fix popovers from being immediately dismissed on safari

the callback only needs a single state update for the useeffect to fire

.cspell/frigate-dictionary.txt

@@ -22,7 +22,6 @@ autotrack
 autotracked
 autotracker
 autotracking
-backchannel
 balena
 Beelink
 BGRA
@@ -192,7 +191,6 @@ ONVIF
 openai
 opencv
 openvino
-overfitting
 OWASP
 paddleocr
 paho
@@ -229,7 +227,6 @@ Reolink
 restream
 restreamed
 restreaming
-RJSF
 rkmpp
 rknn
 rkrga
@@ -318,4 +315,4 @@ yolo
 yolonas
 yolox
 zeep
-zerolatency
+zerolatency

.github/DISCUSSION_TEMPLATE/beta-support.yml

@@ -1,129 +0,0 @@
-title: "[Beta Support]: "
-labels: ["support", "triage", "beta"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thank you for testing Frigate beta versions! Use this form for support with beta releases.
-
-        **Note:** Beta versions may have incomplete features, known issues, or unexpected behavior. Please check the [release notes](https://github.com/blakeblackshear/frigate/releases) and [recent discussions][discussions] for known beta issues before submitting.
-
-        Before submitting, read the [beta documentation][docs].
-
-        [docs]: https://deploy-preview-19787--frigate-docs.netlify.app/
-  - type: textarea
-    id: description
-    attributes:
-      label: Describe the problem you are having
-      description: Please be as detailed as possible. Include what you expected to happen vs what actually happened.
-    validations:
-      required: true
-  - type: input
-    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"
-    validations:
-      required: true
-  - type: dropdown
-    id: issue-category
-    attributes:
-      label: Issue Category
-      description: What area is your issue related to? This helps us understand the context.
-      options:
-        - Object Detection / Detectors
-        - Hardware Acceleration
-        - Configuration / Setup
-        - WebUI / Frontend
-        - Recordings / Storage
-        - Notifications / Events
-        - Integration (Home Assistant, etc)
-        - Performance / Stability
-        - Installation / Updates
-        - Other
-    validations:
-      required: true
-  - type: textarea
-    id: config
-    attributes:
-      label: Frigate config file
-      description: This will be automatically formatted into code, so no need for backticks. Remove any sensitive information like passwords or URLs.
-      render: yaml
-    validations:
-      required: true
-  - type: textarea
-    id: frigatelogs
-    attributes:
-      label: Relevant Frigate log output
-      description: Please copy and paste any relevant Frigate log output. Include logs before and after your exact error when possible. This will be automatically formatted into code, so no need for backticks.
-      render: shell
-    validations:
-      required: true
-  - type: textarea
-    id: go2rtclogs
-    attributes:
-      label: Relevant go2rtc log output (if applicable)
-      description: If your issue involves cameras, streams, or playback, please include go2rtc logs. Logs can be viewed via the Frigate UI, Docker, or the go2rtc dashboard. This will be automatically formatted into code, so no need for backticks.
-      render: shell
-  - type: dropdown
-    id: install-method
-    attributes:
-      label: Install method
-      options:
-        - Home Assistant Add-on
-        - Docker Compose
-        - Docker CLI
-        - Proxmox via Docker
-        - Proxmox via TTeck Script
-        - Windows WSL2
-    validations:
-      required: true
-  - type: textarea
-    id: docker
-    attributes:
-      label: docker-compose file or Docker CLI command
-      description: This will be automatically formatted into code, so no need for backticks. Include relevant environment variables and device mappings.
-      render: yaml
-    validations:
-      required: true
-  - type: dropdown
-    id: os
-    attributes:
-      label: Operating system
-      options:
-        - Home Assistant OS
-        - Debian
-        - Ubuntu
-        - Other Linux
-        - Proxmox
-        - UNRAID
-        - Windows
-        - Other
-    validations:
-      required: true
-  - type: input
-    id: hardware
-    attributes:
-      label: CPU / GPU / Hardware
-      description: Provide details about your hardware (e.g., Intel i5-9400, NVIDIA RTX 3060, Raspberry Pi 4, etc)
-      placeholder: "Intel i7-10700, NVIDIA GTX 1660"
-  - type: textarea
-    id: screenshots
-    attributes:
-      label: Screenshots
-      description: Screenshots of the issue, System metrics pages, or any relevant UI. Drag and drop or paste images directly.
-  - type: textarea
-    id: steps-to-reproduce
-    attributes:
-      label: Steps to reproduce
-      description: If applicable, provide detailed steps to reproduce the issue
-      placeholder: |
-        1. Go to '...'
-        2. Click on '...'
-        3. See error
-  - type: textarea
-    id: other
-    attributes:
-      label: Any other information that may be helpful
-      description: Additional context, related issues, when the problem started appearing, etc.

.github/DISCUSSION_TEMPLATE/report-a-bug.yml

@@ -6,8 +6,6 @@ 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.**
-
         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.**

