
+:::
+
+Because these are operating-system-level errors, they must be resolved on the **host**, not within Frigate's configuration. The most common underlying errors are below.
+
+#### [Errno 28] No space left on device
+
+The filesystem Frigate is writing to is full. Things to check:
+
+- **The recordings volume is genuinely full.** Check free space on the host with `df -h` for the path mapped to `/media/frigate`, and review the **Storage** page in the Frigate UI.
+- **The disk shows free space but is still "full".** This usually means the filesystem has run out of **inodes** (check with `df -i`), or recordings are landing on a different, smaller filesystem than you expect because of an incorrect bind mount. See [The storage volume isn't mounted correctly](#the-storage-volume-isnt-mounted-correctly) above.
+- **`/tmp/cache` is full.** If you mounted `/tmp/cache` as a small `tmpfs`, a backlog of segments can fill it. Increase the tmpfs size, or address whatever is causing segments to pile up (see the [Too many unprocessed recording segments](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) question above).
+- **The host blocks writes before Frigate can purge.** On some systems (for example Unraid with a fill-up threshold), the host stops writes before Frigate's emergency cleanup can run. Leave more headroom on the volume, or lower your retention so Frigate purges sooner.
+
+#### [Errno 17] File exists (with ffmpeg "Error writing trailer" or "unable to re-open output file")
+
+Errors like `[Errno 17] File exists: '/media/frigate/recordings/.../'`, often alongside ffmpeg errors such as `Unable to re-open ... output file for shifting data` or `Error writing trailer: No such file or directory`, are a hallmark of an **unreliable network share** (NFS or SMB). The mount is dropping, serving stale directory entries, or mishandling file locking.
+
+- Confirm the network connection to the NAS is stable and fast. An intermittent link produces these errors sporadically.
+- Prefer **NFS over SMB** for the recordings mount; several users have found NFS more reliable and faster.
+- Review your `fstab`/mount options for settings that hurt consistency or performance (see the `sync` vs `async` note in the [Unable to keep up with recording segments](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) question above).
+- Enable `frigate.record.maintainer` debug logging to confirm whether the errors line up with the share becoming unavailable.
+
+#### Errors referencing a camera you manually renamed or removed
+
+If the next-line error references a camera name that no longer exists in your config, orphaned data is left over from a rename or removal in a persistent `/tmp/cache` volume.
+
+- Using a `tmpfs` mount for `/tmp/cache` as recommended in the [installation docs](/frigate/installation#storage) prevents stale cache files under the old camera name from surviving a restart, which avoids this issue entirely.
+- If errors persist, stop Frigate and remove any leftover segments for the old camera name from `/tmp/cache`.
+
+
+
+## Other recording questions
+
+
+
+You'll want to:
+
+- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
+- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
+- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+
+
+
+
+
+The scrubbing previews (the timelapse clips shown when dragging the History timeline, the secondary-camera previews, and the preview that plays when hovering a review card) are not recorded continuously. Frigate caches low-resolution preview frames in `/tmp/cache` throughout each hour and only assembles them into a finished preview clip **at the top of the hour**.
+
+In the recommended configuration, `/tmp/cache` is a small in-memory (`tmpfs`) area. When Frigate starts, it tries to restore the current hour's cached frames, so a **soft restart from the UI** preserves them. But if you recreate the Docker container or stop Frigate forcibly by any other means partway through an hour, the in-memory cache is discarded, so no preview clip is produced for that partial hour.
+
+This is expected behavior, not a bug:
+
+- Previews for hours that already completed and were written to disk are unaffected.
+- The next full hour after a restart will generate previews normally.
+- This is unrelated to `shm_size`; increasing shared memory does not change it.
+
+To avoid the gap, use the **Restart Frigate** button in the UI's Settings menu rather than recreating the container when possible.
+
+
diff --git a/docs/docs/usage/explore.md b/docs/docs/usage/explore.md
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+++ b/docs/docs/usage/explore.md
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+---
+id: explore
+title: Explore
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**Explore** is where you browse and search every **tracked object** Frigate has saved. By default it groups recent objects by label; when [Semantic Search](/configuration/semantic_search) is enabled, you can also search by natural-language description or visual similarity. Selecting any object opens a detail pane with its snapshot, lifecycle, and metadata.
+
+This page describes how to _use_ the Explore view. For how the underlying features are _configured_, see [Semantic Search](/configuration/semantic_search) and [Generative AI descriptions](/configuration/genai/genai_objects).
+
+:::tip
+
+If you just want to quickly see what happened on your cameras, it's recommended to use [Review](/usage/review) rather than Explore. Review groups overlapping and adjacent activity on a camera into **review items** and sorts them into Alerts, Detections, and Motion, so you can scan and play back footage in a few clicks instead of sifting through individual objects. Reach for Explore when you need to find a _specific_ tracked object after the fact: by label, time, zone, or description.
+
+:::
+
+## Browsing tracked objects
+
+The default view shows your most recent tracked objects grouped into rows by label (_Person_, _Car_, _Dog_, and so on), each row labeled with the object type and a count. The arrow at the end of a row opens the full, filterable grid for that label.
+
+Clicking a thumbnail opens its [detail dialog](#tracked-object-details); right-clicking or long-pressing a thumbnail opens an [actions menu](#actions-and-bulk-selection). You can switch to a denser grid layout and adjust the number of columns from the view's settings.
+
+## Searching
+
+When [Semantic Search](/configuration/semantic_search) is enabled, a search bar appears that combines two things in one input:
+
+- **Natural-language search**: type a free-text query and press Enter to run a semantic search over your tracked objects.
+- **Filter tokens**: type a `key:` to get suggestions, then a value, to add a structured filter. Each filter becomes a removable chip, and you can chain several together.
+
+You can save a search with the star icon and reload it later, and clear everything with the clear-search icon. A help popover explains the token syntax, for example:
+
+```
+cameras:front_door label:person before:01012024 time_range:3:00PM-4:00PM
+```
+
+### Filter reference
+
+The most common filter tokens are:
+
+| Filter | Description |
+| ---------------------------- | ---------------------------------------------------------------------------------- |
+| **Cameras** | Limit to one or more cameras. |
+| **Labels** | Object labels (person, car, etc.). |
+| **Sub Labels** | Recognized sub labels (e.g. a recognized face or name). |
+| **Attributes** | Classification attributes applied to the object. |
+| **Recognized License Plate** | Match a recognized plate. |
+| **Zones** | Objects that entered specific zones. |
+| **Before / After** | Restrict to a date range. |
+| **Time Range** | Restrict to a time of day (`HH:MM-HH:MM`). |
+| **Min / Max Score** | Restrict by the object's confidence score. |
+| **Min / Max Speed** | Restrict by estimated speed (when speed estimation is configured). |
+| **Has Snapshot / Has Clip** | Only objects that saved a snapshot or recording. |
+| **Submitted to Frigate+** | Only objects already submitted (when Frigate+ is enabled). |
+| **Search Type** | Whether semantic search matches the object's **Thumbnail** or its **Description**. |
+
+### Sorting
+
+When a filter or search is active, a **Sort** control lets you order results by **date**, **object score**, or **estimated speed** (ascending or descending). When a semantic query or similarity search is active, results can also be ordered by **relevance**.
+
+### Thumbnail and description search
+
+- The **Search Type** setting controls whether a text query is matched against each object's **thumbnail** or its **description**. Each result indicates which one it matched and the confidence.
+
+Natural-language search, thumbnail search, and description search all require [Semantic Search](/configuration/semantic_search) to be enabled.
+
+## Tracked Object Details
+
+Selecting an object opens the **Tracked Object Details** dialog. Use the arrows (or the left/right keys) to step to the previous or next object. The dialog has two tabs:
+
+- **Snapshot** or **Thumbnail**: the saved snapshot (or thumbnail).
+- **Tracking Details**: the object's lifecycle, available when the object has a recording. It lists each significant moment (detected, entered a zone, became active or stationary, left, and so on); clicking a moment plays that part of the recording with the bounding box overlaid. A settings popover lets you show all zones and adjust the annotation offset.
+
+The details pane shows the object's **label**, **scores**, **camera**, **timestamp**, estimated **speed**, any **recognized license plate** and **classification attributes**, and its **description**. Admins can edit the sub label, license plate, and attributes inline.
+
+The **description** can be edited by hand, and, when [Generative AI descriptions](/configuration/genai/genai_objects) are enabled and the object's lifecycle has ended, regenerated from the snapshot or from thumbnails. For `speech` objects, a **Transcribe** action is available when audio transcription is enabled. When [Frigate+](/integrations/plus) is enabled, admins can submit a snapshot to improve their model directly from this pane.
+
+## Actions and bulk selection
+
+Right-clicking or long-pressing an object (in the grid or its thumbnail) opens an actions menu with options to **download** the video, snapshot, or a clean snapshot; **view tracking details**; **find similar**; **add a trigger**; **view in History**; and **delete the tracked object**.
+
+:::note
+
+Deleting a tracked object removes its snapshot, embeddings, and tracking-details entries, but the recorded footage of that object in [History](/usage/history) is **not** deleted.
+
+:::
+
+To act on many objects at once, Ctrl/Cmd-click or right-click to start a selection (selected tiles gain a blue ring), then use the toolbar to select all, clear the selection, or delete (admins).
+
+## Semantic Search - Usage and best practices {#usage-and-best-practices}
+
+1. Semantic Search is used in conjunction with the other filters available on the Explore page. Use a combination of traditional filtering and Semantic Search for the best results.
+2. Use the thumbnail search type when searching for particular objects in the scene. Use the description search type when attempting to discern the intent of your object.
+3. Because of how the AI models Frigate uses have been trained, the comparison between text and image embedding distances generally means that with multi-modal (`thumbnail` and `description`) searches, results matching `description` will appear first, even if a `thumbnail` embedding may be a better match. Play with the "Search Type" setting to help find what you are looking for. Note that if you are generating descriptions for specific objects or zones only, this may cause search results to prioritize the objects with descriptions even if the ones without them are more relevant.
+4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
+5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
+6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
+
+## Triggers
+
+From an object's actions menu, **Add trigger** sets up a per-camera trigger that uses Semantic Search to automate an action (a notification, sub label, or attribute) whenever a similar object appears. Triggers require Semantic Search and are managed under . See [Triggers](/configuration/semantic_search#triggers) for full configuration and best practices.
diff --git a/docs/docs/usage/exports.md b/docs/docs/usage/exports.md
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+---
+id: exports
+title: Exports
+---
+
+**Exports** are how you keep a specific piece of footage permanently.
+
+Frigate's recordings are governed by your [retention settings](/configuration/record): once footage ages past its retention window (or, depending on your configuration, once it is only kept where motion, alerts, or detections occurred), it is deleted to free up disk space. An **export** saves a copy of a chosen time range to a separate location that is **never removed by retention**, so it stays available until you delete it yourself.
+
+This is the answer to the common question _"how do I stop Frigate from deleting an important clip?"_ Instead of increasing retention for an entire camera (which uses far more storage to protect a single moment), export just the footage you want to keep.
+
+:::tip
+
+Exports are stored under `/media/frigate/exports`, separate from your recordings, and are not counted against or removed by recording retention. They remain on disk until you delete them, so be aware that they accumulate over time.
+
+:::
+
+## Creating an export
+
+There are a few ways to create an export:
+
+- **From Review**: select (right click or long-press) an individual review item directly, and choose Export from the header menu. You can also select multiple review items and export them all at once, optionally grouping them into a [case](#cases).
+- **From History**: open the **Actions** menu and choose **Export**. You can export a preset duration (the last 1, 4, 8, 12, or 24 hours), enter a custom start and end time, or select a range directly on the timeline. A **multi-camera** option lets you export the same time range across several cameras at once.
+
+In every case you can give the export a name. Frigate then saves the footage from your recordings as a single video file. Larger ranges take time to process; the export is marked _in progress_ until it finishes, and you can keep using Frigate while it runs.
+
+## Managing exports
+
+All of your exports live on the **Exports** page, reachable from the main navigation, where you can search for one by name. Each export offers the following actions:
+
+- **Play** it in the browser,
+- **Download** it to save the footage outside of Frigate,
+- **Share** it: copies a direct link to the export (or uses your device's share sheet),
+- **Rename** it, and
+- **Delete** it: deleting is the only way an export is removed.
+
+You can also select multiple exports at once to **delete** them in bulk, or to **add them to** (or **remove them from**) a [case](#cases). To download multiple exports as a zip archive, add them to a **case** and use the Download button there.
+
+## Cases
+
+A **case** groups related exports together: for example, all the clips from a single incident across multiple cameras. On the **Exports** page you can create a case with a name and description, add existing exports to it (or create a new case while exporting), and **download the entire case as a single archive** to hand off as one package.
+
+Exports that don't belong to a case appear under **Uncategorized Exports**. Deleting a case lets you either keep its exports (they move back to uncategorized) or delete them along with the case.
diff --git a/docs/docs/usage/history.md b/docs/docs/usage/history.md
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+++ b/docs/docs/usage/history.md
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+---
+id: history
+title: History
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**History** is Frigate's full-resolution recording viewer. Unlike Live, Review, and Explore, there is no menu item for it. You reach it from within another view, then scrub the timeline, switch cameras, inspect a tracked object's lifecycle, and export or share any moment.
+
+This page describes how to _use_ the History view. For how recordings are _configured_ (retention, pre/post capture), see [Recording](/configuration/record).
+
+## Opening History
+
+You can open History from several places:
+
+- **From [Review](/usage/review):** clicking a review item opens its recording, scrubbed to just before the activity on that camera.
+- **From [Live](/usage/live):** the **History** button in a camera's single-camera view opens that camera about 30 seconds in the past.
+- **From a share link:** opening a shared timestamp link (see [Share Timestamp](#the-actions-menu) below) jumps straight to that camera and moment.
+
+Use the **Back** button to return where you came from, or the **Live** button to jump to the current camera's live view.
+
+:::tip
+
+If you see **"No recordings found for this time"**, the most common causes are: recording was not enabled for that camera at the time of the event; the retention window has since expired and those segments were removed; or storage ran low and Frigate deleted them early to free space. See [Recording](/configuration/record) to verify your retention settings.
+
+:::
+
+## Timeline, Events, and Detail
+
+A toggle (a drawer on mobile) switches the side panel between three modes:
+
+- **Timeline**: a scrubbable vertical timeline of the selected camera. Horizontal lines down the center represent motion, with longer lines indicating more motion at that moment. Review items are marked as shaded areas (**red** for alerts, **orange** for detections), and sections with no colored background are times when no recording exists.
+- **Events**: a scrollable list of the camera's review items for the time range; clicking one seeks the player to it.