.github/copilot-instructions.md

@@ -1,401 +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 <div>{t("camera_not_found")}</div>;
-  }
-  ```
-
-- **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
-<div>Camera not found</div>  # ❌ 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();
-<div>{t("camera_not_found")}</div>  # ✅ 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",
-        },
-    )
-```
-
-## 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

.github/pull_request_template.md

@@ -1,18 +1,17 @@
-_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) before submitting a PR._
-
 ## Proposed change
-
 <!--
   Thank you!
 
-  Describe what this pull request does and how it will benefit users of Frigate.
-  Please describe in detail any considerations, breaking changes, etc.
-
   If you're introducing a new feature or significantly refactoring existing functionality,
   we encourage you to start a discussion first. This helps ensure your idea aligns with
   Frigate's development goals.
+
+  Describe what this pull request does and how it will benefit users of Frigate.
+  Please describe in detail any considerations, breaking changes, etc. that are
+  made in this pull request.
 -->
 
+
 ## Type of change
 
 - [ ] Dependency upgrade
@@ -26,45 +25,6 @@ _Please read the [contributing guidelines](https://github.com/blakeblackshear/fr
 
 - This PR fixes or closes issue: fixes #
 - This PR is related to issue:
-- Link to discussion with maintainers (**required** for large/pinned features):
-
-## For new features
-
-<!--
-  Every new feature adds scope that maintainers must test, maintain, and support long-term.
-  We try to be thoughtful about what we take on, and sometimes that means saying no to
-  good code if the feature isn't the right fit — or saying yes to something we weren't sure
-  about. These calls are sometimes subjective, and we won't always get them right. We're
-  happy to discuss and reconsider.
-
-  Linking to an existing feature request or discussion with community interest helps us
-  understand demand, but a great idea is a great idea even without a crowd behind it.
-
-  You can delete this section for bugfixes and non-feature changes.
--->
-
-- [ ] There is an existing feature request or discussion with community interest for this change.
-  - Link:
-
-## AI disclosure
-
-<!--
-  We welcome contributions that use AI tools, but we need to understand your relationship
-  with the code you're submitting. See our AI usage policy in CONTRIBUTING.md for details.
-
-  Be honest — this won't disqualify your PR. Trust matters more than method.
--->
-
-- [ ] 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
 
@@ -75,6 +35,5 @@ _Please read the [contributing guidelines](https://github.com/blakeblackshear/fr
 - [ ] 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`)

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  PYTHON_VERSION: 3.11
+  PYTHON_VERSION: 3.9
 
 jobs:
   amd64_build:
@@ -23,7 +23,7 @@ jobs:
     name: AMD64 Build
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -32,7 +32,7 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - name: Build and push amd64 standard build
-        uses: docker/build-push-action@v7
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: docker/main/Dockerfile
@@ -47,7 +47,7 @@ jobs:
     name: ARM Build
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -56,7 +56,7 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - name: Build and push arm64 standard build
-        uses: docker/build-push-action@v7
+        uses: docker/build-push-action@v5
         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@v7
+        uses: docker/bake-action@v6
         with:
           source: .
           push: true
@@ -82,7 +82,7 @@ jobs:
     name: Jetson Jetpack 6
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -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@v7
+        uses: docker/bake-action@v6
         with:
           source: .
           push: true
@@ -113,7 +113,7 @@ jobs:
       - amd64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -124,7 +124,7 @@ jobs:
       - name: Build and push TensorRT (x86 GPU)
         env:
           COMPUTE_LEVEL: "50 60 70 80 90"
-        uses: docker/bake-action@v7
+        uses: docker/bake-action@v6
         with:
           source: .
           push: true
@@ -136,8 +136,9 @@ jobs:
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-tensorrt,mode=max
       - name: AMD/ROCm general build
         env:
+          AMDGPU: gfx
           HSA_OVERRIDE: 0
-        uses: docker/bake-action@v7
+        uses: docker/bake-action@v6
         with:
           source: .
           push: true
@@ -154,7 +155,7 @@ jobs:
       - arm64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -163,7 +164,7 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - name: Build and push Rockchip build
-        uses: docker/bake-action@v7
+        uses: docker/bake-action@v6
         with:
           source: .
           push: true
@@ -179,7 +180,7 @@ jobs:
       - arm64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -188,7 +189,7 @@ jobs:
         with:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       - name: Build and push Synaptics build
-        uses: docker/bake-action@v7
+        uses: docker/bake-action@v6
         with:
           source: .
           push: true

.github/workflows/pr_template_check.yml

@@ -1,120 +0,0 @@
-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@v7
-        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*)?([\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.');

.github/workflows/pull_request.yml

@@ -16,29 +16,26 @@ jobs:
     name: Web - Lint
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
-          node-version: 20.x
+          node-version: 16.x
       - run: npm install
         working-directory: ./web
       - 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
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
           node-version: 20.x
       - run: npm install
@@ -50,43 +47,12 @@ 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@v4
-        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
     steps:
       - name: Check out the repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
@@ -109,10 +75,10 @@ jobs:
     name: Python Tests
     steps:
       - name: Check out code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
           node-version: 20.x
       - name: Install devcontainer cli

.github/workflows/release.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v5
         with:
           persist-credentials: false
       - id: lowercaseRepo
@@ -39,14 +39,14 @@ jobs:
           STABLE_TAG=${BASE}:stable
           PULL_TAG=${BASE}:${BUILD_TAG}
           docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${VERSION_TAG}
-          for variant in standard-arm64 tensorrt tensorrt-jp6 rk rocm synaptics; do
+          for variant in standard-arm64 tensorrt tensorrt-jp6 rk rocm; do
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${VERSION_TAG}-${variant}
           done
 
           # stable tag
           if [[ "${BUILD_TYPE}" == "stable" ]]; then
             docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG} docker://${STABLE_TAG}
-            for variant in standard-arm64 tensorrt tensorrt-jp6 rk rocm synaptics; do
+            for variant in standard-arm64 tensorrt tensorrt-jp6 rk rocm; do
               docker run --rm -v $HOME/.docker/config.json:/config.json quay.io/skopeo/stable:latest copy --authfile /config.json --multi-arch all docker://${PULL_TAG}-${variant} docker://${STABLE_TAG}-${variant}
             done
           fi

.gitignore

@@ -3,8 +3,6 @@ __pycache__
 .mypy_cache
 *.swp
 debug
-.claude/*
-.mcp.json
 .vscode/*
 !.vscode/launch.json
 config/*
@@ -21,4 +19,4 @@ web/.env
 core
 !/web/**/*.ts
 .idea/*
-.ipynb_checkpoints
+.ipynb_checkpoints

.vscode/launch.json

@@ -6,23 +6,6 @@
       "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"
     }
   ]
 }

CONTRIBUTING.md

@@ -1,140 +0,0 @@
-# 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.
-
-## 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
-
-Every new feature adds scope that the maintainers must test, maintain, and support long-term. 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. Pinned feature requests 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.
-3. **Be open to "no".** We try to be thoughtful about what we take on, and sometimes that means saying no to good code if the feature isn't the right fit for the project. These calls are sometimes subjective, and we won't always get them right. We're happy to discuss and reconsider.
-
-## 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. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
-
-### 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 they 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. **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.
-
-## 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)

LICENSE

@@ -1,6 +1,6 @@
 The MIT License
 
-Copyright (c) 2026 Frigate, Inc. (Frigate™)
+Copyright (c) 2020 Blake Blackshear
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.18.0
+VERSION = 0.17.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
@@ -49,8 +49,7 @@ push: push-boards
 		--push
 
 run: local
-	docker run --rm --publish=5000:5000 --publish=8971:8971 \
-		--volume=${PWD}/config:/config frigate:latest
+	docker run --rm --publish=5000:5000 --volume=${PWD}/config:/config frigate:latest
 
 run_tests: local
 	docker run --rm --workdir=/opt/frigate --entrypoint= frigate:latest \

README.md

@@ -1,10 +1,8 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/frigate.png">
 </p>
 
-# Frigate NVR™ - Realtime Object Detection for IP Cameras
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+# Frigate - NVR With Realtime Object Detection for IP Cameras
 
 <a href="https://hosted.weblate.org/engage/frigate-nvr/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/language-badge.svg" alt="Translation status" />
@@ -35,15 +33,6 @@ View the documentation at https://docs.frigate.video
 
 If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
 
-## License
-
-This project is licensed under the **MIT License**.
-
-- **Code:** The source code, configuration files, and documentation in this repository are available under the [MIT License](LICENSE). You are free to use, modify, and distribute the code as long as you include the original copyright notice.
-- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate, Inc.** and are **not** covered by the MIT License.
-
-Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of our brand assets.
-
 ## Screenshots
 
 ### Live dashboard
@@ -67,7 +56,7 @@ Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of
 ### Built-in mask and zone editor
 
 <div>
-<img width="800" alt="Built-in mask and zone editor" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
+<img width="800" alt="Multi-camera scrubbing" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
 ## Translations
@@ -77,7 +66,3 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
 <a href="https://hosted.weblate.org/engage/frigate-nvr/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/multi-auto.svg" alt="Translation status" />
 </a>
-
----
-
-**Copyright © 2026 Frigate, Inc.**

README_CN.md

@@ -1,31 +1,28 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/frigate.png">
 </p>
 
-# Frigate NVR™ - 一个具有实时目标检测的本地 NVR
+# Frigate - 一个具有实时目标检测的本地NVR
+
+[English](https://github.com/blakeblackshear/frigate) | \[简体中文\] 
 
 <a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
 </a>
 
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
+一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备AI物体检测功能。使用OpenCV和TensorFlow在本地为IP摄像头执行实时物体检测。
 
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-
-一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备 AI 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
-
-强烈推荐使用 GPU 或者 AI 加速器（例如[Google Coral 加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。
-
-- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与 Home Assistant 紧密集成
-- 设计上通过仅在必要时和必要地点寻找目标，最大限度地减少资源使用并最大化性能
+强烈推荐使用GPU或者AI加速器（例如[Google Coral加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)）。它们的性能甚至超过目前的顶级CPU，并且可以以极低的耗电实现更优的性能。
+- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与Home Assistant紧密集成
+- 设计上通过仅在必要时和必要地点寻找物体，最大限度地减少资源使用并最大化性能
 - 大量利用多进程处理，强调实时性而非处理每一帧
-- 使用非常低开销的画面变动检测（也叫运动检测）来确定运行目标检测的位置
-- 使用 TensorFlow 进行目标检测，并运行在单独的进程中以达到最大 FPS
-- 通过 MQTT 进行通信，便于集成到其他系统中
+- 使用非常低开销的运动检测来确定运行物体检测的位置
+- 使用TensorFlow进行物体检测，运行在单独的进程中以达到最大FPS
+- 通过MQTT进行通信，便于集成到其他系统中
 - 根据检测到的物体设置保留时间进行视频录制
-- 24/7 全天候录制
-- 通过 RTSP 重新流传输以减少摄像头的连接数
-- 支持 WebRTC 和 MSE，实现低延迟的实时观看
+- 24/7全天候录制
+- 通过RTSP重新流传输以减少摄像头的连接数
+- 支持WebRTC和MSE，实现低延迟的实时观看
 
 ## 社区中文翻译文档
 
@@ -35,56 +32,39 @@
 
 如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
 
-## 协议
-
-本项目采用 **MIT 许可证**授权。
-
-**代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
-
-**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate, Inc. 的商标**，**不在** MIT 许可证覆盖范围内。
-有关品牌资产的规范使用详情，请参阅我们的[《商标政策》](TRADEMARK.md)。
-
 ## 截图
 
 ### 实时监控面板
-
 <div>
 <img width="800" alt="实时监控面板" src="https://github.com/blakeblackshear/frigate/assets/569905/5e713cb9-9db5-41dc-947a-6937c3bc376e">
 </div>
 
 ### 简单的核查工作流程
-
 <div>
 <img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
 </div>
 
 ### 多摄像头可按时间轴查看
-
 <div>
 <img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
 </div>
 
 ### 内置遮罩和区域编辑器
-
 <div>
 <img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
-## 翻译
 
+## 翻译
 我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
 
-## 非官方中文讨论社区
 
-欢迎加入中文讨论 QQ 群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
+## 非官方中文讨论社区
+欢迎加入中文讨论QQ群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
 
 Bilibili：https://space.bilibili.com/3546894915602564
 
-## 中文社区赞助商
 
+## 中文社区赞助商
 [![EdgeOne](https://edgeone.ai/media/34fe3a45-492d-4ea4-ae5d-ea1087ca7b4b.png)](https://edgeone.ai/zh?from=github)
 本项目 CDN 加速及安全防护由 Tencent EdgeOne 赞助
-
----
-
-**Copyright © 2026 Frigate, Inc.**

TRADEMARK.md

@@ -1,58 +0,0 @@
-# Trademark Policy
-
-**Last Updated:** November 2025
-
-This document outlines the policy regarding the use of the trademarks associated with the Frigate NVR project.
-
-## 1. Our Trademarks
-
-The following terms and visual assets are trademarks (the "Marks") of **Frigate, Inc.**:
-
-- **Frigate™**
-- **Frigate NVR™**
-- **Frigate+™**
-- **The Frigate Logo**
-
-**Note on Common Law Rights:**
-Frigate, Inc. asserts all common law rights in these Marks. The absence of a federal registration symbol (®) does not constitute a waiver of our intellectual property rights.
-
-## 2. Interaction with the MIT License
-
-The software in this repository is licensed under the [MIT License](LICENSE).
-
-**Crucial Distinction:**
-
-- The **Code** is free to use, modify, and distribute under the MIT terms.
-- The **Brand (Trademarks)** is **NOT** licensed under MIT.
-
-You may not use the Marks in any way that is not explicitly permitted by this policy or by written agreement with Frigate, Inc.
-
-## 3. Acceptable Use
-
-You may use the Marks without prior written permission in the following specific contexts:
-
-- **Referential Use:** To truthfully refer to the software (e.g., _"I use Frigate NVR for my home security"_).
-- **Compatibility:** To indicate that your product or project works with the software (e.g., _"MyPlugin for Frigate NVR"_ or _"Compatible with Frigate"_).
-- **Commentary:** In news articles, blog posts, or tutorials discussing the software.
-
-## 4. Prohibited Use
-
-You may **NOT** use the Marks in the following ways:
-
-- **Commercial Products:** You may not use "Frigate" in the name of a commercial product, service, or app (e.g., selling an app named _"Frigate Viewer"_ is prohibited).
-- **Implying Affiliation:** You may not use the Marks in a way that suggests your project is official, sponsored by, or endorsed by Frigate, Inc.
-- **Confusing Forks:** If you fork this repository to create a derivative work, you **must** remove the Frigate logo and rename your project to avoid user confusion. You cannot distribute a modified version of the software under the name "Frigate".
-- **Domain Names:** You may not register domain names containing "Frigate" that are likely to confuse users (e.g., `frigate-official-support.com`).
-
-## 5. The Logo
-
-The Frigate logo (the bird icon) is a visual trademark.
-
-- You generally **cannot** use the logo on your own website or product packaging without permission.
-- If you are building a dashboard or integration that interfaces with Frigate, you may use the logo only to represent the Frigate node/service, provided it does not imply you _are_ Frigate.
-
-## 6. Questions & Permissions
-
-If you are unsure if your intended use violates this policy, or if you wish to request a specific license to use the Marks (e.g., for a partnership), please contact us at:
-
-**help@frigate.video**

docker-compose.yml

@@ -14,8 +14,6 @@ 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:

docker/hailo8l/user_installation.sh

@@ -2,19 +2,15 @@
 
 # Update package list and install dependencies
 sudo apt-get update
-sudo apt-get install -y build-essential cmake git wget linux-headers-$(uname -r)
+sudo apt-get install -y build-essential cmake git wget
 
 hailo_version="4.21.0"
 arch=$(uname -m)
 
-if [[ $arch == "aarch64" ]]; then
-    source /etc/os-release
-    os_codename=$VERSION_CODENAME
-    echo "Detected OS codename: $os_codename"
-fi
-
-if [ "$os_codename" = "trixie" ]; then
-    sudo apt install -y dkms
+if [[ $arch == "x86_64" ]]; then
+    sudo apt install -y linux-headers-$(uname -r);
+else
+    sudo apt install -y linux-modules-extra-$(uname -r);
 fi
 
 # Clone the HailoRT driver repository
@@ -51,4 +47,3 @@ sudo udevadm control --reload-rules && sudo udevadm trigger
 
 echo "HailoRT driver installation complete."
 echo "reboot your system to load the firmware!"
-echo "Driver version: $(modinfo -F version hailo_pci)"

docker/main/Dockerfile

@@ -52,18 +52,10 @@ 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/v1.9.13/go2rtc_linux_${TARGETARCH}" go2rtc
+ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.10/go2rtc_linux_${TARGETARCH}" go2rtc
 
 FROM wget AS tempio
 ARG TARGETARCH
@@ -208,7 +200,6 @@ 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/ /
@@ -246,18 +237,8 @@ ENV PYTHONWARNINGS="ignore:::numpy.core.getlimits"
 # Set HailoRT to disable logging
 ENV HAILORT_LOGGER_PATH=NONE
 
-# TensorFlow C++ logging suppression (must be set before import)
-# TF_CPP_MIN_LOG_LEVEL: 0=all, 1=INFO+, 2=WARNING+, 3=ERROR+ (we use 3 for errors only)
+# TensorFlow error only
 ENV TF_CPP_MIN_LOG_LEVEL=3
-# Suppress verbose logging from TensorFlow C++ code
-ENV TF_CPP_MIN_VLOG_LEVEL=3
-# Disable oneDNN optimization messages ("optimized with oneDNN...")
-ENV TF_ENABLE_ONEDNN_OPTS=0
-# Suppress AutoGraph verbosity during conversion
-ENV AUTOGRAPH_VERBOSITY=0
-# Google Logging (GLOG) suppression for TensorFlow components
-ENV GLOG_minloglevel=3
-ENV GLOG_logtostderr=0
 
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
@@ -275,12 +256,6 @@ 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"

docker/main/build_intel_media_driver.sh

@@ -1,48 +0,0 @@
-#!/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

docker/main/build_nginx.sh

@@ -73,7 +73,6 @@ 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 \

docker/main/install_deps.sh

@@ -52,7 +52,7 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
     rm -rf ffmpeg.tar.xz
     mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linux64-gpl-7.1.tar.xz"
+    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2024-09-19-12-51/ffmpeg-n7.0.2-18-g3e6cec1286-linux64-gpl-7.0.tar.xz"
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/7.0 --strip-components 1 amd64/bin/ffmpeg amd64/bin/ffprobe
     rm -rf ffmpeg.tar.xz
 fi
@@ -64,7 +64,7 @@ if [[ "${TARGETARCH}" == "arm64" ]]; then
     tar -xf ffmpeg.tar.xz -C /usr/lib/ffmpeg/5.0 --strip-components 1 arm64/bin/ffmpeg arm64/bin/ffprobe
     rm -f ffmpeg.tar.xz
     mkdir -p /usr/lib/ffmpeg/7.0
-    wget -qO ffmpeg.tar.xz "https://github.com/NickM-27/FFmpeg-Builds/releases/download/autobuild-2026-03-19-13-03/ffmpeg-n7.1.3-43-g5a1f107b4c-linuxarm64-gpl-7.1.tar.xz"
+    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
 fi
@@ -87,60 +87,46 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     # intel packages use zst compression so we need to update dpkg
     apt-get install -y dpkg
 
-    # use intel apt repo for libmfx1 (legacy QSV, pre-Gen12)
+    # use intel apt intel packages
     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
-
-    # 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
+        intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2
 
-    # 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
 
-    # install legacy and standard intel compute packages
+    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
     # 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/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
+    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
 
-    # legacy compute-runtime packages
+    # legacy 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 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
+    # 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
     # npu packages
-    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
+    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
 
     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
@@ -159,6 +145,6 @@ rm -rf /var/lib/apt/lists/*
 
 # Install yq, for frigate-prepare and go2rtc echo source
 curl -fsSL \
-    "https://github.com/mikefarah/yq/releases/download/v4.48.2/yq_linux_$(dpkg --print-architecture)" \
+    "https://github.com/mikefarah/yq/releases/download/v4.33.3/yq_linux_$(dpkg --print-architecture)" \
     --output /usr/local/bin/yq
 chmod +x /usr/local/bin/yq

docker/main/requirements-wheels.txt

@@ -21,7 +21,7 @@ onvif-zeep-async == 4.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
-peewee_migrate == 1.14.*
+peewee_migrate == 1.13.*
 psutil == 7.1.*
 pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
@@ -42,13 +42,13 @@ opencv-python-headless == 4.11.0.*
 opencv-contrib-python == 4.11.0.*
 scipy == 1.16.*
 # OpenVino & ONNX
-openvino == 2025.4.*
+openvino == 2025.3.*
 onnxruntime == 1.22.*
 # Embeddings
 transformers == 4.45.*
 # Generative AI
-google-genai == 1.58.*
-ollama == 0.6.*
+google-generativeai == 0.8.*
+ollama == 0.5.*
 openai == 1.65.*
 # push notifications
 py-vapid == 1.9.*
@@ -81,5 +81,3 @@ librosa==0.11.*
 soundfile==0.13.*
 # DeGirum detector
 degirum == 0.16.*
-# Memory profiling
-memray == 1.15.*

docker/main/rootfs/etc/s6-overlay/s6-rc.d/certsync/run

@@ -10,8 +10,7 @@ echo "[INFO] Starting certsync..."
 
 lefile="/etc/letsencrypt/live/frigate/fullchain.pem"
 
-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`
+tls_enabled=`python3 /usr/local/nginx/get_listen_settings.py | jq -r .tls.enabled`
 
 while true
 do
@@ -35,7 +34,7 @@ do
             ;;
     esac
 
-    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'`
+    liveprint=`echo | openssl s_client -showcerts -connect 127.0.0.1:8971 2>&1 | openssl x509 -fingerprint 2>&1 | grep -i fingerprint  || echo 'failed'`
 
     case "$liveprint" in
         *Fingerprint*)
@@ -56,4 +55,4 @@ do
 
 done
 
-exit 0
+exit 0

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -54,8 +54,8 @@ function setup_homekit_config() {
     local config_path="$1"
 
     if [[ ! -f "${config_path}" ]]; then
-        echo "[INFO] Creating empty config file for HomeKit..."
-        : > "${config_path}"
+        echo "[INFO] Creating empty HomeKit config file..."
+        echo '{}' > "${config_path}"
     fi
 
     # Convert YAML to JSON for jq processing
@@ -65,25 +65,21 @@ function setup_homekit_config() {
         return 0
     }
 
-    # Use jq to extract the homekit section, if it exists
-    local homekit_json
-    homekit_json=$(jq '
-        if has("homekit") then {homekit: .homekit} else null end
-    ' "${temp_json}" 2>/dev/null) || homekit_json="null"
+    # Use jq to filter and keep only the homekit section
+    local cleaned_json="/tmp/cache/homekit_cleaned.json"
+    jq '
+        # Keep only the homekit section if it exists, otherwise empty object
+        if has("homekit") then {homekit: .homekit} else {homekit: {}} end
+    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || echo '{"homekit": {}}' > "${cleaned_json}"
 
-    # If no homekit section, write an empty config file
-    if [[ "${homekit_json}" == "null" ]]; then
-        : > "${config_path}"
-    else
-        # Convert homekit JSON back to YAML and write to the config file
-        echo "${homekit_json}" | yq eval -P - > "${config_path}" 2>/dev/null || {
-            echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
-            : > "${config_path}"
-        }
-    fi
+    # Convert back to YAML and write to the config file
+    yq eval -P "${cleaned_json}" > "${config_path}" 2>/dev/null || {
+        echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
+        echo '{"homekit": {}}' > "${config_path}"
+    }
 
     # Clean up temp files
-    rm -f "${temp_json}"
+    rm -f "${temp_json}" "${cleaned_json}"
 }
 
 set_libva_version

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_nginx_settings.py | \
+python3 /usr/local/nginx/get_base_path.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 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
+# 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
 
 # Replace the bash process with the NGINX process, redirecting stderr to stdout
 exec 2>&1

docker/main/rootfs/usr/local/go2rtc/create_config.py

@@ -9,7 +9,6 @@ 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,
@@ -23,31 +22,14 @@ sys.path.remove("/opt/frigate")
 
 yaml = YAML()
 
-# Check if arbitrary exec sources are allowed (defaults to False for security)
-allow_arbitrary_exec = None
-if "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.environ:
-    allow_arbitrary_exec = os.environ.get("GO2RTC_ALLOW_ARBITRARY_EXEC")
-elif (
-    os.path.isdir("/run/secrets")
-    and os.access("/run/secrets", os.R_OK)
-    and "GO2RTC_ALLOW_ARBITRARY_EXEC" in os.listdir("/run/secrets")
-):
-    allow_arbitrary_exec = (
-        Path(os.path.join("/run/secrets", "GO2RTC_ALLOW_ARBITRARY_EXEC"))
-        .read_text()
-        .strip()
-    )
-# check for the add-on options file
-elif os.path.isfile("/data/options.json"):
-    with open("/data/options.json") as f:
-        raw_options = f.read()
-    options = json.loads(raw_options)
-    allow_arbitrary_exec = options.get("go2rtc_allow_arbitrary_exec")
-
-ALLOW_ARBITRARY_EXEC = allow_arbitrary_exec is not None and str(
-    allow_arbitrary_exec
-).lower() in ("true", "1", "yes")
-
+FRIGATE_ENV_VARS = {k: v for k, v in os.environ.items() if k.startswith("FRIGATE_")}
+# read docker secret files as env vars too
+if os.path.isdir("/run/secrets"):
+    for secret_file in os.listdir("/run/secrets"):
+        if secret_file.startswith("FRIGATE_"):
+            FRIGATE_ENV_VARS[secret_file] = (
+                Path(os.path.join("/run/secrets", secret_file)).read_text().strip()
+            )
 
 config_file = find_config_file()
 
@@ -96,13 +78,13 @@ 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"] = substitute_frigate_vars(
-        go2rtc_config["rtsp"]["username"]
+    go2rtc_config["rtsp"]["username"] = go2rtc_config["rtsp"]["username"].format(
+        **FRIGATE_ENV_VARS
     )
 
 if go2rtc_config.get("rtsp", {}).get("password") is not None:
-    go2rtc_config["rtsp"]["password"] = substitute_frigate_vars(
-        go2rtc_config["rtsp"]["password"]
+    go2rtc_config["rtsp"]["password"] = go2rtc_config["rtsp"]["password"].format(
+        **FRIGATE_ENV_VARS
     )
 
 # ensure ffmpeg path is set correctly
@@ -127,26 +109,14 @@ if LIBAVFORMAT_VERSION_MAJOR < 59:
     elif go2rtc_config["ffmpeg"].get("rtsp") is None:
         go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args
 
-
-def is_restricted_source(stream_source: str) -> bool:
-    """Check if a stream source is restricted (echo, expr, or exec)."""
-    return stream_source.strip().startswith(("echo:", "expr:", "exec:"))
-
-
-for name in list(go2rtc_config.get("streams", {})):
+for name in go2rtc_config.get("streams", {}):
     stream = go2rtc_config["streams"][name]
 
     if isinstance(stream, str):
         try:
-            formatted_stream = substitute_frigate_vars(stream)
-            if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
-                print(
-                    f"[ERROR] Stream '{name}' uses a restricted source (echo/expr/exec) which is disabled by default for security. "
-                    f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-                )
-                del go2rtc_config["streams"][name]
-                continue
-            go2rtc_config["streams"][name] = formatted_stream
+            go2rtc_config["streams"][name] = go2rtc_config["streams"][name].format(
+                **FRIGATE_ENV_VARS
+            )
         except KeyError as e:
             print(
                 "[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
@@ -154,33 +124,15 @@ for name in list(go2rtc_config.get("streams", {})):
             sys.exit(e)
 
     elif isinstance(stream, list):
-        filtered_streams = []
-        for i, stream_item in enumerate(stream):
+        for i, stream in enumerate(stream):
             try:
-                formatted_stream = substitute_frigate_vars(stream_item)
-                if not ALLOW_ARBITRARY_EXEC and is_restricted_source(formatted_stream):
-                    print(
-                        f"[ERROR] Stream '{name}' item {i + 1} uses a restricted source (echo/expr/exec) which is disabled by default for security. "
-                        f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-                    )
-                    continue
-
-                filtered_streams.append(formatted_stream)
+                go2rtc_config["streams"][name][i] = stream.format(**FRIGATE_ENV_VARS)
             except KeyError as e:
                 print(
                     "[ERROR] Invalid substitution found, see https://docs.frigate.video/configuration/restream#advanced-restream-configurations for more info."
                 )
                 sys.exit(e)
 
-        if filtered_streams:
-            go2rtc_config["streams"][name] = filtered_streams
-        else:
-            print(
-                f"[ERROR] Stream '{name}' was removed because all sources were restricted (echo/expr/exec). "
-                f"Set GO2RTC_ALLOW_ARBITRARY_EXEC=true to enable arbitrary exec sources."
-            )
-            del go2rtc_config["streams"][name]
-
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, Any] = config.get("birdseye")

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -63,9 +63,6 @@ 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 '';
@@ -227,6 +224,16 @@ 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;

docker/main/rootfs/usr/local/nginx/conf/proxy_trusted_headers.conf

@@ -18,10 +18,6 @@ proxy_set_header X-Forwarded-User $http_x_forwarded_user;
 proxy_set_header X-Forwarded-Groups $http_x_forwarded_groups;
 proxy_set_header X-Forwarded-Email $http_x_forwarded_email;
 proxy_set_header X-Forwarded-Preferred-Username $http_x_forwarded_preferred_username;
-proxy_set_header X-Auth-Request-User $http_x_auth_request_user;
-proxy_set_header X-Auth-Request-Groups $http_x_auth_request_groups;
-proxy_set_header X-Auth-Request-Email $http_x_auth_request_email;
-proxy_set_header X-Auth-Request-Preferred-Username $http_x_auth_request_preferred_username;
 proxy_set_header X-authentik-username $http_x_authentik_username;
 proxy_set_header X-authentik-groups $http_x_authentik_groups;
 proxy_set_header X-authentik-email $http_x_authentik_email;

docker/main/rootfs/usr/local/nginx/get_base_path.py

@@ -0,0 +1,11 @@
+"""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))

docker/main/rootfs/usr/local/nginx/get_listen_settings.py

@@ -0,0 +1,35 @@
+"""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))

docker/main/rootfs/usr/local/nginx/get_nginx_settings.py

@@ -1,62 +0,0 @@
-"""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))

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:{{ .listen.external_port }};
+    proxy_pass $scheme://127.0.0.1:8971;
     proxy_http_version 1.1;
     proxy_set_header Upgrade $http_upgrade;
     proxy_set_header Connection "upgrade";

docker/main/rootfs/usr/local/nginx/templates/listen.gotmpl

@@ -1,36 +1,45 @@
+
 # Internal (IPv4 always; IPv6 optional)
-listen {{ .listen.internal }};
-{{ if .ipv6.enabled }}listen [::]:{{ .listen.internal_port }};{{ end }}
+listen 5000;
+{{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:5000;{{ end }}{{ end }}
+
 
 # intended for external traffic, protected by auth
-{{ if .tls.enabled }}
-    # external HTTPS (IPv4 always; IPv6 optional)
-    listen {{ .listen.external }} ssl;
-    {{ if .ipv6.enabled }}listen [::]:{{ .listen.external_port }} ssl;{{ end }}
+{{ if .tls }}
+    {{ if .tls.enabled }}
+        # external HTTPS (IPv4 always; IPv6 optional)
+        listen 8971 ssl;
+        {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971 ssl;{{ end }}{{ 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;
-    }
+        # 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 }}
 {{ else }}
-    # (No tls) default to HTTP (IPv4 always; IPv6 optional)
-    listen {{ .listen.external }};
-    {{ if .ipv6.enabled }}listen [::]:{{ .listen.external_port }};{{ end }}
+    # (No tls section) default to HTTP (IPv4 always; IPv6 optional)
+    listen 8971;
+    {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971;{{ end }}{{ end }}
 {{ end }}
+

docker/rocm/Dockerfile

@@ -3,6 +3,7 @@
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
 ARG ROCM=1
+ARG AMDGPU=gfx900
 ARG HSA_OVERRIDE_GFX_VERSION
 ARG HSA_OVERRIDE
 
@@ -10,10 +11,11 @@ ARG HSA_OVERRIDE
 FROM wget AS rocm
 
 ARG ROCM
+ARG AMDGPU
 
 RUN apt update -qq && \
     apt install -y wget gpg && \
-    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.2/ubuntu/jammy/amdgpu-install_7.2.70200-1_all.deb && \
+    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.0.2/ubuntu/jammy/amdgpu-install_7.0.2.70002-1_all.deb && \
     apt install -y ./rocm.deb && \
     apt update && \
     apt install -qq -y rocm
@@ -34,10 +36,7 @@ FROM deps AS deps-prelim
 COPY docker/rocm/debian-backports.sources /etc/apt/sources.list.d/debian-backports.sources
 RUN 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 && \
-    rm -rf /var/lib/apt/lists/*
+    apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers
 
 WORKDIR /opt/frigate
 COPY --from=rootfs / /
@@ -55,18 +54,12 @@ RUN pip3 uninstall -y onnxruntime \
 FROM scratch AS rocm-dist
 
 ARG ROCM
+ARG AMDGPU
 
-# 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-$ROCM/bin/
-# Copy MIOpen database files for gfx10xx, gfx11xx, and gfx12xx only (RDNA2/RDNA3/RDNA4)
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx10* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx11* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$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-$ROCM/lib/rocblas/library/
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share/miopen/db/
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
+COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
 
 #######################################################################

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +1 @@
-onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.2.0/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl
+onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.0.2/onnxruntime_migraphx-1.23.1-cp311-cp311-linux_x86_64.whl

docker/rocm/rocm.hcl

@@ -1,5 +1,8 @@
+variable "AMDGPU" {
+  default = "gfx900"
+}
 variable "ROCM" {
-  default = "7.2.0"
+  default = "7.0.2"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""
@@ -35,6 +38,7 @@ target rocm {
   }
   platforms = ["linux/amd64"]
   args = {
+    AMDGPU = AMDGPU,
     ROCM = ROCM,
     HSA_OVERRIDE_GFX_VERSION = HSA_OVERRIDE_GFX_VERSION,
     HSA_OVERRIDE = HSA_OVERRIDE

docker/rocm/rocm.mk

@@ -1,15 +1,53 @@
 BOARDS += rocm
 
+# AMD/ROCm is chunky so we build couple of smaller images for specific chipsets
+ROCM_CHIPSETS:=gfx900:9.0.0 gfx1030:10.3.0 gfx1100:11.0.0
+
 local-rocm: version
+	$(foreach chipset,$(ROCM_CHIPSETS), \
+		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
+		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
+		HSA_OVERRIDE=1 \
+		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
+			--set rocm.tags=frigate:latest-rocm-$(word 1,$(subst :, ,$(chipset))) \
+			--load \
+	&&) true
+	
+	unset HSA_OVERRIDE_GFX_VERSION && \
+	HSA_OVERRIDE=0 \
+	AMDGPU=gfx \
 	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
 		--set rocm.tags=frigate:latest-rocm \
 		--load
 
 build-rocm: version
+	$(foreach chipset,$(ROCM_CHIPSETS), \
+		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
+		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
+		HSA_OVERRIDE=1 \
+		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
+			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
+	&&) true
+
+	unset HSA_OVERRIDE_GFX_VERSION && \
+	HSA_OVERRIDE=0 \
+	AMDGPU=gfx \
 	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
 		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm
 
 push-rocm: build-rocm
+	$(foreach chipset,$(ROCM_CHIPSETS), \
+		AMDGPU=$(word 1,$(subst :, ,$(chipset))) \
+		HSA_OVERRIDE_GFX_VERSION=$(word 2,$(subst :, ,$(chipset))) \
+		HSA_OVERRIDE=1 \
+		docker buildx bake --file=docker/rocm/rocm.hcl rocm \
+			--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm-$(chipset) \
+			--push \
+	&&) true
+
+	unset HSA_OVERRIDE_GFX_VERSION && \
+	HSA_OVERRIDE=0 \
+	AMDGPU=gfx \
 	docker buildx bake --file=docker/rocm/rocm.hcl rocm \
 		--set rocm.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rocm \
 		--push

docker/tensorrt/requirements-amd64.txt

@@ -1,18 +1,18 @@
-# Nvidia ONNX Runtime GPU Support
+# NVidia TensorRT Support (amd64 only)
 --extra-index-url 'https://pypi.nvidia.com'
 cython==3.0.*; 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'
+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'
 onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.24.*; platform_machine == 'x86_64'
+onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
 protobuf==3.20.3; platform_machine == 'x86_64'

docs/.gitignore

@@ -7,7 +7,6 @@
 # Generated files
 .docusaurus
 .cache-loader
-docs/integrations/api/
 
 # Misc
 .DS_Store

docs/docs/configuration/advanced.md

@@ -4,29 +4,12 @@ title: Advanced Options
 sidebar_label: Advanced Options
 ---
 
-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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Logging" />.
-
-| Field                     | Description                                             |
-| ------------------------- | ------------------------------------------------------- |
-| **Logging level**         | The default log level for all modules (default: `info`) |
-| **Per-process log level** | Override the log level for specific modules             |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 logger:
   # Optional: default log level (default: shown below)
@@ -36,16 +19,13 @@ logger:
     frigate.mqtt: error
 ```
 