+- **Detail**: the [tracking details inspector](#the-detail-view) for the objects in view.
+
+While you are selecting a range to export, the panel temporarily switches to Timeline.
+
+## Scrubbing and previews
+
+Drag the timeline handlebar to move through time; the main player and any secondary camera previews scrub together so everything stays in sync. Press the zoom buttons on the timeline to change its zoom level (from coarse to fine segments). Sections of the timeline with no recordings are shown as gaps.
+
+On desktop, when more than one camera is available, a **row of secondary previews** shows the other cameras at the same moment. Clicking one of them makes it the main camera at the current timestamp, so you can follow activity across cameras without losing your place. On mobile, use the camera drawer to switch cameras.
+
+## Filtering and the calendar
+
+You can filter History by **cameras** and **date**. The calendar behaves the same as it does in [Review](/usage/review#filtering-and-the-calendar): an **underline** under a day means recordings exist for that day, and a **colored dot** (red for unreviewed alerts, orange for unreviewed detections) marks days with unreviewed activity.
+
+## The Detail view
+
+The **Detail** mode turns the side panel into a tracking details inspector. It lists one card per review item, each showing the item's severity, start time, the object labels involved, a count of tracked objects, and the duration. The active card is highlighted as the video plays, and clicking a card seeks to it.
+
+Expanding a card reveals the **lifecycle** of each tracked object: a row for each significant moment (detected, entered a zone, became active, became stationary, left, and so on), with a progress line that follows the current playback position. Hovering a row shows that moment's score, ratio, and area, and clicking a row seeks the video to that exact timestamp.
+
+The **Detail View Settings** at the bottom let you toggle whether the active item's objects expand automatically, and adjust the **annotation offset**: a fine timing correction that aligns the bounding-box overlays with the recorded video when your camera's snapshot and recording timestamps drift. Admins can save the offset to the camera's configuration.
+
+## The Actions menu
+
+On desktop, the **Actions** menu (the film icon) collects the things you can do with the footage you are viewing:
+
+- **Export**: save a clip of a chosen time range so it is never removed by retention. The dialog pre-selects the last hour; adjust the range or drag the timeline handles, then export. See [Exports](/usage/exports) for managing and downloading exports.
+- **Share Timestamp**: generate a link to the current moment (or a custom timestamp) to share with another Frigate user. This is an internal link, not a public share URL.
+- **Motion Search**: scan this camera's recordings for changes in a region you draw. This is the same tool documented under [Reviewing Motion](/usage/review#motion-search).
+- **Debug Replay** (admins): replay a recorded range back through Frigate's detection pipeline to see how it would be processed.
+
+You can also capture an instant snapshot of the current frame, and submit a frame to [Frigate+](/integrations/plus) directly from the player (admins only).
+
+## AI review summaries
+
+When [Generative AI review](/configuration/genai/genai_review) is configured, Frigate can generate a title, description, and threat classification for review items and surface them as you scrub through History. A review item that has an AI summary exposes its details in a few places:
+
+- **Over the video**: when the item is on screen, a popup appears over the player.
+- **In the Events side panel**: items with a summary show the title below the thumbnail.
+- **In the Detail side panel**: the item's card shows the title alongside its tracking details.
+
+Clicking any of these opens the **AI Analysis** dialog with the generated detail and any flagged concerns for that item.
diff --git a/docs/docs/usage/live.md b/docs/docs/usage/live.md
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+++ b/docs/docs/usage/live.md
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+---
+id: live
+title: Live View
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**Live view** is Frigate's real-time dashboard and the page you land on by default. It shows all of your cameras at a glance, streams your most recent alerts across the top, and lets you open any camera in a full-resolution single-camera view with audio, two-way talk, PTZ, and on-demand recording controls.
+
+This page describes how to _use_ the Live view. For how to _configure_ live streaming (go2rtc, stream selection, smart streaming, WebRTC, and audio), see the [Live View configuration](/configuration/live) docs.
+
+## The dashboard at a glance
+
+The default **All Cameras** dashboard shows every camera, with a filmstrip of recent **alerts** scrolling across the top. Clicking an alert opens it in [Review](/usage/review); each card also has a check button to mark it reviewed without leaving the dashboard. Only **alerts** appear in the filmstrip. To suppress a label or zone from showing there, configure it as a detection instead (see [Alerts and Detections](/configuration/review#alerts-and-detections)).
+
+By default Frigate uses **smart streaming**: a camera's image updates roughly once per minute while nothing is happening, and switches to a full live stream the moment activity is detected. This conserves bandwidth and resources. You can change this for each camera when using a camera group (see [Streaming settings](#streaming-settings-and-the-right-click-menu) below), and the behavior is explained in detail under [Live view technologies](/configuration/live#live-view-technologies).
+
+On mobile, a toggle in the header switches between a **grid** layout and a single-column **list** layout. On desktop a **fullscreen** button is available in the lower-right corner.
+
+## Switching dashboards and camera groups
+
+The icon rail (top-left on desktop, a horizontal strip on mobile) switches between dashboards:
+
+- The **home** icon is the **All Cameras** dashboard, which shows every camera enabled for the dashboard.
+- Each **camera group** you create appears as its own icon. Selecting a group shows only that group's cameras.
+
+Camera groups are useful for organizing cameras by location (for example, _Front of House_ or _Backyard_) and for giving each group its own dashboard layout and camera streaming preferences.
+
+You can also view [Birdseye](/configuration/birdseye) on the dashboard, or open it directly at `http://:5000/#birdseye`. Clicking a camera inside the Birdseye view jumps to that camera's live feed.
+
+## Creating and editing camera groups
+
+Admins can manage groups from the pencil icon next to the group rail, which opens the **Camera Groups** dialog. From there you can add a group, or edit and delete existing ones. When creating a group you choose:
+
+- a **Name** (spaces are converted to underscores),
+- the **cameras** to include (each camera has a toggle and a gear that opens its [streaming settings](#streaming-settings-and-the-right-click-menu)), and
+- an **icon** used for the group's button in the rail.
+
+Deleting a group also clears any custom layout you saved for it.
+
+## Rearranging a camera group layout
+
+On desktop and tablet, each camera group has its own freely-arrangeable grid. Enter **Edit Layout** mode from the layout button in the lower-right corner: camera tiles gain a drag handle and corner resize handles. Drag a tile to reposition it and drag a corner to resize it (the aspect ratio is preserved). Exit edit mode to save. The layout is stored in your browser per device, so each device can have its own arrangement.
+
+The default **All Cameras** dashboard is not manually arrangeable. It automatically sizes tiles based on each camera's aspect ratio (wide cameras span two columns, tall cameras span two rows).
+
+## Reading the tile indicators
+
+Each camera tile surfaces its current state with a few overlays:
+
+- A **pulsing red dot** in the corner means **motion is currently detected** on that camera.
+- A **red outline** around the tile means an **active tracked object** is on that camera.
+- A small **label chip** lists the object types currently detected (for example, _Person_, _Car_).
+- A **camera-name label** appears when you have enabled always-on camera names, or when a camera is offline or disabled.
+- A **Stream Offline** or **Camera is off** placeholder appears when no frames are being received or the camera has been turned off.
+
+You can optionally overlay live streaming statistics (stream type, bandwidth, latency, and frame counts) on a tile to diagnose playback issues.
+
+## Streaming settings and the right-click menu
+
+Right-clicking (or long-pressing) a camera tile opens a context menu with quick controls: an **audio volume** control for streams that support audio, **Mute / Unmute all cameras**, **show or hide streaming statistics**, the **debug view**, **notification** options, and, for admins, turning the camera on or off. If the audio control doesn't appear, see [Audio Support](/configuration/live#audio-support). Audio requires go2rtc configured with a compatible codec.
+
+A **Low-bandwidth mode** notice may also appear in the context menu with a **Reset** option when Frigate has fallen back to the lower-quality jsmpeg stream. See the [Live view FAQ](/configuration/live#live-view-faq) for why this happens.
+
+For non-default groups, the context menu also exposes **Streaming Settings** for that camera, which let you choose:
+
+- the **stream** to display (the dropdown lists the streams you configured under [`live -> streams`](/configuration/live#setting-streams-for-live-ui), and indicates whether audio is available),
+- the **streaming method**: **No Streaming**, **Smart Streaming** (recommended), or **Continuous Streaming** (higher bandwidth), and
+- **compatibility mode**, for devices that have trouble rendering the default player.
+
+These settings are saved per group and per device in your browser, not in your config file.
+
+## The single-camera view
+
+Clicking a camera tile opens its full-resolution single-camera view. The top bar provides:
+
+- **Back** (also the `Esc` key) to return to the dashboard,
+- **History** to jump to the [recordings](/usage/history) for this camera, starting about 30 seconds in the past,
+- **Fullscreen** and **Picture-in-Picture** (if supported by your browser),
+- **Two-way talk** (the microphone button, which requires a supported camera and WebRTC; keyboard shortcut `t`), and
+- **Camera audio muting** (the speaker button; keyboard shortcut `m`).
+
+You can pinch or scroll to zoom into the feed. A **settings** gear provides a **stream** selector (with audio and two-way-talk availability indicators), **Play in background**, **Show stats**, and a **Debug view** that overlays Frigate's detection regions and bounding boxes.
+
+:::tip
+
+Two-way talk and camera audio have specific codec and port requirements. See [Audio Support](/configuration/live#audio-support) and [WebRTC](/configuration/live#webrtc-extra-configuration) for setup details.
+
+:::
+
+## Camera controls
+
+Admins get a row of toggles in the single-camera view (a settings drawer on mobile) to turn camera features on and off in real time:
+
+- **Camera** on/off,
+- **Object detection**,
+- **Recording** (only available when recording is enabled in the camera's config),
+- **Snapshots**,
+- **Audio detection**,
+- **Live audio transcription** (when audio detection is enabled), and
+- **Autotracking** (for [autotracking-capable PTZ cameras](/configuration/autotracking)).
+
+These toggles change runtime behavior immediately. Whether a change persists across a restart depends on the feature. See the relevant configuration page.
+
+## On-demand recording and snapshots
+
+The single-camera view can capture footage on demand:
+
+- **Start on-demand recording** begins a manual recording based on the camera's recording retention settings (the button pulses while active). If recording is disabled for the camera, only a snapshot is saved. Use **End on-demand recording** to stop.
+- **Download instant snapshot** saves a still image of the current frame.
+
+See [Recording](/configuration/record) and [Snapshots](/configuration/snapshots) for how retention is configured, and [Exports](/usage/exports) for keeping a clip permanently.
+
+## PTZ controls
+
+For ONVIF cameras that support it, a control panel provides pan/tilt arrows, **zoom**, **focus**, and saved **presets**. You can also enable a **click-to-move / drag-to-zoom** overlay: click a point in the frame to center the camera there, or drag a box to pan and zoom to that area (dragging top-left to bottom-right zooms in, the reverse zooms out).
+
+For continuous, automatic tracking of a moving object, see [Autotracking](/configuration/autotracking).
diff --git a/docs/docs/usage/review.md b/docs/docs/usage/review.md
new file mode 100644
index 0000000000..6416b500e1
--- /dev/null
+++ b/docs/docs/usage/review.md
@@ -0,0 +1,140 @@
+---
+id: review
+title: Review
+---
+
+import NavPath from "@site/src/components/NavPath";
+
+**Review** is where you triage what happened on your cameras. It groups activity into **review items**, segments of time on a single camera that bundle together the objects and audio that were active at once, and sorts them into **Alerts**, **Detections**, and **Motion**. From here you can scrub through activity, mark items as reviewed, filter, export, and jump to the full recording in [History](/usage/history).
+
+This page describes how to _use_ the Review view. For how alerts and detections are _configured_ (labels, zones, required zones, retention), see the [Review configuration](/configuration/review) docs.
+
+:::info
+
+Review items are only created for a camera when **object tracking and recording are enabled** for that camera. See [Recording](/configuration/record).
+
+:::
+
+## Alerts, Detections, and Motion
+
+Not every segment of video captured by Frigate is of the same level of interest. The people who enter your property may be a higher priority than those just walking by on the sidewalk. For this reason, Frigate sorts **review items** by importance into **alerts** and **detections**, with a separate **Motion** category for significant motion.
+
+The toggle at the top of the page switches between these three severities. One is always selected.
+
+| Tab | Indicator color | What it shows |
+| -------------- | --------------- | ---------------------------------------------------------------------------------------------------------------- |
+| **Alerts** | dark red | The activity you most want to see. By default, all `person` and `car` tracked objects are alerts. |
+| **Detections** | orange | Everything else Frigate tracked that wasn't promoted to an alert. |
+| **Motion** | yellow | Periods of significant motion, with the ability to filter to periods which did **not** produce a tracked object. |
+
+This same color coding is used for the ring around a selected item and the dots on the calendar. How an object is categorized as an alert vs. a detection, and how required zones refine that, is covered in [Alerts and Detections](/configuration/review#alerts-and-detections).
+
+The **Alerts** and **Detections** tabs show a count next to their label. With **Show Reviewed** turned off (the default), this is the number of items still left to review; with it on, the count reflects every item in the selected time range.
+
+## Marking items as reviewed
+
+Review items are shown as a grid of thumbnail cards next to a vertical activity timeline. Hovering a card (desktop) or swiping to the right (mobile) plays a short preview inline.
+
+- **Clicking** a card opens its recording in [History](/usage/history) and marks the item as reviewed.
+- The object chip on each card is **gray** when the item is unreviewed and turns **green** once it has been reviewed.
+- The **Mark these items as reviewed** button marks everything currently shown as reviewed at once.
+
+Reviewed state is tracked per user, so marking an item reviewed does not hide it for other users. Marking an item reviewed does not delete anything: the footage and the review item itself remain until they expire via retention.
+
+## Selecting and acting on multiple items
+
+To act on several items at once, start a selection by **Ctrl/Cmd-clicking** a card (desktop) or **long-pressing** one (mobile). Selected cards gain a colored ring matching their severity. Keyboard shortcuts speed this up: `Ctrl+A` selects all, `R` marks the selection reviewed, and `Esc` clears it.
+
+With items selected, an action bar appears with options to:
+
+- **Export** the selected items (a single item exports directly; multiple items open the batch [export](/usage/exports) dialog),
+- **Mark as reviewed** or **Mark as unreviewed**, and
+- **Delete** them (admins only).
+
+## Filtering and the calendar
+
+Use the filter controls in the header to narrow what's shown. The available filters depend on the tab: Alerts and Detections can be filtered by **cameras**, **date**, **labels**, **zones**, and whether items are already reviewed; the Motion tab can be filtered by **cameras**, **date**, and **motion only**.
+
+The **calendar** filter lets you jump to a specific day (it shows **Last 24 Hours** until you pick one). On each day:
+
+- An **underline** under the day number means **recordings exist** for that day. Days without recordings are dimmed.
+- A **colored dot** under the day number means there is **unreviewed activity** that day: a **red dot** for unreviewed alerts, or an **orange dot** for unreviewed detections when there are no unreviewed alerts. Motion is not represented by a dot.
+
+Future dates are disabled, and the week start and time zone follow your configuration.
+
+## Reviewing Motion
+
+The Review page also can show periods of motion that didn't produce a tracked object, and provides a way to search past recordings for motion in a specific region. These tools complement the alerts and detections workflow above. See [Tuning Motion Detection](/configuration/motion_detection) for how the underlying motion detector is configured.
+
+The **Motion** tab itself shows a multi-camera grid scrubbed to a shared point in time, with a draggable timeline and a playback-speed selector. A camera tile gains a colored ring when a review item or significant motion overlaps the current time, and clicking a tile opens that camera's recording at that moment. Each camera's options menu (the kebab in the corner of its tile) is where you open **Motion Previews** and **Motion Search**, described below.
+
+### Motion Previews
+
+The Motion Previews pane shows preview clips for periods of significant motion that did not produce a tracked object. It is useful for spotting things that motion detection picked up but object detection did not, which can help validate tuning or catch missed objects.
+
+On the page, click the kebab menu on a camera and choose **Motion Previews**. Each card represents a continuous range of motion-only activity and plays back the recorded preview for that range. A heatmap overlay dims areas of the frame with no motion so the moving regions stand out.
+
+The pane provides a few controls:
+
+- **Speed**: speeds up or slows down all of the preview clips at once.
+- **Dim**: controls how strongly non-motion areas are darkened by the heatmap overlay. Higher values increase motion area visibility.
+- **Filter**: opens a 16×16 grid overlaid on a snapshot of the camera. Select one or more cells to only show clips with motion in those regions. This is helpful for filtering out motion in areas like a busy street while keeping motion in your driveway.
+
+Clicking a preview clip seeks the recording player to that timestamp so you can review the full footage.
+
+### Motion Search
+
+Motion Search lets you scan recorded footage for changes inside a region of interest you draw on the camera. Unlike Motion Previews, which surfaces what Frigate's motion detector flagged in real time, Motion Search re-analyzes the saved recordings, so it can find changes that were missed (for example, an object that appeared while motion detection was paused by `lightning_threshold`, or in a region that is normally motion-masked).
+
+To start a search, open the Actions menu in [History](/usage/history) or click the kebab menu on a camera in the page and choose **Motion Search**. In the dialog:
+
+1. Pick the camera and time range to scan. In the date pickers, days that have recordings available are underlined.
+2. Draw a polygon on the camera frame to define the region of interest.
+3. Adjust the search parameters if needed:
+
+| Field | Description |
+| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Sensitivity Threshold** | Per-pixel luminance change required to count as motion inside the ROI. Behaves like Frigate's motion detection `threshold` setting. |
+| **Minimum Change Area** | Minimum size of a single moving region, as a percentage of the ROI, for a frame to count as significant. Raise it to ignore small movements (leaves, distant motion); lower it when your subject covers only a small slice of the ROI. Every result shows the percentage it scored, so you can use those values to tune this. |
+| **Maximum Results** | Maximum number of matching timestamps to return. The search stops once it reaches this many results, so a lower value finishes sooner while a higher value scans further into the range. |
+| **Parallel mode** | Decode multiple recording ranges at the same time. Speeds up large time ranges at the cost of higher decoding and CPU usage. |
+
+Motion Search samples each recording's keyframes automatically, so there is no frame-rate or sampling setting to tune.
+
+Once running, Frigate scans the recording segments that overlap the time range and reports timestamps where changes were detected inside the polygon, along with the percentage of the ROI that changed. Clicking a result seeks the player to that moment so you can review what happened.
+
+The results panel shows the time range being scanned, a live progress bar with the timestamp currently being analyzed, and the running result count. A collapsible **Search Metrics** section reports how many segments were scanned and processed, how many were skipped because no motion was recorded in the ROI (using the stored motion heatmap), how many frames were decoded, and the total search time. Skipping segments with no recorded motion in the selected ROI is what makes searching long time ranges practical.
+
+#### Common use cases
+
+Frigate's main use case is to record and surface tracked objects, so Motion Search is most useful for the cases where object detection produced nothing: there is no object to find in Explore, but you suspect something happened.
+
+- **Locating an unattributed change.** You know something appeared, disappeared, or moved in a window of footage (a package now gone, a gate left open), but no detection points to it. A search returns the candidate timestamps instead of scrubbing the timeline by hand.
+- **An object that was never detected.** Something Frigate doesn't have a model label for, an object too small or distant to be detected, or movement in a region where detection isn't running. The activity left no tracked object but did change the pixels, so a search can still find it.
+- **Activity while detection was effectively paused.** Changes that occurred while object detection was disabled, motion was suppressed by `skip_motion_threshold`, or inside an area covered by a motion mask, won't appear as review items or tracked objects but can be recovered by searching the recordings directly.
+
+#### Examples
+
+These show how to choose the ROI and **Minimum Change Area** for two common goals. Minimum Change Area is the size of a single moving region as a percentage of the ROI you draw, so the right value depends on how much of the ROI your subject, and its movement between samples, covers.
+
+Because samples are a second or more apart, a moving subject usually appears in two places at once in the comparison, so even ordinary motion often scores tens of percent and a low threshold lets in almost everything. The most reliable approach is to **run a search, look at the percentage each result scored, and set Minimum Change Area just below the values for the events you care about.** The default is 20%; the suggestions below are starting points.
+
+- **When did this item first appear (or disappear)?** A package was dropped off, a car parked, or a trash can was moved, and you want the exact moment. Draw a **tight ROI** around the spot the item occupies and **raise Minimum Change Area** (start around 40–60%). Because the item fills most of a tight ROI, its arrival or removal is a large change, while smaller nearby motion (shadows, a passing pedestrian) stays below the threshold. The **earliest result** is when it appeared; if you only care about that moment, a low Maximum Results finishes faster. If you get no hits, the ROI is probably looser than the item: lower the threshold or tighten the ROI.
+- **What's been getting into the garden?** Something has been trampling a flower bed overnight and no object was ever tracked. Draw a **looser ROI** covering the whole bed and use a **lower Minimum Change Area than the case above**: start near the 20% default and lower it (toward 5–10%) only if a small or distant subject is missed, since it covers just a slice of a large region. Expect more results to scan through: step through the timestamps and jump to each to see what triggered it. If wind-blown plants add noise, raise Minimum Change Area or the Sensitivity Threshold.
+
+#### Expected performance
+
+Motion Search analyzes the saved recordings on demand rather than reading a pre-built index, so a search over a long range takes longer than browsing Motion Previews. Cost scales mainly with how much footage has to be examined: segments with no recorded motion in your ROI are skipped using the stored motion heatmap (shown as "segments skipped" in the status panel), so a quiet range finishes quickly while a busy one takes longer.
+
+To increase the speed of searches:
+
+- Draw a tight ROI. Because **Minimum Change Area** is measured as a percentage of the region you draw, a tight ROI around where you expect the change makes the object fill a larger share of the area, so it clears the threshold more easily. A loose ROI makes the same object a small fraction of the region, so it can fall below the threshold and be missed, forcing you to lower Minimum Change Area, which lets in more noise.
+- Narrow the time range to the window you care about, so there is less footage to examine.
+- Lower **Maximum Results** when you only need the first few hits. Because the search stops once it reaches that many results, a smaller value lets a busy range finish early instead of scanning the whole window.
+- Use Parallel mode to shorten wall-clock time on multi-core systems, at the cost of higher decoding and CPU usage while it runs.
+
+## AI review summaries
+
+When [Generative AI review](/configuration/genai/genai_review) is configured, Frigate can generate a title, description, and threat classification for review items and surface them automatically in Review and History. Clicking the summary chip opens an **AI Analysis** dialog with the generated detail and any flagged concerns.
+
+In Review, an additional icon appears on unreviewed items that the AI classified as **suspicious** (Level 1) or **critical** (Level 2), so the activity that most warrants attention stands out before you open it. The icon goes away once the item has been reviewed.
diff --git a/docs/docusaurus.config.ts b/docs/docusaurus.config.ts
index e11cdd5555..69ab7c3f70 100644
--- a/docs/docusaurus.config.ts
+++ b/docs/docusaurus.config.ts
@@ -186,6 +186,7 @@ const config: Config = {
},
plugins: [
path.resolve(__dirname, "plugins", "raw-loader"),
+ path.resolve(__dirname, "plugins", "yaml-loader"),
[
"docusaurus-plugin-openapi-docs",
{
diff --git a/docs/package-lock.json b/docs/package-lock.json
index be16754be3..2310274651 100644
--- a/docs/package-lock.json
+++ b/docs/package-lock.json
@@ -14,9 +14,11 @@
"@docusaurus/theme-mermaid": "^3.7.0",
"@inkeep/docusaurus": "^2.0.16",
"@mdx-js/react": "^3.1.0",
+ "@types/js-yaml": "^4.0.9",
"clsx": "^2.1.1",
"docusaurus-plugin-openapi-docs": "^4.5.1",
"docusaurus-theme-openapi-docs": "^4.5.1",
+ "js-yaml": "^4.1.1",
"prism-react-renderer": "^2.4.1",
"raw-loader": "^4.0.2",
"react": "^18.3.1",
@@ -5747,6 +5749,11 @@
"@types/istanbul-lib-report": "*"
}
},
+ "node_modules/@types/js-yaml": {
+ "version": "4.0.9",
+ "resolved": "https://mirrors.tencent.com/npm/@types/js-yaml/-/js-yaml-4.0.9.tgz",
+ "integrity": "sha512-k4MGaQl5TGo/iipqb2UDG2UwjXziSWkh0uysQelTlJpX1qGlpUZYm8PnO4DxG1qBomtJUdYJ6qR6xdIah10JLg=="
+ },
"node_modules/@types/json-schema": {
"version": "7.0.15",
"resolved": "https://registry.npmjs.org/@types/json-schema/-/json-schema-7.0.15.tgz",
@@ -10897,9 +10904,9 @@
"license": "MIT"
},
"node_modules/express/node_modules/path-to-regexp": {
- "version": "0.1.12",
- "resolved": "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.12.tgz",
- "integrity": "sha512-RA1GjUVMnvYFxuqovrEqZoxxW5NUZqbwKtYz/Tt7nXerk0LbLblQmrsgdeOxV5SFHf0UDggjS/bSeOZwt1pmEQ==",
+ "version": "0.1.13",
+ "resolved": "https://registry.npmjs.org/path-to-regexp/-/path-to-regexp-0.1.13.tgz",
+ "integrity": "sha512-A/AGNMFN3c8bOlvV9RreMdrv7jsmF9XIfDeCd87+I8RNg6s78BhJxMu69NEMHBSJFxKidViTEdruRwEk/WIKqA==",
"license": "MIT"
},
"node_modules/express/node_modules/range-parser": {
@@ -10964,9 +10971,9 @@
"license": "MIT"
},
"node_modules/fast-uri": {
- "version": "3.1.0",
- "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.0.tgz",
- "integrity": "sha512-iPeeDKJSWf4IEOasVVrknXpaBV0IApz/gp7S2bb7Z4Lljbl2MGJRqInZiUrQwV16cpzw/D3S5j5Julj/gT52AA==",
+ "version": "3.1.2",
+ "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.2.tgz",
+ "integrity": "sha512-rVjf7ArG3LTk+FS6Yw81V1DLuZl1bRbNrev6Tmd/9RaroeeRRJhAt7jg/6YFxbvAQXUCavSoZhPPj6oOx+5KjQ==",
"funding": [
{
"type": "github",
@@ -12313,9 +12320,9 @@
}
},
"node_modules/immutable": {
- "version": "5.1.4",
- "resolved": "https://registry.npmjs.org/immutable/-/immutable-5.1.4.tgz",
- "integrity": "sha512-p6u1bG3YSnINT5RQmx/yRZBpenIl30kVxkTLDyHLIMk0gict704Q9n+thfDI7lTRm9vXdDYutVzXhzcThxTnXA==",
+ "version": "5.1.5",
+ "resolved": "https://registry.npmjs.org/immutable/-/immutable-5.1.5.tgz",
+ "integrity": "sha512-t7xcm2siw+hlUM68I+UEOK+z84RzmN59as9DZ7P1l0994DKUWV7UXBMQZVxaoMSRQ+PBZbHCOoBt7a2wxOMt+A==",
"license": "MIT"
},
"node_modules/import-fresh": {
@@ -12883,7 +12890,7 @@
},
"node_modules/js-yaml": {
"version": "4.1.1",
- "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.1.tgz",
+ "resolved": "https://mirrors.tencent.com/npm/js-yaml/-/js-yaml-4.1.1.tgz",
"integrity": "sha512-qQKT4zQxXl8lLwBtHMWwaTcGfFOZviOJet3Oy/xmGk2gZH677CJM9EvtfdSkgWcATZhj/55JZ0rmy3myCT5lsA==",
"license": "MIT",
"dependencies": {
diff --git a/docs/package.json b/docs/package.json
index 0ff76c4739..1649df7e0c 100644
--- a/docs/package.json
+++ b/docs/package.json
@@ -3,9 +3,10 @@
"version": "0.0.0",
"private": true,
"scripts": {
+ "build:config": "node scripts/build-config.mjs",
"docusaurus": "docusaurus",
- "start": "npm run regen-docs && docusaurus start --host 0.0.0.0",
- "build": "npm run regen-docs && docusaurus build",
+ "start": "npm run build:config && npm run regen-docs && docusaurus start --host 0.0.0.0",
+ "build": "npm run build:config && npm run regen-docs && docusaurus build",
"swizzle": "docusaurus swizzle",
"deploy": "docusaurus deploy",
"clear": "docusaurus clear",
@@ -23,9 +24,12 @@
"@docusaurus/theme-mermaid": "^3.7.0",
"@inkeep/docusaurus": "^2.0.16",
"@mdx-js/react": "^3.1.0",
+ "@types/js-yaml": "^4.0.9",
"clsx": "^2.1.1",
"docusaurus-plugin-openapi-docs": "^4.5.1",
"docusaurus-theme-openapi-docs": "^4.5.1",
+ "js-yaml": "^4.1.1",
+ "marked": "^16.4.2",
"prism-react-renderer": "^2.4.1",
"raw-loader": "^4.0.2",
"react": "^18.3.1",
diff --git a/docs/plugins/js-yaml-loader.js b/docs/plugins/js-yaml-loader.js
new file mode 100644
index 0000000000..66451bd3a9
--- /dev/null
+++ b/docs/plugins/js-yaml-loader.js
@@ -0,0 +1,9 @@
+const yaml = require("js-yaml");
+
+// Webpack loader that compiles a YAML file into a default-exported JS object,
+// so docs data can be authored in YAML (block scalars, no quote/newline
+// escaping) but imported exactly like the JSON it replaces.