-</TabItem>
-</ConfigTabs>
-
 Available log levels are: `debug`, `info`, `warning`, `error`, `critical`
 
 Examples of available modules are:
 
 - `frigate.app`
 - `frigate.mqtt`
-- `frigate.object_detection.base`
+- `frigate.object_detection`
 - `detector.<detector_name>`
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
@@ -64,93 +44,28 @@ go2rtc:
 
 ### `environment_vars`
 
-This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS. Docker users should set environment variables in their `docker run` command (`-e FRIGATE_MQTT_PASSWORD=secret`) or `docker-compose.yml` file (`environment:` section) instead. Note that values set here are stored in plain text in your config file, so if the goal is to keep credentials out of your configuration, use Docker environment variables or Docker secrets instead.
+This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS.
 
-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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Environment variables" /> to add or edit environment variables.
-
-| Field     | Description                                               |
-| --------- | --------------------------------------------------------- |
-| **Key**   | 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.
-
-</TabItem>
-<TabItem value="yaml">
+Example:
 
 ```yaml
 environment_vars:
-  FRIGATE_MQTT_USER: my_mqtt_user
-  FRIGATE_MQTT_PASSWORD: my_mqtt_password
-
-mqtt:
-  host: "{FRIGATE_MQTT_HOST}"
-  user: "{FRIGATE_MQTT_USER}"
-  password: "{FRIGATE_MQTT_PASSWORD}"
+  VARIABLE_NAME: variable_value
 ```
 
-</TabItem>
-</ConfigTabs>
-
-#### TensorFlow Thread Configuration
-
-If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Environment variables" /> 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)      |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-environment_vars:
-  TF_INTRA_OP_PARALLELISM_THREADS: "2" # Threads within operations (0 = use default)
-  TF_INTER_OP_PARALLELISM_THREADS: "2" # Threads between operations (0 = use default)
-  TF_DATASET_THREAD_POOL_SIZE: "2" # Data pipeline threads (0 = use default)
-```
-
-</TabItem>
-</ConfigTabs>
-
 ### `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 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 in the config if necessary.
 
 This may need to be in a custom location if network storage is used for the media folder.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Database" />.
-
-- Set **Database path** to the custom path for the Frigate database file (default: `/config/frigate.db`)
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 database:
   path: /path/to/frigate.db
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### `model`
 
 If using a custom model, the width and height will need to be specified.
@@ -169,22 +84,6 @@ Custom models may also require different input tensor formats. The colorspace co
 |            "nhwc"             |
 |            "nchw"             |
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Detection model" /> 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` |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 # Optional: model config
 model:
@@ -195,9 +94,6 @@ model:
   input_pixel_format: "bgr"
 ```
 
-</TabItem>
-</ConfigTabs>
-
 #### `labelmap`
 
 :::warning
@@ -248,57 +144,33 @@ services:
 
 ### Enabling IPv6
 
-IPv6 is disabled by default. Enable it in the Frigate configuration.
+IPv6 is disabled by default, to enable IPv6 listen.gotmpl needs to be bind mounted with IPv6 enabled. For example:
 
-<ConfigTabs>
-<TabItem value="ui">
+```
+{{ if not .enabled }}
+# intended for external traffic, protected by auth
+listen 8971;
+{{ else }}
+# intended for external traffic, protected by auth
+listen 8971 ssl;
 
-Navigate to <NavPath path="Settings > System > Networking" /> and expand **IPv6 configuration**, then enable **Enable IPv6**.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-networking:
-  ipv6:
-    enabled: True
+# intended for internal traffic, not protected by auth
+listen 5000;
 ```
 
-</TabItem>
-</ConfigTabs>
+becomes
 
-### 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Networking" /> 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`)   |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-networking:
-  listen:
-    internal: 127.0.0.1:5000
-    external: 8971
 ```
+{{ 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;
 
-</TabItem>
-</ConfigTabs>
-
-:::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.
-
-:::
+# intended for internal traffic, not protected by auth
+listen [::]:5000 ipv6only=off;
+```
 
 ## Base path
 
@@ -351,7 +223,7 @@ To do this:
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.13, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.10, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 
@@ -375,7 +247,7 @@ curl -X POST http://frigate_host:5000/api/config/save -d @config.json
 if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
 
 ```bash
-yq -o=json '.' config.yaml | curl -X POST 'http://frigate_host:5000/api/config/save?save_option=saveonly' --data-binary @-
+yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
 ```
 
 ### Via Command Line

docs/docs/configuration/audio_detectors.md

@@ -3,10 +3,6 @@ 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
@@ -15,17 +11,7 @@ Audio events work by detecting a type of audio and creating an event, the event
 
 ### Enabling Audio Events
 
-Audio events can be enabled globally or for specific cameras.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-**Global:** Navigate to <NavPath path="Settings > Global configuration > Audio events" /> and set **Enable audio detection** to on.
-
-**Per-camera:** Navigate to <NavPath path="Settings > Camera configuration > Audio events" /> and set **Enable audio detection** to on for the desired camera.
-
-</TabItem>
-<TabItem value="yaml">
+Audio events can be enabled for all cameras or only for specific cameras.
 
 ```yaml
 
@@ -40,9 +26,6 @@ cameras:
       enabled: True # <- enable audio events for the front_camera
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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
@@ -51,14 +34,6 @@ The ffmpeg process for capturing audio will be a separate connection to the came
 
 :::
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add an input with the `audio` role pointing to a stream that includes audio.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   front_camera:
@@ -73,12 +48,9 @@ cameras:
             - detect
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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 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.
 