+module.exports = function (source) {
+ const data = yaml.load(source);
+ return `export default ${JSON.stringify(data)};`;
+};
diff --git a/docs/plugins/yaml-loader.js b/docs/plugins/yaml-loader.js
new file mode 100644
index 0000000000..475940e6f2
--- /dev/null
+++ b/docs/plugins/yaml-loader.js
@@ -0,0 +1,23 @@
+const path = require("node:path");
+
+// Enables importing YAML data files from docs/data as plain JS objects.
+// Scoped to the data directory so it never intercepts other .yaml files
+// (e.g. the OpenAPI spec under static/).
+module.exports = function (context, options) {
+ return {
+ name: "yaml-data-loader",
+ configureWebpack(config, isServer, utils) {
+ return {
+ module: {
+ rules: [
+ {
+ test: /\.ya?ml$/,
+ include: path.resolve(__dirname, "..", "data"),
+ use: path.resolve(__dirname, "js-yaml-loader.js"),
+ },
+ ],
+ },
+ };
+ },
+ };
+};
diff --git a/docs/scripts/README.md b/docs/scripts/README.md
new file mode 100644
index 0000000000..347536a07b
--- /dev/null
+++ b/docs/scripts/README.md
@@ -0,0 +1,184 @@
+# Documentation Scripts
+
+## generate_ui_tabs.py
+
+Automatically generates "Frigate UI" tab content for documentation files based on the YAML config examples already in the docs.
+
+Instead of manually writing UI instructions for every YAML block, this script reads three data sources from the codebase and generates the UI tabs:
+
+1. **JSON Schema** (from Pydantic config models) -- field names, types, defaults
+2. **i18n translation files** -- the exact labels shown in the Settings UI
+3. **Section mappings** (from Settings.tsx) -- config key to UI navigation path
+
+### Prerequisites
+
+Run from the repository root. The script imports Frigate's Python config models directly, so the `frigate` package must be importable:
+
+```bash
+# From repo root -- no extra install needed if your environment can import frigate
+python3 docs/scripts/generate_ui_tabs.py --help
+```
+
+### Usage
+
+#### Preview (default)
+
+Shows what would be generated for each bare YAML block, without modifying any files:
+
+```bash
+# Single file
+python3 docs/scripts/generate_ui_tabs.py docs/docs/configuration/record.md
+
+# All config docs
+python3 docs/scripts/generate_ui_tabs.py docs/docs/configuration/
+```
+
+#### Inject
+
+Wraps bare YAML blocks with `` and inserts the generated UI tab. Also adds the required imports (`ConfigTabs`, `TabItem`, `NavPath`) after the frontmatter if missing.
+
+Already-wrapped blocks are skipped (idempotent).
+
+```bash
+python3 docs/scripts/generate_ui_tabs.py --inject docs/docs/configuration/record.md
+```
+
+#### Check
+
+Compares existing UI tabs against what the script would generate from the current schema and i18n files. Prints a unified diff for each drifted block and exits with code 1 if any drift is found.
+
+Use this in CI to catch stale docs after schema or i18n changes.
+
+```bash
+python3 docs/scripts/generate_ui_tabs.py --check docs/docs/configuration/
+```
+
+#### Regenerate
+
+Replaces the UI tab content in existing `` blocks with freshly generated content. The YAML tab is preserved exactly as-is. Only blocks that have actually changed are rewritten.
+
+```bash
+# Preview changes without writing
+python3 docs/scripts/generate_ui_tabs.py --regenerate --dry-run docs/docs/configuration/
+
+# Apply changes
+python3 docs/scripts/generate_ui_tabs.py --regenerate docs/docs/configuration/
+```
+
+#### Output to directory (`--outdir`)
+
+Write generated files to a separate directory instead of modifying the originals. The source directory structure is mirrored. Files without changes are copied as-is so the output is a complete snapshot suitable for diffing.
+
+Works with `--inject` and `--regenerate`.
+
+```bash
+# Generate into a named directory
+python3 docs/scripts/generate_ui_tabs.py --inject --outdir /tmp/generated docs/docs/configuration/
+
+# Then diff original vs generated
+diff -rq docs/docs/configuration/ /tmp/generated/
+
+# Or let an AI agent compare them
+diff -ru docs/docs/configuration/record.md /tmp/generated/record.md
+```
+
+This is useful for AI agents that need to review the generated output before applying it, or for previewing what `--inject` or `--regenerate` would do across an entire directory.
+
+#### Verbose mode
+
+Add `-v` to any mode for detailed diagnostics (skipped blocks, reasons, unchanged blocks):
+
+```bash
+python3 docs/scripts/generate_ui_tabs.py -v docs/docs/configuration/
+```
+
+### Typical workflow
+
+```bash
+# 1. Preview what would be generated (output to temp dir, originals untouched)
+python3 docs/scripts/generate_ui_tabs.py --inject --outdir /tmp/ui-preview docs/docs/configuration/
+# Compare: diff -ru docs/docs/configuration/ /tmp/ui-preview/
+
+# 2. Apply: inject UI tabs into the actual docs
+python3 docs/scripts/generate_ui_tabs.py --inject docs/docs/configuration/
+
+# 3. Review and hand-edit where needed (the script gets you 90% there)
+
+# 4. Later, after schema or i18n changes, check for drift
+python3 docs/scripts/generate_ui_tabs.py --check docs/docs/configuration/
+
+# 5. If drifted, preview then regenerate
+python3 docs/scripts/generate_ui_tabs.py --regenerate --outdir /tmp/ui-regen docs/docs/configuration/
+# Compare: diff -ru docs/docs/configuration/ /tmp/ui-regen/
+
+# 6. Apply regeneration
+python3 docs/scripts/generate_ui_tabs.py --regenerate docs/docs/configuration/
+```
+
+### How it decides what to generate
+
+The script detects two patterns from the YAML block content:
+
+**Pattern A -- Field table.** When the YAML has inline comments (e.g., `# <- description`), the script generates a markdown table with field names and descriptions:
+
+```markdown
+Navigate to .
+
+| Field | Description |
+|-------|-------------|
+| **Continuous retention > Retention days** | Days to retain recordings. |
+| **Motion retention > Retention days** | Days to retain recordings. |
+```
+
+**Pattern B -- Set instructions.** When the YAML has concrete values without comments, the script generates step-by-step instructions:
+
+```markdown
+Navigate to .
+
+- Set **Enable recording** to on
+- Set **Continuous retention > Retention days** to `3`
+- Set **Alert retention > Event retention > Retention days** to `30`
+- Set **Alert retention > Event retention > Retention mode** to `all`
+```
+
+**Camera-level config** is auto-detected when the YAML is nested under `cameras:`. The output uses a generic camera reference rather than the example camera name from the YAML:
+
+```markdown
+1. Navigate to and select your camera.
+ - Set **Enable recording** to on
+ - Set **Continuous retention > Retention days** to `5`
+```
+
+### What gets skipped
+
+- YAML blocks already inside `` (for `--inject`)
+- YAML blocks whose top-level key is not a known config section (e.g., `go2rtc`, `docker-compose`, `scrape_configs`)
+- Fields listed in `hiddenFields` in the section configs (e.g., `enabled_in_config`)
+
+### File structure
+
+```
+docs/scripts/
+├── generate_ui_tabs.py # CLI entry point
+├── README.md # This file
+└── lib/
+ ├── __init__.py
+ ├── schema_loader.py # Loads JSON schema from Pydantic models
+ ├── i18n_loader.py # Loads i18n translation JSON files
+ ├── section_config_parser.py # Parses TS section configs (hiddenFields, etc.)
+ ├── yaml_extractor.py # Extracts YAML blocks and ConfigTabs from markdown
+ ├── ui_generator.py # Generates UI tab markdown content
+ └── nav_map.py # Maps config sections to Settings UI nav paths
+```
+
+### Data sources
+
+| Source | Path | What it provides |
+|--------|------|------------------|
+| Pydantic models | `frigate/config/` | Field names, types, defaults, nesting |
+| JSON schema | Generated from Pydantic at runtime | Full schema with `$defs` and `$ref` |
+| i18n (global) | `web/public/locales/en/config/global.json` | Field labels for global settings |
+| i18n (cameras) | `web/public/locales/en/config/cameras.json` | Field labels for camera settings |
+| i18n (menu) | `web/public/locales/en/views/settings.json` | Sidebar menu labels |
+| Section configs | `web/src/components/config-form/section-configs/*.ts` | Hidden fields, advanced fields, field order |
+| Navigation map | Hardcoded from `web/src/pages/Settings.tsx` | Config section to UI path mapping |
diff --git a/docs/scripts/build-config.mjs b/docs/scripts/build-config.mjs
new file mode 100644
index 0000000000..78926bed5b
--- /dev/null
+++ b/docs/scripts/build-config.mjs
@@ -0,0 +1,64 @@
+#!/usr/bin/env node
+
+/**
+ * Build script: reads config.yaml and generates TypeScript files
+ * for the Docker Compose Generator.
+ *
+ * Usage: node scripts/build-config.mjs
+ */
+
+import fs from "node:fs";
+import path from "node:path";
+import { fileURLToPath } from "node:url";
+import yaml from "js-yaml";
+
+const __dirname = path.dirname(fileURLToPath(import.meta.url));
+const CONFIG_DIR = path.resolve(__dirname, "../src/components/DockerComposeGenerator/config");
+const YAML_PATH = path.join(CONFIG_DIR, "config.yaml");
+
+// Read & parse YAML
+const raw = fs.readFileSync(YAML_PATH, "utf8");
+const config = yaml.load(raw);
+
+if (!config.devices || !config.hardware || !config.ports) {
+ console.error("config.yaml must contain 'devices', 'hardware', and 'ports' sections.");
+ process.exit(1);
+}
+
+/**
+ * Generate a .ts file from a section of the YAML config.
+ */
+function generateTsFile(sectionName, items, typeName, varName, mapVarName, yamlFilename) {
+ const jsonItems = JSON.stringify(items, null, 2);
+ // Indent JSON to fit inside the array literal
+ const indented = jsonItems
+ .split("\n")
+ .map((line, i) => (i === 0 ? line : " " + line))
+ .join("\n");
+
+ const content = `/**
+ * AUTO-GENERATED FILE — do not edit directly.
+ * Source: ${yamlFilename}
+ * To update, edit the YAML file and run: npm run build:config
+ */
+
+import type { ${typeName} } from "./types";
+
+export const ${varName}: ${typeName}[] = ${indented};
+
+/** Lookup map for quick access by ID */
+export const ${mapVarName}: Map = new Map(${varName}.map((item) => [item.id, item]));
+`;
+
+ const outPath = path.join(CONFIG_DIR, `${sectionName}.ts`);
+ fs.writeFileSync(outPath, content, "utf8");
+ console.log(` ✓ Generated ${sectionName}.ts (${items.length} items)`);
+}
+
+console.log("Building config from config.yaml...");
+
+generateTsFile("devices", config.devices, "DeviceConfig", "devices", "deviceMap", "config.yaml");
+generateTsFile("hardware", config.hardware, "HardwareOption", "hardwareOptions", "hardwareMap", "config.yaml");
+generateTsFile("ports", config.ports, "PortConfig", "ports", "portMap", "config.yaml");
+
+console.log("Done!");
diff --git a/docs/scripts/generate_ui_tabs.py b/docs/scripts/generate_ui_tabs.py
new file mode 100644
index 0000000000..fa468922c3
--- /dev/null
+++ b/docs/scripts/generate_ui_tabs.py
@@ -0,0 +1,660 @@
+#!/usr/bin/env python3
+"""Generate Frigate UI tab content for documentation files.
+
+This script reads YAML code blocks from documentation markdown files and
+generates corresponding "Frigate UI" tab instructions based on:
+- JSON Schema (from Pydantic config models)
+- i18n translation files (for UI field labels)
+- Section configs (for hidden/advanced field info)
+- Navigation mappings (for Settings UI paths)
+
+Usage:
+ # Preview generated UI tabs for a single file
+ python docs/scripts/generate_ui_tabs.py docs/docs/configuration/record.md
+
+ # Preview all config docs
+ python docs/scripts/generate_ui_tabs.py docs/docs/configuration/
+
+ # Inject UI tabs into files (wraps bare YAML blocks with ConfigTabs)
+ python docs/scripts/generate_ui_tabs.py --inject docs/docs/configuration/record.md
+
+ # Regenerate existing UI tabs from current schema/i18n
+ python docs/scripts/generate_ui_tabs.py --regenerate docs/docs/configuration/
+
+ # Check for drift between existing UI tabs and what would be generated
+ python docs/scripts/generate_ui_tabs.py --check docs/docs/configuration/
+
+ # Write generated files to a temp directory for comparison (originals unchanged)
+ python docs/scripts/generate_ui_tabs.py --inject --outdir /tmp/generated docs/docs/configuration/
+
+ # Show detailed warnings and diagnostics
+ python docs/scripts/generate_ui_tabs.py --verbose docs/docs/configuration/
+"""
+
+import argparse
+import difflib
+import shutil
+import sys
+import tempfile
+from pathlib import Path
+
+# Ensure frigate package is importable
+sys.path.insert(0, str(Path(__file__).resolve().parents[1].parent))
+
+from lib.i18n_loader import load_i18n
+from lib.nav_map import ALL_CONFIG_SECTIONS
+from lib.schema_loader import load_schema
+from lib.section_config_parser import load_section_configs
+from lib.ui_generator import generate_ui_content, wrap_with_config_tabs
+from lib.yaml_extractor import (
+ extract_config_tabs_blocks,
+ extract_yaml_blocks,
+)
+
+
+def process_file(
+ filepath: Path,
+ schema: dict,
+ i18n: dict,
+ section_configs: dict,
+ inject: bool = False,
+ verbose: bool = False,
+ outpath: Path | None = None,
+) -> dict:
+ """Process a single markdown file for initial injection of bare YAML blocks.
+
+ Args:
+ outpath: If set, write the result here instead of modifying filepath.
+
+ Returns:
+ Stats dict with counts of blocks found, generated, skipped, etc.
+ """
+ content = filepath.read_text()
+ blocks = extract_yaml_blocks(content)
+
+ stats = {
+ "file": str(filepath),
+ "total_blocks": len(blocks),
+ "config_blocks": 0,
+ "already_wrapped": 0,
+ "generated": 0,
+ "skipped": 0,
+ "warnings": [],
+ }
+
+ if not blocks:
+ return stats
+
+ # For injection, we need to track replacements
+ replacements: list[tuple[int, int, str]] = []
+
+ for block in blocks:
+ # Skip non-config YAML blocks
+ if block.section_key is None or (
+ block.section_key not in ALL_CONFIG_SECTIONS
+ and not block.is_camera_level
+ ):
+ stats["skipped"] += 1
+ if verbose and block.config_keys:
+ stats["warnings"].append(
+ f" Line {block.line_start}: Skipped block with keys "
+ f"{block.config_keys} (not a known config section)"
+ )
+ continue
+
+ stats["config_blocks"] += 1
+
+ # Skip already-wrapped blocks
+ if block.inside_config_tabs:
+ stats["already_wrapped"] += 1
+ if verbose:
+ stats["warnings"].append(
+ f" Line {block.line_start}: Already inside ConfigTabs, skipping"
+ )
+ continue
+
+ # Generate UI content
+ ui_content = generate_ui_content(
+ block, schema, i18n, section_configs
+ )
+
+ if ui_content is None:
+ stats["skipped"] += 1
+ if verbose:
+ stats["warnings"].append(
+ f" Line {block.line_start}: Could not generate UI content "
+ f"for section '{block.section_key}'"
+ )
+ continue
+
+ stats["generated"] += 1
+
+ if inject:
+ full_block = wrap_with_config_tabs(
+ ui_content, block.raw, block.highlight
+ )
+ replacements.append((block.line_start, block.line_end, full_block))
+ else:
+ # Preview mode: print to stdout
+ print(f"\n{'='*60}")
+ print(f"File: {filepath}")
+ print(f"Line {block.line_start}: section={block.section_key}, "
+ f"camera={block.is_camera_level}")
+ print(f"{'='*60}")
+ print()
+ print("--- Generated UI tab ---")
+ print(ui_content)
+ print()
+ print("--- Would produce ---")
+ print(wrap_with_config_tabs(ui_content, block.raw, block.highlight))
+ print()
+
+ # Apply injections in reverse order (to preserve line numbers)
+ if inject and replacements:
+ lines = content.split("\n")
+ for start, end, replacement in reversed(replacements):
+ # start/end are 1-based line numbers
+ # The YAML block spans from the ``` line before start to the ``` line at end
+ # We need to replace from the opening ``` to the closing ```
+ block_start = start - 2 # 0-based index of ```yaml line
+ block_end = end - 1 # 0-based index of closing ``` line
+
+ replacement_lines = replacement.split("\n")
+ lines[block_start : block_end + 1] = replacement_lines
+
+ new_content = "\n".join(lines)
+
+ # Ensure imports are present
+ new_content = _ensure_imports(new_content)
+
+ target = outpath or filepath
+ target.parent.mkdir(parents=True, exist_ok=True)
+ target.write_text(new_content)
+ print(f" Injected {len(replacements)} ConfigTabs block(s) into {target}")
+ elif outpath is not None:
+ # No changes but outdir requested -- copy original so the output
+ # directory contains a complete set of files for diffing.
+ outpath.parent.mkdir(parents=True, exist_ok=True)
+ shutil.copy2(filepath, outpath)
+
+ return stats
+
+
+def regenerate_file(
+ filepath: Path,
+ schema: dict,
+ i18n: dict,
+ section_configs: dict,
+ dry_run: bool = False,
+ verbose: bool = False,
+ outpath: Path | None = None,
+) -> dict:
+ """Regenerate UI tabs in existing ConfigTabs blocks.
+
+ Strips the current UI tab content and regenerates it from the YAML tab
+ using the current schema and i18n data.
+
+ Args:
+ outpath: If set, write the result here instead of modifying filepath.
+
+ Returns:
+ Stats dict
+ """
+ content = filepath.read_text()
+ tab_blocks = extract_config_tabs_blocks(content)
+
+ stats = {
+ "file": str(filepath),
+ "total_blocks": len(tab_blocks),
+ "regenerated": 0,
+ "unchanged": 0,
+ "skipped": 0,
+ "warnings": [],
+ }
+
+ if not tab_blocks:
+ return stats
+
+ replacements: list[tuple[int, int, str]] = []
+
+ for tab_block in tab_blocks:
+ yaml_block = tab_block.yaml_block
+
+ # Skip non-config blocks
+ if yaml_block.section_key is None or (
+ yaml_block.section_key not in ALL_CONFIG_SECTIONS
+ and not yaml_block.is_camera_level
+ ):
+ stats["skipped"] += 1
+ if verbose:
+ stats["warnings"].append(
+ f" Line {tab_block.line_start}: Skipped (not a config section)"
+ )
+ continue
+
+ # Generate fresh UI content
+ new_ui = generate_ui_content(
+ yaml_block, schema, i18n, section_configs
+ )
+
+ if new_ui is None:
+ stats["skipped"] += 1
+ if verbose:
+ stats["warnings"].append(
+ f" Line {tab_block.line_start}: Could not regenerate "
+ f"for section '{yaml_block.section_key}'"
+ )
+ continue
+
+ # Compare with existing
+ existing_ui = tab_block.ui_content
+ if _normalize_whitespace(new_ui) == _normalize_whitespace(existing_ui):
+ stats["unchanged"] += 1
+ if verbose:
+ stats["warnings"].append(
+ f" Line {tab_block.line_start}: Unchanged"
+ )
+ continue
+
+ stats["regenerated"] += 1
+
+ new_full = wrap_with_config_tabs(
+ new_ui, yaml_block.raw, yaml_block.highlight
+ )
+ replacements.append(
+ (tab_block.line_start, tab_block.line_end, new_full)
+ )
+
+ if dry_run or verbose:
+ print(f"\n{'='*60}")
+ print(f"File: {filepath}, line {tab_block.line_start}")
+ print(f"Section: {yaml_block.section_key}")
+ print(f"{'='*60}")
+ _print_diff(existing_ui, new_ui, filepath, tab_block.line_start)
+
+ # Apply replacements
+ if not dry_run and replacements:
+ lines = content.split("\n")
+ for start, end, replacement in reversed(replacements):
+ block_start = start - 1 # 0-based index of line
+ block_end = end - 1 # 0-based index of line
+ replacement_lines = replacement.split("\n")
+ lines[block_start : block_end + 1] = replacement_lines
+
+ new_content = "\n".join(lines)
+ target = outpath or filepath
+ target.parent.mkdir(parents=True, exist_ok=True)
+ target.write_text(new_content)
+ print(
+ f" Regenerated {len(replacements)} ConfigTabs block(s) in {target}",
+ file=sys.stderr,
+ )
+ elif outpath is not None:
+ outpath.parent.mkdir(parents=True, exist_ok=True)
+ shutil.copy2(filepath, outpath)
+
+ return stats
+
+
+def check_file(
+ filepath: Path,
+ schema: dict,
+ i18n: dict,
+ section_configs: dict,
+ verbose: bool = False,
+) -> dict:
+ """Check for drift between existing UI tabs and what would be generated.
+
+ Returns:
+ Stats dict with drift info. Non-zero "drifted" means the file is stale.
+ """
+ content = filepath.read_text()
+ tab_blocks = extract_config_tabs_blocks(content)
+
+ stats = {
+ "file": str(filepath),
+ "total_blocks": len(tab_blocks),
+ "up_to_date": 0,
+ "drifted": 0,
+ "skipped": 0,
+ "warnings": [],
+ }
+
+ if not tab_blocks:
+ return stats
+
+ for tab_block in tab_blocks:
+ yaml_block = tab_block.yaml_block
+
+ if yaml_block.section_key is None or (
+ yaml_block.section_key not in ALL_CONFIG_SECTIONS
+ and not yaml_block.is_camera_level
+ ):
+ stats["skipped"] += 1
+ continue
+
+ new_ui = generate_ui_content(
+ yaml_block, schema, i18n, section_configs
+ )
+
+ if new_ui is None:
+ stats["skipped"] += 1
+ continue
+
+ existing_ui = tab_block.ui_content
+ if _normalize_whitespace(new_ui) == _normalize_whitespace(existing_ui):
+ stats["up_to_date"] += 1
+ else:
+ stats["drifted"] += 1
+ print(f"\n{'='*60}")
+ print(f"DRIFT: {filepath}, line {tab_block.line_start}")
+ print(f"Section: {yaml_block.section_key}")
+ print(f"{'='*60}")
+ _print_diff(existing_ui, new_ui, filepath, tab_block.line_start)
+
+ return stats
+
+
+def _normalize_whitespace(text: str) -> str:
+ """Normalize whitespace for comparison (strip lines, collapse blanks)."""
+ lines = [line.rstrip() for line in text.strip().splitlines()]
+ # Collapse multiple blank lines into one
+ result: list[str] = []
+ prev_blank = False
+ for line in lines:
+ if line == "":
+ if not prev_blank:
+ result.append(line)
+ prev_blank = True
+ else:
+ result.append(line)
+ prev_blank = False
+ return "\n".join(result)
+
+
+def _print_diff(existing: str, generated: str, filepath: Path, line: int):
+ """Print a unified diff between existing and generated UI content."""
+ existing_lines = existing.strip().splitlines(keepends=True)
+ generated_lines = generated.strip().splitlines(keepends=True)
+
+ diff = difflib.unified_diff(
+ existing_lines,
+ generated_lines,
+ fromfile=f"{filepath}:{line} (existing)",
+ tofile=f"{filepath}:{line} (generated)",
+ lineterm="",
+ )
+ diff_text = "\n".join(diff)
+ if diff_text:
+ print(diff_text)
+ else:
+ print(" (whitespace-only difference)")
+
+
+def _ensure_imports(content: str) -> str:
+ """Ensure ConfigTabs/TabItem/NavPath imports are present in the file."""
+ lines = content.split("\n")
+
+ needed_imports = []
+ if "" in content and 'import ConfigTabs' not in content:
+ needed_imports.append(
+ 'import ConfigTabs from "@site/src/components/ConfigTabs";'
+ )
+ if "outpath mapping
+ file_outpaths: dict[Path, Path | None] = {}
+ for f in files:
+ if outdir is not None:
+ try:
+ rel = f.resolve().relative_to(base_dir)
+ except ValueError:
+ rel = Path(f.name)
+ file_outpaths[f] = outdir / rel
+ else:
+ file_outpaths[f] = None
+
+ # Load data sources
+ print("Loading schema from Pydantic models...", file=sys.stderr)
+ schema = load_schema()
+ print("Loading i18n translations...", file=sys.stderr)
+ i18n = load_i18n()
+ print("Loading section configs...", file=sys.stderr)
+ section_configs = load_section_configs()
+ print(f"Processing {len(files)} file(s)...\n", file=sys.stderr)
+
+ if args.check:
+ _run_check(files, schema, i18n, section_configs, args.verbose)
+ elif args.regenerate:
+ _run_regenerate(
+ files, schema, i18n, section_configs,
+ args.dry_run, args.verbose, file_outpaths,
+ )
+ else:
+ _run_inject(
+ files, schema, i18n, section_configs,
+ args.inject, args.verbose, file_outpaths,
+ )
+
+ if outdir is not None:
+ print(f"\nOutput written to: {outdir}", file=sys.stderr)
+
+
+def _run_inject(files, schema, i18n, section_configs, inject, verbose, file_outpaths):
+ """Run default mode: preview or inject bare YAML blocks."""
+ total_stats = {
+ "files": 0,
+ "total_blocks": 0,
+ "config_blocks": 0,
+ "already_wrapped": 0,
+ "generated": 0,
+ "skipped": 0,
+ }
+
+ for filepath in files:
+ stats = process_file(
+ filepath, schema, i18n, section_configs,
+ inject=inject, verbose=verbose,
+ outpath=file_outpaths.get(filepath),
+ )
+
+ total_stats["files"] += 1
+ for key in ["total_blocks", "config_blocks", "already_wrapped",
+ "generated", "skipped"]:
+ total_stats[key] += stats[key]
+
+ if verbose and stats["warnings"]:
+ print(f"\n{filepath}:", file=sys.stderr)
+ for w in stats["warnings"]:
+ print(w, file=sys.stderr)
+
+ print("\n" + "=" * 60, file=sys.stderr)
+ print("Summary:", file=sys.stderr)
+ print(f" Files processed: {total_stats['files']}", file=sys.stderr)
+ print(f" Total YAML blocks: {total_stats['total_blocks']}", file=sys.stderr)
+ print(f" Config blocks: {total_stats['config_blocks']}", file=sys.stderr)
+ print(f" Already wrapped: {total_stats['already_wrapped']}", file=sys.stderr)
+ print(f" Generated: {total_stats['generated']}", file=sys.stderr)
+ print(f" Skipped: {total_stats['skipped']}", file=sys.stderr)
+ print("=" * 60, file=sys.stderr)
+
+
+def _run_regenerate(files, schema, i18n, section_configs, dry_run, verbose, file_outpaths):
+ """Run regenerate mode: update existing ConfigTabs blocks."""
+ total_stats = {
+ "files": 0,
+ "total_blocks": 0,
+ "regenerated": 0,
+ "unchanged": 0,
+ "skipped": 0,
+ }
+
+ for filepath in files:
+ stats = regenerate_file(
+ filepath, schema, i18n, section_configs,
+ dry_run=dry_run, verbose=verbose,
+ outpath=file_outpaths.get(filepath),
+ )
+
+ total_stats["files"] += 1
+ for key in ["total_blocks", "regenerated", "unchanged", "skipped"]:
+ total_stats[key] += stats[key]
+
+ if verbose and stats["warnings"]:
+ print(f"\n{filepath}:", file=sys.stderr)
+ for w in stats["warnings"]:
+ print(w, file=sys.stderr)
+
+ action = "Would regenerate" if dry_run else "Regenerated"
+ print("\n" + "=" * 60, file=sys.stderr)
+ print("Summary:", file=sys.stderr)
+ print(f" Files processed: {total_stats['files']}", file=sys.stderr)
+ print(f" ConfigTabs blocks: {total_stats['total_blocks']}", file=sys.stderr)
+ print(f" {action}: {total_stats['regenerated']}", file=sys.stderr)
+ print(f" Unchanged: {total_stats['unchanged']}", file=sys.stderr)
+ print(f" Skipped: {total_stats['skipped']}", file=sys.stderr)
+ print("=" * 60, file=sys.stderr)
+
+
+def _run_check(files, schema, i18n, section_configs, verbose):
+ """Run check mode: detect drift without modifying files."""