 :::tip
 
@@ -90,17 +62,6 @@ Volume is considered motion for recordings, this means when the `record -> retai
 
 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Audio events" />.
-
-- Set **Enable audio detection** to on
-- Set **Listen types** to include the audio types you want to detect
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 audio:
   enabled: True
@@ -112,38 +73,9 @@ audio:
     - yell
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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.
-
-:::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, configure it globally and optionally disable for specific cameras. Audio detection must also be enabled as described above.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-**Global:** Navigate to <NavPath path="Settings > Enrichments > Audio transcription" />.
-
-- Set **Enable audio transcription** to on
-- Set **Transcription device** to the desired device
-- Set **Model size** to the desired size
-
-**Per-camera:** Navigate to <NavPath path="Settings > Camera configuration > Audio transcription" /> to enable or disable transcription for a specific camera.
-
-</TabItem>
-<TabItem value="yaml">
+Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. 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.
 
 ```yaml
 audio_transcription:
@@ -162,9 +94,6 @@ cameras:
       enabled: False
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::note
 
 Audio detection must be enabled and configured as described above in order to use audio transcription features.
@@ -211,32 +140,8 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
 
 Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button 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. 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 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).
 
 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.
 
-:::note
-
-Only one `speech` event may be transcribed at a time. Frigate does not automatically transcribe `speech` events or implement a queue for long-running transcription model inference.
-
-:::
-
-Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
-
-#### FAQ
-
-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.
-
-   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.
-
-   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.
-
-   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.
+Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.

docs/docs/configuration/authentication.md

@@ -3,10 +3,6 @@ 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.
@@ -26,30 +22,13 @@ 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Authentication" />.
-
-- Set **Reset admin password** to on to reset the admin password and print it in the logs on next startup
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 auth:
   reset_admin_password: true
 ```
 
-</TabItem>
-</ConfigTabs>
-
-## 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).
-
 ## Login failure rate limiting
 
 In order to limit the risk of brute force attacks, rate limiting is available for login failures. This is implemented with SlowApi, and the string notation for valid values is available in [the documentation](https://limits.readthedocs.io/en/stable/quickstart.html#examples).
@@ -64,20 +43,7 @@ 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, configure rate limiting and trusted proxies as follows:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Authentication" />.
-
-| 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) |
-
-</TabItem>
-<TabItem value="yaml">
+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:
 
 ```yaml
 auth:
@@ -86,9 +52,6 @@ auth:
     - 172.18.0.0/16 # <---- this is the subnet for the internal Docker Compose network
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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.
@@ -100,24 +63,11 @@ 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Authentication" />.
-
-- Set **Session length** to the duration in seconds before the authentication session expires (default: 86400 / 24 hours)
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 auth:
   session_length: 86400
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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.
@@ -132,7 +82,7 @@ Frigate looks for a JWT token secret in the following order:
 
 1. An environment variable named `FRIGATE_JWT_SECRET`
 2. A file named `FRIGATE_JWT_SECRET` in the directory specified by the `CREDENTIALS_DIRECTORY` environment variable (defaults to the Docker Secrets directory: `/run/secrets/`)
-3. A `jwt_secret` option from the Home Assistant App options
+3. A `jwt_secret` option from the Home Assistant Add-on options
 4. A `.jwt_secret` file in the config directory
 
 If no secret is found on startup, Frigate generates one and stores it in a `.jwt_secret` file in the config directory.
@@ -145,18 +95,7 @@ Frigate can be configured to leverage features of common upstream authentication
 
 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.
 
-To disable Frigate's authentication and ensure requests come only from your known proxy:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > Authentication" />.
-   - Set **Enable authentication** to off
-2. Navigate to <NavPath path="Settings > System > Proxy" />.
-   - Set **Proxy secret** to `<some random long string>`
-
-</TabItem>
-<TabItem value="yaml">
+Here is an example of how to disable Frigate's authentication and also ensure the requests come only from your known proxy.
 
 ```yaml
 auth:
@@ -166,9 +105,6 @@ proxy:
   auth_secret: <some random long string>
 ```
 
-</TabItem>
-</ConfigTabs>
-
 You can use the following code to generate a random secret.
 
 ```shell
@@ -179,20 +115,6 @@ 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Proxy" /> 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`)                           |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 proxy:
   ...
@@ -202,37 +124,19 @@ proxy:
     role: x-forwarded-groups
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Proxy" /> and set the default role.
-
-| Field            | Description                                                   |
-| ---------------- | ------------------------------------------------------------- |
-| **Default role** | Fallback role when no role header is present (e.g., `viewer`) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 proxy:
   ...
   default_role: viewer
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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). This is configurable via YAML in the configuration file:
+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).
 
 ```yaml
 proxy:
@@ -258,16 +162,12 @@ In this example:
 - If no mapping matches, Frigate falls back to `default_role` if configured.
 - If `role_map` is not defined, Frigate assumes the role header directly contains `admin`, `viewer`, or a custom role name.
 
-**Note on matching semantics:**
-
-- 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).
-
 #### 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]`.
@@ -324,15 +224,7 @@ The viewer role provides read-only access to all cameras in the UI and API. Cust
 
 ### Role Configuration Example
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Users > Roles" /> to define custom roles and assign which cameras each role can access.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {11-16}
+```yaml
 cameras:
   front_door:
     # ... camera config
@@ -351,16 +243,13 @@ auth:
       - side_yard
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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
@@ -380,43 +269,4 @@ 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**.
-
-## API Authentication Guide
-
-### Getting a Bearer Token
-
-To use the Frigate API, you need to authenticate first. Follow these steps to obtain a Bearer token:
-
-#### 1. Login
-
-Make a POST request to `/login` with your credentials:
-
-```bash
-curl -i -X POST https://frigate_ip:8971/api/login \
-  -H "Content-Type: application/json" \
-  -d '{"user": "admin", "password": "your_password"}'
-```
-
-:::note
-
-You may need to include `-k` in the argument list in these steps (eg: `curl -k -i -X POST ...`) if your Frigate instance is using a self-signed certificate.
-
-:::
-
-The response will contain a cookie with the JWT token.
-
-#### 2. Using the Bearer Token
-
-Once you have the token, include it in the Authorization header for subsequent requests:
-
-```bash
-curl -H "Authorization: Bearer <your_token>" https://frigate_ip:8971/api/profile
-```
-
-#### 3. Token Lifecycle
-
-- Tokens are valid for the configured session length
-- Tokens are automatically refreshed when you visit the `/auth` endpoint
-- Tokens are invalidated when the user's password is changed
-- Use `/logout` to clear your session cookie
+3. Edit a user’s role by selecting **admin** or **viewer**.

docs/docs/configuration/autotracking.md

@@ -3,10 +3,6 @@ id: autotracking
 title: Camera Autotracking
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 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.
 
 ![Autotracking example with zooming](/img/frigate-autotracking-example.gif)
@@ -33,44 +29,12 @@ 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).
 
-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.
+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.
 
 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > ONVIF" /> 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)                                                                            |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   ptzcamera:
@@ -88,10 +52,6 @@ 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:
@@ -128,16 +88,13 @@ cameras:
         movement_weights: []
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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 `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 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.
 
 After calibration has ended, your PTZ will be moved to the preset specified by `return_preset`.
 

docs/docs/configuration/bird_classification.md

@@ -3,18 +3,8 @@ 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.
@@ -25,18 +15,7 @@ The classification model used is the MobileNet INat Bird Classification, [availa
 
 ## Configuration
 
-Bird classification is disabled by default and must be enabled before it can be used. Bird classification is a global configuration setting.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Object classification" />.
-
-- Set **Bird classification config > Bird classification** to on
-- Set **Bird classification config > Minimum score** to the desired confidence score (default: 0.9)
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 classification:
@@ -44,9 +23,6 @@ classification:
     enabled: true
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## Advanced Configuration
 
 Fine-tune bird classification with these optional parameters:

docs/docs/configuration/birdseye.md

@@ -1,9 +1,5 @@
 # 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.
@@ -26,24 +22,9 @@ A custom icon can be added to the birdseye background by providing a 180x180 ima
 
 ### Birdseye view override at camera level
 
-To include a camera in Birdseye view only for specific circumstances, or exclude it entirely, configure Birdseye at the 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-**Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
-
-**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> 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` |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {8-10,12-14}
+```yaml
 # Include all cameras by default in Birdseye view
 birdseye:
   enabled: True
@@ -60,54 +41,22 @@ cameras:
       enabled: False
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Birdseye Inactivity
 
-By default birdseye shows all cameras that have had the configured activity in the last 30 seconds. This threshold can be configured.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Birdseye" />.
-
-| Field | Description |
-|-------|-------------|
-| **Inactivity threshold** | Seconds of inactivity before a camera is hidden from Birdseye (default: 30) |
-
-</TabItem>
-<TabItem value="yaml">
+By default birdseye shows all cameras that have had the configured activity in the last 30 seconds, this can be configured:
 
 ```yaml
 birdseye:
   enabled: True
-  # highlight-next-line
   inactivity_threshold: 15
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Birdseye" />.
-
-| Field | Description |
-|-------|-------------|
-| **Width** | Birdseye output width in pixels (default: 1280) |
-| **Height** | Birdseye output height in pixels (default: 720) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 birdseye:
   enabled: True
@@ -115,20 +64,10 @@ birdseye:
   height: 720
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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 is set at the camera level.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> for each camera and set the **Position** field to control the display order.
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 # Include all cameras by default in Birdseye view
@@ -139,67 +78,34 @@ birdseye:
 cameras:
   front:
     birdseye:
-      # highlight-next-line
       order: 1
   back:
     birdseye:
-      # highlight-next-line
       order: 2
 ```
 
-</TabItem>
-</ConfigTabs>
-
 _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.
 
-<ConfigTabs>
-<TabItem value="ui">
+For example, this can be configured to only show the most recently active camera.
 
-Navigate to <NavPath path="Settings > System > Birdseye" />.
-
-| Field | Description |
-|-------|-------------|
-| **Layout > Max cameras** | Maximum number of cameras shown at once (e.g., `1` for only the most active camera) |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {3-4}
+```yaml
 birdseye:
   enabled: True
   layout:
     max_cameras: 1
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Birdseye" />.
-
-| Field | Description |
-|-------|-------------|
-| **Layout > Scaling factor** | Camera scaling factor between 1.0 and 5.0 (default: 2.0) |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {3-4}
+```yaml
 birdseye:
   enabled: True
   layout:
     scaling_factor: 3.0
 ```
-
-</TabItem>
-</ConfigTabs>

docs/docs/configuration/camera_specific.md

@@ -23,7 +23,6 @@ Some cameras support h265 with different formats, but Safari only supports the a
 cameras:
   h265_cam: # <------ Doesn't matter what the camera is called
     ffmpeg:
-      # highlight-next-line
       apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
 ```
 
@@ -31,7 +30,7 @@ cameras:
 
 Note that mjpeg cameras require encoding the video into h264 for recording, and restream roles. This will use significantly more CPU than if the cameras supported h264 feeds directly. It is recommended to use the restream role to create an h264 restream and then use that as the source for ffmpeg.
 
-```yaml {3,10}
+```yaml
 go2rtc:
   streams:
     mjpeg_cam: "ffmpeg:http://your_mjpeg_stream_url#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
@@ -97,7 +96,6 @@ This camera is H.265 only. To be able to play clips on some devices (like MacOs
 cameras:
   annkec800: # <------ Name the camera
     ffmpeg:
-      # highlight-next-line
       apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
       output_args:
         record: preset-record-generic-audio-aac
@@ -190,10 +188,10 @@ go2rtc:
     # example for connectin 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"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
     your_reolink_camera_twt_sub:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
     # example for connecting to a Reolink NVR
     your_reolink_camera_via_nvr:
       - "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
@@ -229,12 +227,6 @@ cameras:
 
 ### Unifi Protect Cameras
 
-:::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 protect cameras require the rtspx stream to be used with go2rtc.
 To utilize a Unifi protect camera, modify the rtsps link to begin with rtspx.
 Additionally, remove the "?enableSrtp" from the end of the Unifi link.
@@ -246,7 +238,7 @@ go2rtc:
       - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#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.
 
@@ -260,10 +252,6 @@ ffmpeg:
 
 TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
 
-### Wyze Wireless Cameras
-
-Some community members have found better performance on Wyze cameras by using an alternative firmware known as [Thingino](https://thingino.com/).
-
 ## USB Cameras (aka Webcams)
 
 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:
@@ -276,7 +264,7 @@ To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's
 
 - In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
 
-```yaml {4,11-12}
+```
 go2rtc:
   streams:
     usb_camera:

docs/docs/configuration/cameras.md

@@ -3,10 +3,6 @@ id: cameras
 title: Camera Configuration
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 ## 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.
@@ -21,25 +17,6 @@ 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)                          |
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-| Field             | Description                                                         |
-| ----------------- | ------------------------------------------------------------------- |
-| **Camera inputs** | List of input stream definitions (paths and roles) for this camera. |
-
-Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
-
-| 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. |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 mqtt:
   host: mqtt.server.com
@@ -59,18 +36,7 @@ cameras:
       height: 720 # <- optional, by default Frigate tries to automatically detect resolution
 ```
 
-</TabItem>
-</ConfigTabs>
-
-Additional cameras are simply added under the camera configuration section.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > Management" /> and use the add camera button to configure each additional camera.
-
-</TabItem>
-<TabItem value="yaml">
+Additional cameras are simply added to the config under the `cameras` entry.
 
 ```yaml
 mqtt: ...
@@ -80,9 +46,6 @@ cameras:
   side: ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::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.
@@ -101,21 +64,9 @@ Not every PTZ supports ONVIF, which is the standard protocol Frigate uses to com
 
 :::
 
-Configure the ONVIF connection for your camera to enable PTZ controls.
+Add the onvif section to your camera in your configuration file:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > ONVIF" /> 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`
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {4-8}
+```yaml
 cameras:
   back:
     ffmpeg: ...
@@ -126,25 +77,14 @@ cameras:
       password: password
 ```
 
-</TabItem>
-</ConfigTabs>
-
 If the ONVIF connection is successful, PTZ controls will be available in the camera's WebUI.
 