+ total_stats = {
+ "files": 0,
+ "total_blocks": 0,
+ "up_to_date": 0,
+ "drifted": 0,
+ "skipped": 0,
+ }
+
+ for filepath in files:
+ stats = check_file(
+ filepath, schema, i18n, section_configs, verbose=verbose,
+ )
+
+ total_stats["files"] += 1
+ for key in ["total_blocks", "up_to_date", "drifted", "skipped"]:
+ total_stats[key] += stats[key]
+
+ print("\n" + "=" * 60, file=sys.stderr)
+ print("Summary:", file=sys.stderr)
+ print(f" Files processed: {total_stats['files']}", file=sys.stderr)
+ print(f" ConfigTabs blocks: {total_stats['total_blocks']}", file=sys.stderr)
+ print(f" Up to date: {total_stats['up_to_date']}", file=sys.stderr)
+ print(f" Drifted: {total_stats['drifted']}", file=sys.stderr)
+ print(f" Skipped: {total_stats['skipped']}", file=sys.stderr)
+ print("=" * 60, file=sys.stderr)
+
+ if total_stats["drifted"] > 0:
+ print(
+ f"\n{total_stats['drifted']} block(s) have drifted from schema/i18n. "
+ "Run with --regenerate to update.",
+ file=sys.stderr,
+ )
+ sys.exit(1)
+ else:
+ print("\nAll UI tabs are up to date.", file=sys.stderr)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/docs/scripts/lib/__init__.py b/docs/scripts/lib/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/docs/scripts/lib/i18n_loader.py b/docs/scripts/lib/i18n_loader.py
new file mode 100644
index 0000000000..7416e86c7c
--- /dev/null
+++ b/docs/scripts/lib/i18n_loader.py
@@ -0,0 +1,139 @@
+"""Load i18n translation files for Settings UI field labels."""
+
+import json
+from pathlib import Path
+from typing import Any
+
+# Base path for locale files
+WEB_LOCALES = Path(__file__).resolve().parents[3] / "web" / "public" / "locales" / "en"
+
+
+def load_i18n() -> dict[str, Any]:
+ """Load and merge all relevant i18n files.
+
+ Returns:
+ Dict with keys: "global", "cameras", "settings_menu"
+ """
+ global_path = WEB_LOCALES / "config" / "global.json"
+ cameras_path = WEB_LOCALES / "config" / "cameras.json"
+ settings_path = WEB_LOCALES / "views" / "settings.json"
+
+ result: dict[str, Any] = {}
+
+ with open(global_path) as f:
+ result["global"] = json.load(f)
+
+ with open(cameras_path) as f:
+ result["cameras"] = json.load(f)
+
+ with open(settings_path) as f:
+ settings = json.load(f)
+ result["settings_menu"] = settings.get("menu", {})
+
+ # Build a unified enum value → label lookup from all known sources.
+ # Merges multiple maps so callers don't need to know which file
+ # a particular enum lives in.
+ value_labels: dict[str, str] = {}
+
+ config_form = settings.get("configForm", {})
+
+ # FFmpeg preset labels (preset-vaapi → "VAAPI (Intel/AMD GPU)")
+ value_labels.update(
+ config_form.get("ffmpegArgs", {}).get("presetLabels", {})
+ )
+
+ # Timestamp position (tl → "Top left")
+ value_labels.update(settings.get("timestampPosition", {}))
+
+ # Input role options (detect → "Detect")
+ value_labels.update(
+ config_form.get("inputRoles", {}).get("options", {})
+ )
+
+ # GenAI role options (vision → "Vision")
+ value_labels.update(
+ config_form.get("genaiRoles", {}).get("options", {})
+ )
+
+ result["value_labels"] = value_labels
+
+ return result
+
+
+def get_field_label(
+ i18n: dict[str, Any],
+ section_key: str,
+ field_path: list[str],
+ level: str = "global",
+) -> str | None:
+ """Look up the UI label for a field.
+
+ Args:
+ i18n: Loaded i18n data from load_i18n()
+ section_key: Config section (e.g., "record")
+ field_path: Path within section (e.g., ["continuous", "days"])
+ level: "global" or "cameras"
+
+ Returns:
+ The label string, or None if not found.
+ """
+ source = i18n.get(level, {})
+ node = source.get(section_key, {})
+
+ for key in field_path:
+ if not isinstance(node, dict):
+ return None
+ node = node.get(key, {})
+
+ if isinstance(node, dict):
+ return node.get("label")
+ return None
+
+
+def get_field_description(
+ i18n: dict[str, Any],
+ section_key: str,
+ field_path: list[str],
+ level: str = "global",
+) -> str | None:
+ """Look up the UI description for a field."""
+ source = i18n.get(level, {})
+ node = source.get(section_key, {})
+
+ for key in field_path:
+ if not isinstance(node, dict):
+ return None
+ node = node.get(key, {})
+
+ if isinstance(node, dict):
+ return node.get("description")
+ return None
+
+
+def get_value_label(
+ i18n: dict[str, Any],
+ value: str,
+) -> str | None:
+ """Look up the display label for an enum/option value.
+
+ Args:
+ i18n: Loaded i18n data from load_i18n()
+ value: The raw config value (e.g., "preset-vaapi", "tl")
+
+ Returns:
+ The human-readable label (e.g., "VAAPI (Intel/AMD GPU)"), or None.
+ """
+ return i18n.get("value_labels", {}).get(value)
+
+
+def get_section_label(
+ i18n: dict[str, Any],
+ section_key: str,
+ level: str = "global",
+) -> str | None:
+ """Get the top-level label for a config section."""
+ source = i18n.get(level, {})
+ section = source.get(section_key, {})
+ if isinstance(section, dict):
+ return section.get("label")
+ return None
diff --git a/docs/scripts/lib/nav_map.py b/docs/scripts/lib/nav_map.py
new file mode 100644
index 0000000000..0fddf40e00
--- /dev/null
+++ b/docs/scripts/lib/nav_map.py
@@ -0,0 +1,120 @@
+"""Map config section keys to Settings UI navigation paths."""
+
+# Derived from web/src/pages/Settings.tsx section mappings
+# and web/public/locales/en/views/settings.json menu labels.
+#
+# Format: section_key -> (group_label, page_label)
+# Navigation path: "Settings > {group_label} > {page_label}"
+
+GLOBAL_NAV: dict[str, tuple[str, str]] = {
+ "detect": ("Global configuration", "Object detection"),
+ "ffmpeg": ("Global configuration", "FFmpeg"),
+ "record": ("Global configuration", "Recording"),
+ "snapshots": ("Global configuration", "Snapshots"),
+ "motion": ("Global configuration", "Motion detection"),
+ "objects": ("Global configuration", "Objects"),
+ "review": ("Global configuration", "Review"),
+ "audio": ("Global configuration", "Audio events"),
+ "live": ("Global configuration", "Live playback"),
+ "timestamp_style": ("Global configuration", "Timestamp style"),
+ "notifications": ("Notifications", "Notifications"),
+}
+
+CAMERA_NAV: dict[str, tuple[str, str]] = {
+ "detect": ("Camera configuration", "Object detection"),
+ "ffmpeg": ("Camera configuration", "FFmpeg"),
+ "record": ("Camera configuration", "Recording"),
+ "snapshots": ("Camera configuration", "Snapshots"),
+ "motion": ("Camera configuration", "Motion detection"),
+ "objects": ("Camera configuration", "Objects"),
+ "review": ("Camera configuration", "Review"),
+ "audio": ("Camera configuration", "Audio events"),
+ "audio_transcription": ("Camera configuration", "Audio transcription"),
+ "notifications": ("Camera configuration", "Notifications"),
+ "live": ("Camera configuration", "Live playback"),
+ "birdseye": ("Camera configuration", "Birdseye"),
+ "face_recognition": ("Camera configuration", "Face recognition"),
+ "lpr": ("Camera configuration", "License plate recognition"),
+ "mqtt": ("Camera configuration", "MQTT"),
+ "onvif": ("Camera configuration", "ONVIF"),
+ "ui": ("Camera configuration", "Camera UI"),
+ "timestamp_style": ("Camera configuration", "Timestamp style"),
+}
+
+ENRICHMENT_NAV: dict[str, tuple[str, str]] = {
+ "semantic_search": ("Enrichments", "Semantic search"),
+ "genai": ("Enrichments", "Generative AI"),
+ "face_recognition": ("Enrichments", "Face recognition"),
+ "lpr": ("Enrichments", "License plate recognition"),
+ "classification": ("Enrichments", "Object classification"),
+ "audio_transcription": ("Enrichments", "Audio transcription"),
+}
+
+SYSTEM_NAV: dict[str, tuple[str, str]] = {
+ "go2rtc_streams": ("System", "go2rtc streams"),
+ "database": ("System", "Database"),
+ "mqtt": ("System", "MQTT"),
+ "tls": ("System", "TLS"),
+ "auth": ("System", "Authentication"),
+ "networking": ("System", "Networking"),
+ "proxy": ("System", "Proxy"),
+ "ui": ("System", "UI"),
+ "logger": ("System", "Logging"),
+ "environment_vars": ("System", "Environment variables"),
+ "telemetry": ("System", "Telemetry"),
+ "birdseye": ("System", "Birdseye"),
+ "detectors": ("System", "Detectors and model"),
+ "model": ("System", "Detectors and model"),
+}
+
+# All known top-level config section keys
+ALL_CONFIG_SECTIONS = (
+ set(GLOBAL_NAV)
+ | set(CAMERA_NAV)
+ | set(ENRICHMENT_NAV)
+ | set(SYSTEM_NAV)
+ | {"cameras"}
+)
+
+
+def get_nav_path(section_key: str, level: str = "global") -> str | None:
+ """Get the full navigation path for a config section.
+
+ Args:
+ section_key: Config section key (e.g., "record")
+ level: "global", "camera", "enrichment", or "system"
+
+ Returns:
+ NavPath string like "Settings > Global configuration > Recording",
+ or None if not found.
+ """
+ nav_tables = {
+ "global": GLOBAL_NAV,
+ "camera": CAMERA_NAV,
+ "enrichment": ENRICHMENT_NAV,
+ "system": SYSTEM_NAV,
+ }
+
+ table = nav_tables.get(level)
+ if table is None:
+ return None
+
+ entry = table.get(section_key)
+ if entry is None:
+ return None
+
+ group, page = entry
+ return f"Settings > {group} > {page}"
+
+
+def detect_level(section_key: str) -> str:
+ """Detect whether a config section is global, camera, enrichment, or system."""
+ if section_key in SYSTEM_NAV:
+ return "system"
+ if section_key in ENRICHMENT_NAV:
+ return "enrichment"
+ if section_key in GLOBAL_NAV:
+ return "global"
+ if section_key in CAMERA_NAV:
+ return "camera"
+ return "global"
diff --git a/docs/scripts/lib/schema_loader.py b/docs/scripts/lib/schema_loader.py
new file mode 100644
index 0000000000..a1e88a9896
--- /dev/null
+++ b/docs/scripts/lib/schema_loader.py
@@ -0,0 +1,88 @@
+"""Load JSON schema from Frigate's Pydantic config models."""
+
+from typing import Any
+
+
+def load_schema() -> dict[str, Any]:
+ """Generate and return the full JSON schema for FrigateConfig."""
+ from frigate.config.config import FrigateConfig
+ from frigate.util.schema import get_config_schema
+
+ return get_config_schema(FrigateConfig)
+
+
+def resolve_ref(schema: dict[str, Any], ref: str) -> dict[str, Any]:
+ """Resolve a $ref pointer within the schema."""
+ # ref format: "#/$defs/RecordConfig"
+ parts = ref.lstrip("#/").split("/")
+ node = schema
+ for part in parts:
+ node = node[part]
+ return node
+
+
+def resolve_schema_node(
+ schema: dict[str, Any], node: dict[str, Any]
+) -> dict[str, Any]:
+ """Resolve a schema node, following $ref and allOf if present."""
+ if "$ref" in node:
+ node = resolve_ref(schema, node["$ref"])
+ if "allOf" in node:
+ merged: dict[str, Any] = {}
+ for item in node["allOf"]:
+ resolved = resolve_schema_node(schema, item)
+ merged.update(resolved)
+ return merged
+ return node
+
+
+def get_section_schema(
+ schema: dict[str, Any], section_key: str
+) -> dict[str, Any] | None:
+ """Get the resolved schema for a top-level config section."""
+ props = schema.get("properties", {})
+ if section_key not in props:
+ return None
+ return resolve_schema_node(schema, props[section_key])
+
+
+def get_field_info(
+ schema: dict[str, Any], section_key: str, field_path: list[str]
+) -> dict[str, Any] | None:
+ """Get schema info for a specific field path within a section.
+
+ Args:
+ schema: Full JSON schema
+ section_key: Top-level section (e.g., "record")
+ field_path: List of nested keys (e.g., ["continuous", "days"])
+
+ Returns:
+ Resolved schema node for the field, or None if not found.
+ """
+ section = get_section_schema(schema, section_key)
+ if section is None:
+ return None
+
+ node = section
+ for key in field_path:
+ props = node.get("properties", {})
+ if key not in props:
+ return None
+ node = resolve_schema_node(schema, props[key])
+
+ return node
+
+
+def is_boolean_field(field_schema: dict[str, Any]) -> bool:
+ """Check if a schema node represents a boolean field."""
+ return field_schema.get("type") == "boolean"
+
+
+def is_enum_field(field_schema: dict[str, Any]) -> bool:
+ """Check if a schema node is an enum."""
+ return "enum" in field_schema
+
+
+def is_object_field(field_schema: dict[str, Any]) -> bool:
+ """Check if a schema node is an object with properties."""
+ return field_schema.get("type") == "object" or "properties" in field_schema
diff --git a/docs/scripts/lib/section_config_parser.py b/docs/scripts/lib/section_config_parser.py
new file mode 100644
index 0000000000..805ab21455
--- /dev/null
+++ b/docs/scripts/lib/section_config_parser.py
@@ -0,0 +1,130 @@
+"""Parse TypeScript section config files for hidden/advanced field info."""
+
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+SECTION_CONFIGS_DIR = (
+ Path(__file__).resolve().parents[3]
+ / "web"
+ / "src"
+ / "components"
+ / "config-form"
+ / "section-configs"
+)
+
+
+def _extract_string_array(text: str, field_name: str) -> list[str]:
+ """Extract a string array value from TypeScript object literal text."""
+ pattern = rf"{field_name}\s*:\s*\[(.*?)\]"
+ match = re.search(pattern, text, re.DOTALL)
+ if not match:
+ return []
+ content = match.group(1)
+ return re.findall(r'"([^"]*)"', content)
+
+
+def _parse_section_file(filepath: Path) -> dict[str, Any]:
+ """Parse a single section config .ts file."""
+ text = filepath.read_text()
+
+ # Extract base block
+ base_match = re.search(r"base\s*:\s*\{(.*?)\n \}", text, re.DOTALL)
+ base_text = base_match.group(1) if base_match else ""
+
+ # Extract global block
+ global_match = re.search(r"global\s*:\s*\{(.*?)\n \}", text, re.DOTALL)
+ global_text = global_match.group(1) if global_match else ""
+
+ # Extract camera block
+ camera_match = re.search(r"camera\s*:\s*\{(.*?)\n \}", text, re.DOTALL)
+ camera_text = camera_match.group(1) if camera_match else ""
+
+ result: dict[str, Any] = {
+ "fieldOrder": _extract_string_array(base_text, "fieldOrder"),
+ "hiddenFields": _extract_string_array(base_text, "hiddenFields"),
+ "advancedFields": _extract_string_array(base_text, "advancedFields"),
+ }
+
+ # Merge global-level hidden fields
+ global_hidden = _extract_string_array(global_text, "hiddenFields")
+ if global_hidden:
+ result["globalHiddenFields"] = global_hidden
+
+ # Merge camera-level hidden fields
+ camera_hidden = _extract_string_array(camera_text, "hiddenFields")
+ if camera_hidden:
+ result["cameraHiddenFields"] = camera_hidden
+
+ return result
+
+
+def load_section_configs() -> dict[str, dict[str, Any]]:
+ """Load all section configs from TypeScript files.
+
+ Returns:
+ Dict mapping section name to parsed config.
+ """
+ # Read sectionConfigs.ts to get the mapping of section keys to filenames
+ registry_path = SECTION_CONFIGS_DIR.parent / "sectionConfigs.ts"
+ registry_text = registry_path.read_text()
+
+ configs: dict[str, dict[str, Any]] = {}
+
+ for ts_file in SECTION_CONFIGS_DIR.glob("*.ts"):
+ if ts_file.name == "types.ts":
+ continue
+
+ section_name = ts_file.stem
+ configs[section_name] = _parse_section_file(ts_file)
+
+ # Map section config keys from the registry (handles renames like
+ # "timestamp_style: timestampStyle")
+ key_map: dict[str, str] = {}
+ for match in re.finditer(
+ r"(\w+)(?:\s*:\s*\w+)?\s*,", registry_text[registry_text.find("{") :]
+ ):
+ key = match.group(1)
+ key_map[key] = key
+
+ # Handle explicit key mappings like `timestamp_style: timestampStyle`
+ for match in re.finditer(r"(\w+)\s*:\s*(\w+)\s*,", registry_text):
+ key_map[match.group(1)] = match.group(2)
+
+ return configs
+
+
+def get_hidden_fields(
+ configs: dict[str, dict[str, Any]],
+ section_key: str,
+ level: str = "global",
+) -> set[str]:
+ """Get the set of hidden fields for a section at a given level.
+
+ Args:
+ configs: Loaded section configs
+ section_key: Config section name (e.g., "record")
+ level: "global" or "camera"
+
+ Returns:
+ Set of hidden field paths (e.g., {"enabled_in_config", "sync_recordings"})
+ """
+ config = configs.get(section_key, {})
+ hidden = set(config.get("hiddenFields", []))
+
+ if level == "global":
+ hidden.update(config.get("globalHiddenFields", []))
+ elif level == "camera":
+ hidden.update(config.get("cameraHiddenFields", []))
+
+ return hidden
+
+
+def get_advanced_fields(
+ configs: dict[str, dict[str, Any]],
+ section_key: str,
+) -> set[str]:
+ """Get the set of advanced fields for a section."""
+ config = configs.get(section_key, {})
+ return set(config.get("advancedFields", []))
diff --git a/docs/scripts/lib/ui_generator.py b/docs/scripts/lib/ui_generator.py
new file mode 100644
index 0000000000..7b9a592865
--- /dev/null
+++ b/docs/scripts/lib/ui_generator.py
@@ -0,0 +1,283 @@
+"""Generate UI tab markdown content from parsed YAML blocks."""
+
+from typing import Any
+
+from .i18n_loader import get_field_description, get_field_label, get_value_label
+from .nav_map import ALL_CONFIG_SECTIONS, detect_level, get_nav_path
+from .schema_loader import is_boolean_field, is_object_field
+from .section_config_parser import get_hidden_fields
+from .yaml_extractor import YamlBlock, get_leaf_paths
+
+
+def _format_value(
+ value: object,
+ field_schema: dict[str, Any] | None,
+ i18n: dict[str, Any] | None = None,
+) -> str:
+ """Format a YAML value for UI display.
+
+ Looks up i18n labels for enum/option values when available.
+ """
+ if field_schema and is_boolean_field(field_schema):
+ return "on" if value else "off"
+ if isinstance(value, bool):
+ return "on" if value else "off"
+ if isinstance(value, list):
+ if len(value) == 0:
+ return "an empty list"
+ items = []
+ for v in value:
+ label = get_value_label(i18n, str(v)) if i18n else None
+ items.append(f"`{label}`" if label else f"`{v}`")
+ return ", ".join(items)
+ if value is None:
+ return "empty"
+
+ # Try i18n label for the raw value (enum translations)
+ if i18n and isinstance(value, str):
+ label = get_value_label(i18n, value)
+ if label:
+ return f"`{label}`"
+
+ return f"`{value}`"
+
+
+def _build_field_label(
+ i18n: dict[str, Any],
+ section_key: str,
+ field_path: list[str],
+ level: str,
+) -> str:
+ """Build the display label for a field using i18n labels.
+
+ For a path like ["continuous", "days"], produces
+ "Continuous retention > Retention days" using the actual i18n labels.
+ """
+ parts: list[str] = []
+
+ for depth in range(len(field_path)):
+ sub_path = field_path[: depth + 1]
+ label = get_field_label(i18n, section_key, sub_path, level)
+
+ if label:
+ parts.append(label)
+ else:
+ # Fallback to title-cased field name
+ parts.append(field_path[depth].replace("_", " ").title())
+
+ return " > ".join(parts)
+
+
+def _is_hidden(
+ field_key: str,
+ full_path: list[str],
+ hidden_fields: set[str],
+) -> bool:
+ """Check if a field should be hidden from UI output."""
+ # Check exact match
+ if field_key in hidden_fields:
+ return True
+
+ # Check dotted path match (e.g., "alerts.enabled_in_config")
+ dotted = ".".join(str(p) for p in full_path)
+ if dotted in hidden_fields:
+ return True
+
+ # Check wildcard patterns (e.g., "filters.*.mask")
+ for pattern in hidden_fields:
+ if "*" in pattern:
+ parts = pattern.split(".")
+ if len(parts) == len(full_path):
+ match = all(
+ p == "*" or p == fp for p, fp in zip(parts, full_path)
+ )
+ if match:
+ return True
+
+ return False
+
+
+def generate_ui_content(
+ block: YamlBlock,
+ schema: dict[str, Any],
+ i18n: dict[str, Any],
+ section_configs: dict[str, dict[str, Any]],
+) -> str | None:
+ """Generate UI tab markdown content for a YAML block.
+
+ Args:
+ block: Parsed YAML block from a doc file
+ schema: Full JSON schema
+ i18n: Loaded i18n translations
+ section_configs: Parsed section config data
+
+ Returns:
+ Generated markdown string for the UI tab, or None if the block
+ can't be converted (not a config block, etc.)
+ """
+ if block.section_key is None:
+ return None
+
+ # Determine which config data to walk
+ if block.is_camera_level:
+ # Camera-level: unwrap cameras.{name}.{section}
+ cam_data = block.parsed.get("cameras", {})
+ cam_name = block.camera_name or next(iter(cam_data), None)
+ if not cam_name:
+ return None
+ inner = cam_data.get(cam_name, {})
+ if not isinstance(inner, dict):
+ return None
+ level = "camera"
+ else:
+ inner = block.parsed
+ # Determine level from section key
+ level = detect_level(block.section_key)
+
+ # Collect sections to process (may span multiple top-level keys)
+ sections_to_process: list[tuple[str, dict]] = []
+ for key in inner:
+ if key in ALL_CONFIG_SECTIONS or key == block.section_key:
+ val = inner[key]
+ if isinstance(val, dict):
+ sections_to_process.append((key, val))
+ else:
+ # Simple scalar at section level (e.g., record.enabled = True)
+ sections_to_process.append((key, {key: val}))
+
+ # If inner is the section itself (e.g., parsed = {"record": {...}})
+ if not sections_to_process and block.section_key in inner:
+ section_data = inner[block.section_key]
+ if isinstance(section_data, dict):
+ sections_to_process = [(block.section_key, section_data)]
+
+ if not sections_to_process:
+ # Try treating the whole inner dict as the section data
+ sections_to_process = [(block.section_key, inner)]
+
+ # Choose pattern based on whether YAML has comments (descriptive) or values
+ use_table = block.has_comments
+
+ lines: list[str] = []
+ step_num = 1
+
+ for section_key, section_data in sections_to_process:
+ # Get navigation path
+ i18n_level = "cameras" if level == "camera" else "global"
+ nav_path = get_nav_path(section_key, level)
+ if nav_path is None:
+ # Try global as fallback
+ nav_path = get_nav_path(section_key, "global")
+ if nav_path is None:
+ continue
+
+ # Get hidden fields for this section
+ hidden = get_hidden_fields(section_configs, section_key, level)
+
+ # Get leaf paths from the YAML data
+ leaves = get_leaf_paths(section_data)
+
+ # Filter out hidden fields
+ visible_leaves: list[tuple[tuple[str, ...], object]] = []
+ for path, value in leaves:
+ path_list = list(path)
+ if not _is_hidden(path_list[-1], path_list, hidden):
+ visible_leaves.append((path, value))
+
+ if not visible_leaves:
+ continue
+
+ if use_table:
+ # Pattern A: Field table with descriptions
+ lines.append(
+ f'Navigate to .'
+ )
+ lines.append("")
+ lines.append("| Field | Description |")
+ lines.append("|-------|-------------|")
+
+ for path, _value in visible_leaves:
+ path_list = list(path)
+ label = _build_field_label(
+ i18n, section_key, path_list, i18n_level
+ )
+ desc = get_field_description(
+ i18n, section_key, path_list, i18n_level
+ )
+ if not desc:
+ desc = ""
+ lines.append(f"| **{label}** | {desc} |")
+ else:
+ # Pattern B: Set instructions
+ multi_section = len(sections_to_process) > 1
+
+ if multi_section:
+ camera_note = ""
+ if block.is_camera_level:
+ camera_note = (
+ " and select your camera"
+ )
+ lines.append(
+ f'{step_num}. Navigate to {camera_note}.'
+ )
+ else:
+ if block.is_camera_level:
+ lines.append(
+ f'1. Navigate to and select your camera.'
+ )
+ else:
+ lines.append(
+ f'Navigate to .'
+ )
+ lines.append("")
+
+ from .schema_loader import get_field_info
+
+ for path, value in visible_leaves:
+ path_list = list(path)
+ label = _build_field_label(
+ i18n, section_key, path_list, i18n_level
+ )
+ field_info = get_field_info(schema, section_key, path_list)
+ formatted = _format_value(value, field_info, i18n)
+
+ if multi_section or block.is_camera_level:
+ lines.append(f" - Set **{label}** to {formatted}")
+ else:
+ lines.append(f"- Set **{label}** to {formatted}")
+
+ step_num += 1
+
+ if not lines:
+ return None
+
+ return "\n".join(lines)
+
+
+def wrap_with_config_tabs(ui_content: str, yaml_raw: str, highlight: str | None = None) -> str:
+ """Wrap UI content and YAML in ConfigTabs markup.
+
+ Args:
+ ui_content: Generated UI tab markdown
+ yaml_raw: Original YAML text
+ highlight: Optional highlight spec (e.g., "{3-4}")
+
+ Returns:
+ Full ConfigTabs MDX block
+ """
+ highlight_str = f" {highlight}" if highlight else ""
+
+ return f"""
+
+
+{ui_content}
+
+
+
+
+```yaml{highlight_str}
+{yaml_raw}
+```
+
+
+"""
diff --git a/docs/scripts/lib/yaml_extractor.py b/docs/scripts/lib/yaml_extractor.py
new file mode 100644
index 0000000000..c01451cfcc
--- /dev/null
+++ b/docs/scripts/lib/yaml_extractor.py
@@ -0,0 +1,283 @@
+"""Extract YAML code blocks from markdown documentation files."""
+
+import re
+from dataclasses import dataclass, field
+
+import yaml
+
+
+@dataclass
+class YamlBlock:
+ """A YAML code block extracted from a markdown file."""
+
+ raw: str # Original YAML text
+ parsed: dict # Parsed YAML content
+ line_start: int # Line number in the markdown file (1-based)
+ line_end: int # End line number
+ highlight: str | None = None # Highlight spec (e.g., "{3-4}")
+ has_comments: bool = False # Whether the YAML has inline comments
+ inside_config_tabs: bool = False # Already wrapped in ConfigTabs
+ section_key: str | None = None # Detected top-level config section
+ is_camera_level: bool = False # Whether this is camera-level config
+ camera_name: str | None = None # Camera name if camera-level
+ config_keys: list[str] = field(
+ default_factory=list
+ ) # Top-level keys in the YAML
+
+
+def extract_yaml_blocks(content: str) -> list[YamlBlock]:
+ """Extract all YAML fenced code blocks from markdown content.