-:::note
-
-Some cameras use a separate ONVIF/service account that is distinct from the device administrator credentials. If ONVIF authentication fails with the admin account, try creating or using an ONVIF/service user in the camera's firmware. Refer to your camera manufacturer's documentation for more.
-
-:::
-
 :::tip
 
 If your ONVIF camera does not require authentication credentials, you may still need to specify an empty string for `user` and `password`, eg: `user: ""` and `password: ""`.
 
 :::
 
-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
@@ -154,19 +94,18 @@ This list of working and non-working PTZ cameras is based on user feedback. If y
 The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/) can provide a starting point to determine a camera's compatibility with Frigate's autotracking. Look to see if a camera lists `PTZRelative`, `PTZRelativePanTilt` and/or `PTZRelativeZoom`. These features are required for autotracking, but some cameras still fail to respond even if they claim support. If they are missing, autotracking will not work (though basic PTZ in the WebUI might). Avoid cameras with no database entry unless they are confirmed as working below.
 
 | Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                                                                                                                                             |
-| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                                                                                                                                    |
+| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- |
+| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                                                                                                                                   |
 | Amcrest ASH21                |      ✅      |      ❌      | ONVIF service port: 80                                                                                                                                                                                                                                            |
 | Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                                                                                                                                              |
 | Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                                                                                                                                              |
 | Annke CZ504                  |      ✅      |      ✅      | Annke support provide specific firmware ([V5.7.1 build 250227](https://github.com/pierrepinon/annke_cz504/raw/refs/heads/main/digicap_V5-7-1_build_250227.dav)) to fix issue with ONVIF "TranslationSpaceFov"                                                     |
-| Axis Q-6155E                 |      ✅      |      ❌      | ONVIF service port: 80; Camera does not support MoveStatus.                                                                                                                                                                                                       |
 | Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Dahua                        |      ✅      |      ✅      | Some low-end Dahuas (lite series, picoo series (commonly), among others) have been reported to not support autotracking. These models usually don't have a four digit model number with chassis prefix and options postfix (e.g. DH-P5AE-PV vs DH-SD49825GB-HNR). |
 | Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Dahua DH-SD49825GB-HNR       |      ✅      |      ✅      |                                                                                                                                                                                                                                                                   |
 | Dahua DH-P5AE-PV             |      ❌      |      ❌      |                                                                                                                                                                                                                                                                   |
-| Foscam                       |      ✅      |      ❌      | In general support PTZ, but not relative move. There are no official ONVIF certifications and tests available on the ONVIF Conformant Products Database                                                                                                           |
+| Foscam                       |      ✅      |      ❌      | In general support PTZ, but not relative move. There are no official ONVIF certifications and tests available on the ONVIF Conformant Products Database                                                                                                           |     |
 | Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Foscam SD4                   |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
 | Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
@@ -182,15 +121,13 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
 
 ## Setting up camera groups
 
-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.
+:::tip
 
-<ConfigTabs>
-<TabItem value="ui">
+It is recommended to set up camera groups using the UI.
 
-On the Live dashboard, press the **+** 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.
+:::
 
-</TabItem>
-<TabItem value="yaml">
+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:
@@ -202,9 +139,6 @@ camera_groups:
     order: 0
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## Two-Way Audio
 
 See the guide [here](/configuration/live/#two-way-talk)

docs/docs/configuration/custom_classification/object_classification.md

@@ -3,25 +3,13 @@ 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 a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
-
-:::
+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.
 
 ## Minimum System Requirements
 
-Object classification models are lightweight and run very fast on CPU.
+Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
-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.
+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.
 
 ## Classes
 
@@ -37,35 +25,21 @@ 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`.
 
 - **Attribute**:
-  - Added as metadata to the object, visible in the Tracked Object Details pane in Explore, `frigate/events` MQTT messages, and the HTTP API response as `<model_name>: <predicted_value>`.
+  - Added as metadata to the object (visible in /events): `<model_name>: <predicted_value>`.
   - Ideal when multiple attributes can coexist independently.
-  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not, and if they are wearing a yellow vest or not.
-
-:::note
-
-A tracked object can only have a single sub label. If you are using Triggers or Face Recognition and you configure an object classification model for `person` using the sub label type, your sub label may not be assigned correctly as it depends on which enrichment completes its analysis first. This could also occur with `car` objects that are assigned a sub label for a delivery carrier. Consider using the `attribute` type instead.
-
-:::
-
-## Assignment Requirements
-
-Sub labels and attributes are only assigned when both conditions are met:
-
-1. **Threshold**: Each classification attempt must have a confidence score that meets or exceeds the configured `threshold` (default: `0.8`).
-2. **Class Consensus**: After at least 3 classification attempts, 60% of attempts must agree on the same class label. If the consensus class is `none`, no assignment is made.
-
-This two-step verification prevents false positives by requiring consistent predictions across multiple frames before assigning a sub label or attribute.
+  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
 
 ## Example use cases
 
 ### 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.
 
@@ -78,27 +52,7 @@ 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. Specify which object labels should be classified.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-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.
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 classification:
@@ -110,79 +64,20 @@ classification:
         classification_type: sub_label # or: attribute
 ```
 
-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.
-
-</TabItem>
-</ConfigTabs>
-
 ## Training the model
 
-Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
+Creating and training the model is done within the Frigate UI using the `Classification` page.
 
-### Step 1: Name and Define
-
-Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Frigate will automatically include a `none` class for objects that don't fit any specific category.
-
-For example: To classify your two cats, create a model named "Our Cats" and create two classes, "Charlie" and "Leo". A third class, "none", will be created automatically for other neighborhood cats that are not your own.
-
-### Step 2: Assign Training Examples
-
-The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically.
+### Getting Started
 
 When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
 
-If examples for some of your classes do not appear in the grid, you can continue configuring the model without them. New images will begin to appear in the Recent Classifications view. When your missing classes are seen, classify them from this view and retrain your model.
+// TODO add this section once UI is implemented. Explain process of selecting objects and curating training examples.
 
 ### 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.
-- **Preprocessing**: Ensure examples reflect object crops similar to Frigate's boxes; keep the subject centered.
+- **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.
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.
-
-## 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Logging" />.
-
-- Set **Logging level** to `debug`
-- Set **Per-process log level > `frigate.data_processing.real_time.custom_classification`** to `debug` for verbose classification logging
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-logger:
-  default: info
-  logs:
-    # highlight-next-line
-    frigate.data_processing.real_time.custom_classification: debug
-```
-
-</TabItem>
-</ConfigTabs>
-
-The debug logs will show:
-
-- Classification probabilities for each attempt
-- Whether scores meet the threshold requirement
-- Consensus calculations and when assignments are made
-- Object classification history and weighted scores

docs/docs/configuration/custom_classification/state_classification.md

@@ -3,25 +3,13 @@ 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/<camera_name>/classification/<model_name>` MQTT topic and in Home Assistant sensors via the official Frigate integration.
-
-:::info
-
-Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
-
-:::
+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.
 
 ## Minimum System Requirements
 
-State classification models are lightweight and run very fast on CPU.
+State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
-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.
+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.
 
 ## Classes
 
@@ -43,25 +31,7 @@ For state classification:
 
 ## Configuration
 
-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`.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-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.
-
-</TabItem>
-<TabItem value="yaml">
+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`.
 
 ```yaml
 classification:
@@ -76,89 +46,17 @@ classification:
             crop: [0, 180, 220, 400]
 ```
 
-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.
-
-</TabItem>
-</ConfigTabs>
-
 ## Training the model
 
-Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
+Creating and training the model is done within the Frigate UI using the `Classification` page.
 
-### Step 1: Name and Define
+### Getting Started
 
-Enter a name for your model and define at least 2 classes (states) that represent mutually exclusive states. For example, `open` and `closed` for a door, or `on` and `off` for lights.
+When choosing a portion of the camera frame for state classification, it is important to make the crop tight around the area of interest to avoid extra signals unrelated to what is being classified.
 
-### Step 2: Select the Crop Area
-
-Choose one or more cameras and draw a rectangle over the area of interest for each camera. The crop should be tight around the region you want to classify to avoid extra signals unrelated to what is being classified. You can drag and resize the rectangle to adjust the crop area.
-
-### Step 3: Assign Training Examples
-
-The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state. It's not strictly required to select all images you see. If a state is missing from the samples, you can train it from the Recent tab later.
-
-Once some images are assigned, training will begin automatically.
+// TODO add this section once UI is implemented. Explain process of selecting a crop.
 
 ### 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.
-- **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
-
-To troubleshoot issues with state classification models, enable debug logging to see detailed information about classification attempts, scores, and state verification.
-
-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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Logging" />.
-
-- Set **Logging level** to `debug`
-- Set **Per-process log level > `frigate.data_processing.real_time.custom_classification`** to `debug` for verbose classification logging
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-logger:
-  default: info
-  logs:
-    # highlight-next-line
-    frigate.data_processing.real_time.custom_classification: debug
-```
-
-</TabItem>
-</ConfigTabs>
-
-The debug logs will show:
-
-- Classification probabilities for each attempt
-- Whether scores meet the threshold requirement
-- State verification progress (consecutive detections needed)
-- When state changes are published
-
-### Recent Classifications
-
-For state classification, images are only added to recent classifications under specific circumstances:
-
-- **First detection**: The first classification attempt for a camera is always saved
-- **State changes**: Images are saved when the detected state differs from the current verified state
-- **Pending verification**: Images are saved when there's a pending state change being verified (requires 3 consecutive identical states)
-- **Low confidence**: Images with scores below 100% are saved even if the state matches the current state (useful for training)
-
-Images are **not** saved when the state is stable (detected state matches current state) **and** the score is 100%. This prevents unnecessary storage of redundant high-confidence classifications.
+- **Data collection**: Use the model’s Recent Classifications tab to gather balanced examples across times of day and weather.

docs/docs/configuration/face_recognition.md

@@ -3,18 +3,8 @@ 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";
-
 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
@@ -42,109 +32,56 @@ All of these features run locally on your system.
 
 ## Minimum System Requirements
 
-A CPU with AVX + AVX2 instructions is required to run Face Recognition.
-
 The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
 
 The `large` model is optimized for accuracy, an integrated or discrete GPU / NPU is required. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
 
 ## Configuration
 
-Face recognition is disabled by default and must be enabled before it can be used. Face recognition is a global configuration setting.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
-
-- Set **Enable face recognition** to on
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 face_recognition:
   enabled: true
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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. 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 at the global level of your config. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
 
 ### Detection
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
-
-- **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.
+- `detection_threshold`: Face detection confidence score required before recognition runs:
   - Default: `0.7`
-- **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: `500` pixels
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-face_recognition:
-  enabled: true
-  detection_threshold: 0.7
-  min_area: 500
-```
-
-</TabItem>
-</ConfigTabs>
+  - 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.
 
 ### Recognition
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Face recognition" />.
-
-- **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.
+- `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.
   - 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). This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/).
-  - Default: `None`
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
+- `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/)
 
 ## Usage
 
 Follow these steps to begin:
 
-1. **Enable face recognition** in your configuration and restart Frigate.
+1. **Enable face recognition** in your configuration file 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.
@@ -206,14 +143,17 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
 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.
 
    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`.
    - 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_.
+
    - Make sure you have trained at least one face per the recommendations above.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 

docs/docs/configuration/ffmpeg_presets.md

@@ -3,10 +3,6 @@ id: ffmpeg_presets
 title: FFmpeg presets
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 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).
 
 ### Hwaccel Presets
@@ -25,31 +21,7 @@ See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more i
 | 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       |
-
-Select the appropriate hwaccel preset for your hardware.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to the appropriate preset for your hardware.
-2. To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and set **Hardware acceleration arguments** for that camera.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-ffmpeg:
-  hwaccel_args: preset-vaapi
-
-cameras:
-  front_door:
-    ffmpeg:
-      hwaccel_args: preset-nvidia
-```
-
-</TabItem>
-</ConfigTabs>
+| preset-rkmpp        | Rockchip MPP  | Use image with \*-rk suffix and privileged mode       |
 
 ### Input Args Presets
 
@@ -100,7 +72,7 @@ Output args presets help make the config more readable and handle use cases for
 
 | 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            | 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                                                                                                                                                               |

docs/docs/configuration/genai.md

@@ -0,0 +1,231 @@
+---
+id: genai
+title: Generative AI
+---
+
+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.
+
+## Configuration
+
+Generative AI can be enabled for all cameras or only for specific cameras. If GenAI is disabled for a camera, you can still manually generate descriptions for events using the HTTP API. 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.
+
+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_`.
+
+```yaml
+genai:
+  provider: gemini
+  api_key: "{FRIGATE_GEMINI_API_KEY}"
+  model: gemini-2.0-flash
+
+cameras:
+  front_camera:
+    genai:
+      enabled: True # <- enable GenAI for your front camera
+      use_snapshot: True
+      objects:
+        - person
+      required_zones:
+        - steps
+  indoor_camera:
+    objects:
+      genai:
+        enabled: False # <- disable GenAI for your indoor camera
+```
+
+By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or 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 can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
+
+## Ollama
+
+:::warning
+
+Using Ollama on CPU is not recommended, 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 provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). 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://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
+
+### 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). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+
+:::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.
+
+:::
+
+### Configuration
+
+```yaml
+genai:
+  provider: ollama
+  base_url: http://localhost:11434
+  model: qwen3-vl:4b
+```
+
+## Google Gemini
+
+Google Gemini has a free tier allowing [15 queries per minute](https://ai.google.dev/pricing) to the API, which is more than sufficient for standard Frigate usage.
+
+### 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
+
+To start using Gemini, you must first get an API key from [Google AI Studio](https://aistudio.google.com).
+
+1. Accept the Terms of Service
+2. Click "Get API Key" from the right hand navigation
+3. Click "Create API key in new project"
+4. Copy the API key for use in your config
+
+### Configuration
+
+```yaml
+genai:
+  provider: gemini
+  api_key: "{FRIGATE_GEMINI_API_KEY}"
+  model: gemini-2.0-flash
+```
+
+:::note
+
+To use a different Gemini-compatible API endpoint, set the `GEMINI_BASE_URL` environment variable to your provider's API URL.
+
+:::
+
+## 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.
+
+### 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
+
+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
+
+```yaml
+genai:
+  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.
+
+:::
+
+## Azure OpenAI
+
+Microsoft offers several vision models through Azure OpenAI. A subscription is required.
+
+### 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
+
+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
+
+```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}"
+```
+
+## 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.
+
+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.
+
+### Using GenAI for notifications
+
+Frigate provides an [MQTT topic](/integrations/mqtt), `frigate/tracked_object_update`, that is updated with a JSON payload containing `event_id` and `description` when your AI provider returns a description for a tracked object. This description could be used directly in notifications, such as sending alerts to your phone or making audio announcements. If additional details from the tracked object are needed, you can query the [HTTP API](/integrations/api/event-events-event-id-get) using the `event_id`, eg: `http://frigate_ip:5000/api/events/<event_id>`.
+
+If looking to get notifications earlier than when an object ceases to be tracked, an additional send trigger can be configured of `after_significant_updates`.
+
+```yaml
+genai:
+  send_triggers:
+    tracked_object_end: true # default
+    after_significant_updates: 3 # how many updates to a tracked object before we should send an image
+```
+
+## Custom Prompts
+
+Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
+
+```
+Analyze the sequence of images containing the {label}. Focus on the likely intent or behavior of the {label} based on its actions and movement, rather than describing its appearance or the surroundings. Consider what the {label} is doing, why, and what it might do next.
+```
+
+:::tip
+
+Prompts can use variable replacements `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
+
+:::
+
+You are also able to define custom prompts in your configuration.
+
+```yaml
+genai:
+  provider: ollama
+  base_url: http://localhost:11434
+  model: llava
+
+objects:
+  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
+  object_prompts:
+    person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
+    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.
+
+```yaml
+cameras:
+  front_door:
+    objects:
+      genai:
+        enabled: True
+        use_snapshot: True
+        prompt: "Analyze the {label} in these images from the {camera} security camera at the front door. Focus on the actions and potential intent of the {label}."
+        object_prompts:
+          person: "Examine the person in these images. What are they doing, and how might their actions suggest their purpose (e.g., delivering something, approaching, leaving)? If they are carrying or interacting with a package, include details about its source or destination."
+          cat: "Observe the cat in these images. Focus on its movement and intent (e.g., wandering, hunting, interacting with objects). If the cat is near the flower pots or engaging in any specific actions, mention it."
+        objects:
+          - person
+          - cat
+        required_zones:
+          - 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.
+
+- OpenAI - [ChatGPT](https://chatgpt.com)
+- Gemini - [Google AI Studio](https://aistudio.google.com)
+- Ollama - [Open WebUI](https://docs.openwebui.com/)

docs/docs/configuration/genai/config.md

@@ -3,37 +3,29 @@ 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";
-
 ## 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 4 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.
+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.
 
 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_`.
 
-## 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.
+## Ollama
 
 :::warning
 
-Running Generative AI models on CPU is not recommended, as high inference times make using Generative AI impractical.
+Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
 
 :::
 
-### Recommended Local Models
+[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). 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.
 
-You must use a vision-capable model with Frigate. The following models are recommended for local deployment:
+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.
 
-| Model         | Notes                                                                                                                                                                |
-| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl`    | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.5`     | 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.                                     |
-| `Intern3.5VL` | Relatively fast with good vision comprehension                                                                                                                       |
-| `gemma3`      | Slower model with good vision and temporal understanding                                                                                                             |
+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://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
+
+### 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.
 
 :::info
 
@@ -41,200 +33,49 @@ 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
-
-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.
-
-- **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.
-- **Reasoning / 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.
-
-Some models are labeled as **hybrid** (capable of both thinking and instruct tasks). In these cases, it is recommended to disable reasoning / thinking, which is generally model specific (see your models documentation).
-
-**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.
-
-### 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - 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
-   - Under **Provider Options**, set `context_size` to tell Frigate your context size so it can send the appropriate amount of information
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-genai:
-  provider: llamacpp
-  base_url: http://localhost:8080
-  model: your-model-name
-  provider_options:
-    context_size: 16000 # Tell Frigate your context size so it can send the appropriate amount of information.
-```
-
-</TabItem>
-</ConfigTabs>
-
-### 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.
 
 :::
 
-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.
+The following models are recommended:
 
-#### Configuration
+| 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                |
 
-<ConfigTabs>
-<TabItem value="ui">
+:::note
 
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - 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
+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.
 
-</TabItem>
-<TabItem value="yaml">
+:::
+
+### Configuration
 
 ```yaml
 genai:
   provider: ollama
   base_url: http://localhost:11434
-  model: qwen3-vl:4b
-  provider_options: # other Ollama client options can be defined
+  model: minicpm-v:8b
+  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
+        num_ctx: 8192  # make sure the context matches other services that are using ollama
 ```
 
-</TabItem>
-</ConfigTabs>
+## Google Gemini
 
-### OpenAI-Compatible
+Google Gemini has a free tier allowing [15 queries per minute](https://ai.google.dev/pricing) to the API, which is more than sufficient for standard Frigate usage.
 
-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.
+### Supported Models
 
-:::tip
+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). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
 
-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:
-  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
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - 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
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-genai:
-  provider: openai
-  base_url: http://your-server:port
-  api_key: your-api-key # May not be required for local servers
-  model: your-model-name
-```
-
-</TabItem>
-</ConfigTabs>
-
-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 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
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - Set **Provider** to `ollama`
-   - Set **Base URL** to your local Ollama address (e.g., `http://localhost:11434`)
-   - Set **Model** to the cloud model name
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-genai:
-  provider: ollama
-  base_url: http://localhost:11434
-  model: cloud-model-name
-```
-
-</TabItem>
-</ConfigTabs>
-
-### 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
-
-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).
 
@@ -243,69 +84,28 @@ 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
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - 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`)
-
-</TabItem>
-<TabItem value="yaml">
+### Configuration
 
 ```yaml
 genai:
   provider: gemini
   api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-2.5-flash
+  model: gemini-1.5-flash
 ```
 
-</TabItem>
-</ConfigTabs>
-
-:::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}
-genai:
-  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.
 
-#### 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).
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
 
-#### 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
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - 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`)
-
-</TabItem>
-<TabItem value="yaml">
+### Configuration
 
 ```yaml
 genai:
@@ -314,65 +114,29 @@ genai:
   model: gpt-4o
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::note
 
 To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
 
 :::
 
-:::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 {5,6}
-genai:
-  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).
+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). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
 
-#### 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).
+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 and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
 
-#### Configuration
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
-   - 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}`)
-
-</TabItem>
-<TabItem value="yaml">
+### Configuration
 
 ```yaml
 genai:
   provider: azure_openai
-  base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
-  model: gpt-5-mini
+  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
   api_key: "{FRIGATE_OPENAI_API_KEY}"
 ```
-
-</TabItem>
-</ConfigTabs>

docs/docs/configuration/genai/objects.md

@@ -3,10 +3,6 @@ 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.
@@ -15,13 +11,13 @@ 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/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt#frigatecamera_nameobject_descriptionsset).
+Generative AI object descriptions can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
 
 ## 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
 
@@ -37,52 +33,22 @@ Prompts can use variable replacements `{label}`, `{sub_label}`, and `{camera}` t
 
 :::
 
-You can define custom prompts at the global level and per-object type. To configure custom prompts:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Global configuration > Objects" />.
-   - 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
-
-</TabItem>
-<TabItem value="yaml">
+You are also able to define custom prompts in your configuration.
 
 ```yaml
 genai:
   provider: ollama
   base_url: http://localhost:11434
-  model: qwen3-vl:8b-instruct
+  model: llava
 
 objects:
-  genai:
     prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
     object_prompts:
       person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
       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."
 ```
 
-</TabItem>
-</ConfigTabs>
-
-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:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Objects" /> 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`)
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 cameras:
@@ -102,9 +68,6 @@ cameras:
           - steps
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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.

docs/docs/configuration/genai/review_summaries.md

@@ -3,15 +3,11 @@ 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_namereview_descriptionsset).
+Generative AI review summaries can also be toggled dynamically for a [camera via MQTT](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
 
 ## Review Summary Usage and Best Practices
 
@@ -20,112 +16,69 @@ Review summaries provide structured JSON responses that are saved for each revie
 ```
 - `title` (string): A concise, direct title that describes the purpose or overall action (e.g., "Person taking out trash", "Joe walking dog").
 - `scene` (string): A narrative description of what happens across the sequence from start to finish, including setting, detected objects, and their observable actions.
-- `shortSummary` (string): A brief 2-sentence summary of the scene, suitable for notifications. This is a condensed version of the scene description.
 - `confidence` (float): 0-1 confidence in the analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous.
 - `other_concerns` (list): List of user-defined concerns that may need additional investigation.
 - `potential_threat_level` (integer): 0, 1, or 2 as defined below.
 ```
 
-This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will automatically show the title and `shortSummary` when the data is available, while the full `scene` description is available in the UI for detailed review.
+This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will also automatically show the title and description when the data is available.
 