+
+ Args:
+ content: Markdown file content
+
+ Returns:
+ List of YamlBlock instances
+ """
+ blocks: list[YamlBlock] = []
+ lines = content.split("\n")
+ i = 0
+ in_config_tabs = False
+
+ while i < len(lines):
+ line = lines[i]
+
+ # Track ConfigTabs context
+ if "" in line:
+ in_config_tabs = True
+ elif "" in line:
+ in_config_tabs = False
+
+ # Look for YAML fence opening
+ fence_match = re.match(r"^```yaml\s*(\{[^}]*\})?\s*$", line)
+ if fence_match:
+ highlight = fence_match.group(1)
+ start_line = i + 1 # 1-based
+ yaml_lines: list[str] = []
+ i += 1
+
+ # Collect until closing fence
+ while i < len(lines) and not lines[i].startswith("```"):
+ yaml_lines.append(lines[i])
+ i += 1
+
+ end_line = i + 1 # 1-based, inclusive of closing fence
+ raw = "\n".join(yaml_lines)
+
+ # Check for inline comments
+ has_comments = any(
+ re.search(r"#\s*(<-|[A-Za-z])", yl) for yl in yaml_lines
+ )
+
+ # Parse YAML
+ try:
+ parsed = yaml.safe_load(raw)
+ except yaml.YAMLError:
+ i += 1
+ continue
+
+ if not isinstance(parsed, dict):
+ i += 1
+ continue
+
+ # Detect config section and level
+ config_keys = list(parsed.keys())
+ section_key = None
+ is_camera = False
+ camera_name = None
+
+ if "cameras" in parsed and isinstance(parsed["cameras"], dict):
+ is_camera = True
+ cam_entries = parsed["cameras"]
+ if len(cam_entries) == 1:
+ camera_name = list(cam_entries.keys())[0]
+ inner = cam_entries[camera_name]
+ if isinstance(inner, dict):
+ inner_keys = list(inner.keys())
+ if len(inner_keys) >= 1:
+ section_key = inner_keys[0]
+ elif len(config_keys) >= 1:
+ section_key = config_keys[0]
+
+ blocks.append(
+ YamlBlock(
+ raw=raw,
+ parsed=parsed,
+ line_start=start_line,
+ line_end=end_line,
+ highlight=highlight,
+ has_comments=has_comments,
+ inside_config_tabs=in_config_tabs,
+ section_key=section_key,
+ is_camera_level=is_camera,
+ camera_name=camera_name,
+ config_keys=config_keys,
+ )
+ )
+
+ i += 1
+
+ return blocks
+
+
+@dataclass
+class ConfigTabsBlock:
+ """An existing ConfigTabs block in a markdown file."""
+
+ line_start: int # 1-based line of
+ line_end: int # 1-based line of
+ ui_content: str # Content inside the UI TabItem
+ yaml_block: YamlBlock # The YAML block inside the YAML TabItem
+ raw_text: str # Full raw text of the ConfigTabs block
+
+
+def extract_config_tabs_blocks(content: str) -> list[ConfigTabsBlock]:
+ """Extract existing ConfigTabs blocks from markdown content.
+
+ Parses the structure:
+
+
+ ...ui content...
+
+
+ ```yaml
+ ...yaml...
+ ```
+
+
+
+ Returns:
+ List of ConfigTabsBlock instances
+ """
+ blocks: list[ConfigTabsBlock] = []
+ lines = content.split("\n")
+ i = 0
+
+ while i < len(lines):
+ if "" not in lines[i]:
+ i += 1
+ continue
+
+ block_start = i # 0-based
+
+ # Find
+ j = i + 1
+ while j < len(lines) and "" not in lines[j]:
+ j += 1
+
+ if j >= len(lines):
+ i += 1
+ continue
+
+ block_end = j # 0-based, line with
+ block_text = "\n".join(lines[block_start : block_end + 1])
+
+ # Extract UI content (between and )
+ ui_match = re.search(
+ r'\s*\n(.*?)\n\s*',
+ block_text,
+ re.DOTALL,
+ )
+ ui_content = ui_match.group(1).strip() if ui_match else ""
+
+ # Extract YAML block from inside the yaml TabItem
+ yaml_tab_match = re.search(
+ r'\s*\n(.*?)\n\s*',
+ block_text,
+ re.DOTALL,
+ )
+
+ yaml_block = None
+ if yaml_tab_match:
+ yaml_tab_text = yaml_tab_match.group(1)
+ fence_match = re.search(
+ r"```yaml\s*(\{[^}]*\})?\s*\n(.*?)\n```",
+ yaml_tab_text,
+ re.DOTALL,
+ )
+ if fence_match:
+ highlight = fence_match.group(1)
+ yaml_raw = fence_match.group(2)
+ has_comments = bool(
+ re.search(r"#\s*(<-|[A-Za-z])", yaml_raw)
+ )
+
+ try:
+ parsed = yaml.safe_load(yaml_raw)
+ except yaml.YAMLError:
+ parsed = {}
+
+ if isinstance(parsed, dict):
+ config_keys = list(parsed.keys())
+ section_key = None
+ is_camera = False
+ camera_name = None
+
+ if "cameras" in parsed and isinstance(
+ parsed["cameras"], dict
+ ):
+ is_camera = True
+ cam_entries = parsed["cameras"]
+ if len(cam_entries) == 1:
+ camera_name = list(cam_entries.keys())[0]
+ inner = cam_entries[camera_name]
+ if isinstance(inner, dict):
+ inner_keys = list(inner.keys())
+ if len(inner_keys) >= 1:
+ section_key = inner_keys[0]
+ elif len(config_keys) >= 1:
+ section_key = config_keys[0]
+
+ yaml_block = YamlBlock(
+ raw=yaml_raw,
+ parsed=parsed,
+ line_start=block_start + 1,
+ line_end=block_end + 1,
+ highlight=highlight,
+ has_comments=has_comments,
+ inside_config_tabs=True,
+ section_key=section_key,
+ is_camera_level=is_camera,
+ camera_name=camera_name,
+ config_keys=config_keys,
+ )
+
+ if yaml_block:
+ blocks.append(
+ ConfigTabsBlock(
+ line_start=block_start + 1, # 1-based
+ line_end=block_end + 1, # 1-based
+ ui_content=ui_content,
+ yaml_block=yaml_block,
+ raw_text=block_text,
+ )
+ )
+
+ i = j + 1
+
+ return blocks
+
+
+def get_leaf_paths(
+ data: dict, prefix: tuple[str, ...] = ()
+) -> list[tuple[tuple[str, ...], object]]:
+ """Walk a parsed YAML dict and return all leaf key paths with values.
+
+ Args:
+ data: Parsed YAML dict
+ prefix: Current key path prefix
+
+ Returns:
+ List of (key_path_tuple, value) pairs.
+ e.g., [( ("record", "continuous", "days"), 3 ), ...]
+ """
+ results: list[tuple[tuple[str, ...], object]] = []
+
+ for key, value in data.items():
+ path = prefix + (str(key),)
+ if isinstance(value, dict):
+ results.extend(get_leaf_paths(value, path))
+ else:
+ results.append((path, value))
+
+ return results
diff --git a/docs/sidebars.ts b/docs/sidebars.ts
index ea0d2f5c81..4c656df8c2 100644
--- a/docs/sidebars.ts
+++ b/docs/sidebars.ts
@@ -12,94 +12,132 @@ const sidebars: SidebarsConfig = {
"frigate/updating",
"frigate/camera_setup",
"frigate/video_pipeline",
+ "frigate/network_requirements",
"frigate/glossary",
],
Guides: [
"guides/getting_started",
- "guides/configuring_go2rtc",
"guides/ha_notifications",
"guides/ha_network_storage",
"guides/reverse_proxy",
],
- Configuration: {
- "Configuration Files": [
- "configuration/index",
- "configuration/reference",
- {
- type: "link",
- label: "Go2RTC Configuration Reference",
- href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.10#configuration",
- } as PropSidebarItemLink,
- ],
- Detectors: [
- "configuration/object_detectors",
- "configuration/audio_detectors",
- ],
- Enrichments: [
- "configuration/semantic_search",
- "configuration/face_recognition",
- "configuration/license_plate_recognition",
- "configuration/bird_classification",
- {
- type: "category",
- label: "Custom Classification",
- link: {
- type: "generated-index",
- title: "Custom Classification",
- description: "Configuration for custom classification models",
+ Usage: [
+ "usage/live",
+ "usage/review",
+ "usage/history",
+ "usage/explore",
+ "usage/exports",
+ ],
+ Configuration: [
+ "configuration/config",
+ "configuration/config_overrides",
+ {
+ type: "category",
+ label: "Detectors",
+ items: [
+ "configuration/object_detectors",
+ "configuration/audio_detectors",
+ ],
+ },
+ {
+ type: "category",
+ label: "Enrichments",
+ items: [
+ "configuration/semantic_search",
+ "configuration/face_recognition",
+ "configuration/license_plate_recognition",
+ "configuration/bird_classification",
+ {
+ type: "category",
+ label: "Custom Classification",
+ link: {
+ type: "generated-index",
+ title: "Custom Classification",
+ description: "Configuration for custom classification models",
+ },
+ items: [
+ "configuration/custom_classification/state_classification",
+ "configuration/custom_classification/object_classification",
+ ],
},
- items: [
- "configuration/custom_classification/state_classification",
- "configuration/custom_classification/object_classification",
- ],
- },
- {
- type: "category",
- label: "Generative AI",
- link: {
- type: "generated-index",
- title: "Generative AI",
- description: "Generative AI Features",
+ {
+ type: "category",
+ label: "Generative AI",
+ link: {
+ type: "generated-index",
+ title: "Generative AI",
+ description: "Generative AI Features",
+ },
+ items: [
+ "configuration/genai/genai_config",
+ "configuration/genai/genai_review",
+ "configuration/genai/genai_objects",
+ ],
},
- items: [
- "configuration/genai/genai_config",
- "configuration/genai/genai_review",
- "configuration/genai/genai_objects",
- ],
- },
- ],
- Cameras: [
- "configuration/cameras",
- "configuration/review",
- "configuration/record",
- "configuration/snapshots",
- "configuration/motion_detection",
- "configuration/birdseye",
- "configuration/live",
- "configuration/restream",
- "configuration/autotracking",
- "configuration/camera_specific",
- ],
- Objects: [
- "configuration/object_filters",
- "configuration/masks",
- "configuration/zones",
- "configuration/objects",
- "configuration/stationary_objects",
- ],
- "Hardware Acceleration": [
- "configuration/hardware_acceleration_video",
- "configuration/hardware_acceleration_enrichments",
- ],
- "Extra Configuration": [
- "configuration/authentication",
- "configuration/notifications",
- "configuration/ffmpeg_presets",
- "configuration/pwa",
- "configuration/tls",
- "configuration/advanced",
- ],
- },
+ ],
+ },
+ {
+ type: "category",
+ label: "Cameras",
+ items: [
+ "configuration/cameras",
+ "configuration/review",
+ "configuration/record",
+ "configuration/snapshots",
+ "configuration/motion_detection",
+ "configuration/birdseye",
+ "configuration/live",
+ "configuration/restream",
+ "configuration/autotracking",
+ "configuration/camera_specific",
+ ],
+ },
+ {
+ type: "category",
+ label: "Objects",
+ items: [
+ "configuration/object_filters",
+ "configuration/masks",
+ "configuration/zones",
+ "configuration/objects",
+ "configuration/stationary_objects",
+ ],
+ },
+ {
+ type: "category",
+ label: "Hardware Acceleration",
+ items: [
+ "configuration/hardware_acceleration_video",
+ "configuration/hardware_acceleration_enrichments",
+ ],
+ },
+ {
+ type: "category",
+ label: "Extra Configuration",
+ items: [
+ "configuration/authentication",
+ "configuration/notifications",
+ "configuration/profiles",
+ "configuration/go2rtc",
+ "configuration/ffmpeg_presets",
+ "configuration/pwa",
+ "configuration/tls",
+ ],
+ },
+ {
+ type: "category",
+ label: "Advanced Configuration",
+ items: [
+ "configuration/advanced/system",
+ "configuration/advanced/reference",
+ {
+ type: "link",
+ label: "Go2RTC Configuration Reference",
+ href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.14#configuration",
+ } as PropSidebarItemLink,
+ ],
+ },
+ ],
Integrations: [
"integrations/plus",
"integrations/home-assistant",
@@ -128,6 +166,8 @@ const sidebars: SidebarsConfig = {
],
Troubleshooting: [
"troubleshooting/faqs",
+ "troubleshooting/common_errors",
+ "troubleshooting/go2rtc",
"troubleshooting/recordings",
"troubleshooting/dummy-camera",
{
diff --git a/docs/src/components/ConfigTabs/index.jsx b/docs/src/components/ConfigTabs/index.jsx
new file mode 100644
index 0000000000..0fbc51897b
--- /dev/null
+++ b/docs/src/components/ConfigTabs/index.jsx
@@ -0,0 +1,34 @@
+import React, { Children, cloneElement } from "react";
+import Tabs from "@theme/Tabs";
+import TabItem from "@theme/TabItem";
+
+export default function ConfigTabs({ children }) {
+ const wrapped = Children.map(children, (child) => {
+ if (child?.props?.value === "ui") {
+ return cloneElement(child, {
+ className: "config-tab-ui",
+ });
+ }
+ if (child?.props?.value === "yaml") {
+ return cloneElement(child, {
+ className: "config-tab-yaml",
+ });
+ }
+ return child;
+ });
+
+ return (
+
+
+ {wrapped}
+
+
+ );
+}
diff --git a/docs/src/components/DockerComposeGenerator/DockerComposeGenerator.tsx b/docs/src/components/DockerComposeGenerator/DockerComposeGenerator.tsx
new file mode 100644
index 0000000000..b8a8a8fc85
--- /dev/null
+++ b/docs/src/components/DockerComposeGenerator/DockerComposeGenerator.tsx
@@ -0,0 +1,108 @@
+import React from "react";
+import Admonition from "@theme/Admonition";
+import DeviceSelector from "./components/DeviceSelector";
+import HardwareOptions from "./components/HardwareOptions";
+import PortConfigSection from "./components/PortConfig";
+import StoragePaths from "./components/StoragePaths";
+import NvidiaGpuConfig from "./components/NvidiaGpuConfig";
+import OtherOptions from "./components/OtherOptions";
+import GeneratedOutput from "./components/GeneratedOutput";
+import { useConfigGenerator } from "./hooks/useConfigGenerator";
+import styles from "./styles.module.css";
+
+/**
+ * Simple markdown-link-to-React renderer for help text.
+ * Only supports [text](url) syntax — no nested brackets.
+ */
+function renderHelpText(text: string): React.ReactNode {
+ const parts = text.split(/(\[[^\]]+\]\([^)]+\))/g);
+ return parts.map((part, i) => {
+ const match = part.match(/^\[([^\]]+)\]\(([^)]+)\)$/);
+ if (match) {
+ return (
+
+ {match[1]}
+
+ );
+ }
+ return {part};
+ });
+}
+
+export default function DockerComposeGenerator() {
+ const {
+ deviceId, device, hardwareEnabled,
+ portEnabled,
+ nvidiaGpuCount, nvidiaGpuDeviceId,
+ configPath, mediaPath, rtspPassword, timezone, shmSize,
+ shmSizeError, gpuDeviceIdError, configPathError, mediaPathError,
+ hasAnyHardware, generatedYaml,
+ selectDevice, toggleHardware, togglePort,
+ handleShmSizeChange, handleConfigPathChange, handleMediaPathChange,
+ handleNvidiaGpuCountChange, handleNvidiaGpuDeviceIdChange,
+ setRtspPassword, setTimezone, isHardwareDisabled,
+ } = useConfigGenerator();
+
+ return (
+