 ### Defining Typical Activity
 
 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:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Review" />.
-
-- Set **GenAI config > Activity context prompt** to your custom activity context text
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-review:
-  genai:
-    activity_context_prompt: |
-      ### Normal Activity Indicators (Level 0)
-      - Known/verified people in any zone at any time
-      ...
-```
-
-</TabItem>
-</ConfigTabs>
-
 <details>
   <summary>Default Activity Context Prompt</summary>
 
-```yaml
-review:
-  genai:
-    activity_context_prompt: |
-      ### Normal Activity Indicators (Level 0)
-      - Known/verified people in any zone at any time
-      - People with pets in residential areas
-      - Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
-      - Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
-      - Activity confined to public areas only (sidewalks, streets) without entering property at any time
+```
+### Normal Activity Indicators (Level 0)
+- Known/verified people in any zone at any time
+- People with pets in residential areas
+- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
+- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
+- Activity confined to public areas only (sidewalks, streets) without entering property at any time
 
-      ### Suspicious Activity Indicators (Level 1)
-      - **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
-      - **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
-      - Taking items that don't belong to them (packages, objects from porches/driveways)
-      - Climbing or jumping fences/barriers to access property
-      - Attempting to conceal actions or items from view
-      - Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
+### Suspicious Activity Indicators (Level 1)
+- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
+- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
+- Taking items that don't belong to them (packages, objects from porches/driveways)
+- Climbing or jumping fences/barriers to access property
+- Attempting to conceal actions or items from view
+- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
 
-      ### Critical Threat Indicators (Level 2)
-      - Holding break-in tools (crowbars, pry bars, bolt cutters)
-      - Weapons visible (guns, knives, bats used aggressively)
-      - Forced entry in progress
-      - Physical aggression or violence
-      - Active property damage or theft in progress
+### Critical Threat Indicators (Level 2)
+- Holding break-in tools (crowbars, pry bars, bolt cutters)
+- Weapons visible (guns, knives, bats used aggressively)
+- Forced entry in progress
+- Physical aggression or violence
+- Active property damage or theft in progress
 
-      ### Assessment Guidance
-      Evaluate in this order:
+### Assessment Guidance
+Evaluate in this order:
 
-      1. **If person is verified/known** → Level 0 regardless of time or activity
-      2. **If person is unidentified:**
-        - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
-        - Check actions: If testing doors/handles, taking items, climbing → Level 1
-        - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
-      3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
+1. **If person is verified/known** → Level 0 regardless of time or activity
+2. **If person is unidentified:**
+   - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
+   - Check actions: If testing doors/handles, taking items, climbing → Level 1
+   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
+3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
 
-      The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
+The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
 ```
 
 </details>
 
 ### 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, configure Frigate to extract frames directly from recordings at a higher resolution.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Review" />.
-
-- Set **GenAI config > Enable GenAI descriptions** to on
-- Set **GenAI config > Review image source** to `recordings` (default is `preview`)
-
-</TabItem>
-<TabItem value="yaml">
+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:
 
 ```yaml
 review:
   genai:
     enabled: true
-    # highlight-next-line
     image_source: recordings # Options: "preview" (default) or "recordings"
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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:
@@ -145,19 +98,9 @@ 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. Configure these concerns so that review summaries will make note of them if the activity requires additional review.
+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:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Review" />.
-
-- Set **GenAI config > Additional concerns** to a list of your concerns (e.g., `animals in the garden`)
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {4,5}
+```yaml
 review:
   genai:
     enabled: true
@@ -165,39 +108,6 @@ review:
       - animals in the garden
 ```
 
-</TabItem>
-</ConfigTabs>
-
-### Preferred Language
-
-By default, review summaries are generated in English. Configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Review" />.
-
-- Set **GenAI config > Preferred language** to the desired language (e.g., `Spanish`)
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {4}
-review:
-  genai:
-    enabled: true
-    preferred_language: Spanish
-```
-
-</TabItem>
-</ConfigTabs>
-
 ## 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).
-
-### Requesting Reports Programmatically
-
-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.
+Along with individual review item summaries, Generative AI provides the ability to request a report of a given time period. For example, you can get a daily report while on a vacation of any suspicious activity or other concerns that may require review.

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -12,20 +12,23 @@ Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accel
 Object detection and enrichments (like Semantic Search, Face Recognition, and License Plate Recognition) are independent features. To use a GPU / NPU for object detection, see the [Object Detectors](/configuration/object_detectors.md) documentation. If you want to use your GPU for any supported enrichments, you must choose the appropriate Frigate Docker image for your GPU / NPU and configure the enrichment according to its specific documentation.
 
 - **AMD**
-  - ROCm support in the `-rocm` Frigate image is automatically detected for enrichments, but only some enrichment models are available due to ROCm's focus on LLMs and limited stability with certain neural network models. Frigate disables models that perform poorly or are unstable to ensure reliable operation, so only compatible enrichments may be active.
+
+  - ROCm will automatically be detected and used for enrichments in the `-rocm` Frigate image.
 
 - **Intel**
+
   - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
   - **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
 
 - **Nvidia**
+
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 
 - **RockChip**
   - RockChip NPU will automatically be detected and used for semantic search v1 and face recognition in the `-rk` Frigate image.
 
-Utilizing a GPU for enrichments does not require you to use the same GPU for object detection. For example, you can run the `tensorrt` Docker image to run enrichments on an Nvidia GPU and still use other dedicated hardware like a Coral or Hailo for object detection. However, one combination that is not supported is the `tensorrt` image for object detection on an Nvidia GPU and Intel iGPU for enrichments.
+Utilizing a GPU for enrichments does not require you to use the same GPU for object detection. For example, you can run the `tensorrt` Docker image for enrichments and still use other dedicated hardware like a Coral or Hailo for object detection. However, one combination that is not supported is TensorRT for object detection and OpenVINO for enrichments.
 
 :::note
 

docs/docs/configuration/hardware_acceleration_video.md

@@ -3,76 +3,84 @@ id: hardware_acceleration_video
 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
 
-It is highly recommended to use an integrated or discrete GPU for hardware acceleration video decoding in Frigate.
+It is highly recommended to use a GPU for hardware acceleration video decoding in Frigate. Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg.
 
-Some types of hardware acceleration are detected and used automatically, but you may need to update your configuration to enable hardware accelerated decoding in ffmpeg. To verify that hardware acceleration is working:
+Depending on your system, these parameters may not be compatible. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
 
-- 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
+## Raspberry Pi 3/4
 
-Frigate supports presets for optimal hardware accelerated video decoding:
+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 Add-on, you may need to use the full access variant and turn off _Protection mode_ for hardware acceleration.
 
-**AMD**
+```yaml
+# if you want to decode a h264 stream
+ffmpeg:
+  hwaccel_args: preset-rpi-64-h264
 
-- [AMD](#amd-based-cpus): Frigate can utilize modern AMD integrated GPUs and AMD discrete GPUs to accelerate video decoding.
+# if you want to decode a h265 (hevc) stream
+ffmpeg:
+  hwaccel_args: preset-rpi-64-h265
+```
 
-**Intel**
+:::note
 
-- [Intel](#intel-based-cpus): Frigate can utilize most Intel integrated GPUs and Arc GPUs to accelerate video decoding.
+If running Frigate through Docker, you either need to run in privileged mode or
+map the `/dev/video*` devices to Frigate. With Docker Compose add:
 
-**Nvidia GPU**
+```yaml
+services:
+  frigate:
+    ...
+    devices:
+      - /dev/video11:/dev/video11
+```
 
-- [Nvidia GPU](#nvidia-gpus): Frigate can utilize most modern Nvidia GPUs to accelerate video decoding.
+Or with `docker run`:
 
-**Raspberry Pi 3/4**
+```bash
+docker run -d \
+  --name frigate \
+  ...
+  --device /dev/video11 \
+  ghcr.io/blakeblackshear/frigate:stable
+```
 
-- [Raspberry Pi](#raspberry-pi-34): Frigate can utilize the media engine in the Raspberry Pi 3 and 4 to slightly accelerate video decoding.
+`/dev/video11` is the correct device (on Raspberry Pi 4B). You can check
+by running the following and looking for `H264`:
 
-**Nvidia Jetson** <CommunityBadge />
+```bash
+for d in /dev/video*; do
+  echo -e "---\n$d"
+  v4l2-ctl --list-formats-ext -d $d
+done
+```
 
-- [Jetson](#nvidia-jetson): Frigate can utilize the media engine in Jetson hardware to accelerate video decoding.
-
-**Rockchip** <CommunityBadge />
-
-- [RKNN](#rockchip-platform): Frigate can utilize the media engine in RockChip SOCs to accelerate video decoding.
-
-**Other Hardware**
-
-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
+Or map in all the `/dev/video*` devices.
 
 :::
 
 ## 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 A-series | iHD / Xe     | preset-intel-qsv-\* |                                             |
-| Intel Arc B-series | iHD / Xe     | preset-intel-qsv-\* | Requires host kernel 6.12+                  |
+| CPU Generation | Intel Driver | Recommended Preset  | Notes                                |
+| -------------- | ------------ | ------------------- | ------------------------------------ |
+| gen1 - gen5    | i965         | preset-vaapi        | qsv is not supported                 |
+| 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-\* |                                      |
 
 :::
 
 :::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 Add-on users](advanced.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.
 
@@ -82,60 +90,27 @@ 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Via Quicksync
 
 #### H.264 streams
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.264)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 ffmpeg:
   hwaccel_args: preset-intel-qsv-h264
 ```
 
-</TabItem>
-</ConfigTabs>
-
 #### H.265 streams
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Intel QuickSync (H.265)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 ffmpeg:
   hwaccel_args: preset-intel-qsv-h265
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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:
@@ -154,13 +129,12 @@ services:
   frigate:
     ...
     image: ghcr.io/blakeblackshear/frigate:stable
-    # highlight-next-line
     privileged: true
 ```
 
 ##### Docker Run CLI - Privileged
 
-```bash {4}
+```bash
 docker run -d \
   --name frigate \
   ...
@@ -174,7 +148,7 @@ Only recent versions of Docker support the `CAP_PERFMON` capability. You can tes
 
 ##### Docker Compose - CAP_PERFMON
 
-```yaml {5,6}
+```yaml
 services:
   frigate:
     ...
@@ -185,7 +159,7 @@ services:
 
 ##### Docker Run CLI - CAP_PERFMON
 
-```bash {4}
+```bash
 docker run -d \
   --name frigate \
   ...
@@ -221,34 +195,21 @@ telemetry:
 
 If you are passing in a device path, make sure you've passed the device through to the container.
 
-## AMD-based CPUs
-
-Frigate can utilize modern AMD integrated GPUs and AMD GPUs to accelerate video decoding using VAAPI.
-
-### 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).
-
-### Via VAAPI
+## AMD/ATI GPUs (Radeon HD 2000 and newer GPUs) via libva-mesa-driver
 
 VAAPI supports automatic profile selection so it will work automatically with both H.264 and H.265 streams.
 
-<ConfigTabs>
-<TabItem value="ui">
+:::note
 
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
+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 Add-on users](advanced.md#environment_vars).
 
-</TabItem>
-<TabItem value="yaml">
+:::
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-vaapi
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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.
@@ -263,7 +224,7 @@ Additional configuration is needed for the Docker container to be able to access
 
 #### Docker Compose - Nvidia GPU
 
-```yaml {5-12}
+```yaml
 services:
   frigate:
     ...
@@ -280,7 +241,7 @@ services:
 
 #### Docker Run CLI - Nvidia GPU
 
-```bash {4}
+```bash
 docker run -d \
   --name frigate \
   ...
@@ -292,29 +253,18 @@ 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA GPU`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 ffmpeg:
   hwaccel_args: preset-nvidia
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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:
 
 :::note
 
-`nvidia-smi` will not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
+`nvidia-smi` may not show `ffmpeg` processes when run inside the container [due to docker limitations](https://github.com/NVIDIA/nvidia-docker/issues/179#issuecomment-645579458).
 
 :::
 
@@ -350,80 +300,18 @@ If you do not see these processes, check the `docker logs` for the container and
 
 These instructions were originally based on the [Jellyfin documentation](https://jellyfin.org/docs/general/administration/hardware-acceleration.html#nvidia-hardware-acceleration-on-docker-linux).
 
-## Raspberry Pi 3/4
-
-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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> 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 <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-# if you want to decode a h264 stream
-ffmpeg:
-  hwaccel_args: preset-rpi-64-h264
-
-# if you want to decode a h265 (hevc) stream
-ffmpeg:
-  hwaccel_args: preset-rpi-64-h265
-```
-
-</TabItem>
-</ConfigTabs>
-
-:::note
-
-If running Frigate through Docker, you either need to run in privileged mode or
-map the `/dev/video*` devices to Frigate. With Docker Compose add:
-
-```yaml {4-5}
-services:
-  frigate:
-    ...
-    devices:
-      - /dev/video11:/dev/video11
-```
-
-Or with `docker run`:
-
-```bash {4}
-docker run -d \
-  --name frigate \
-  ...
-  --device /dev/video11 \
-  ghcr.io/blakeblackshear/frigate:stable
-```
-
-`/dev/video11` is the correct device (on Raspberry Pi 4B). You can check
-by running the following and looking for `H264`:
-
-```bash
-for d in /dev/video*; do
-  echo -e "---\n$d"
-  v4l2-ctl --list-formats-ext -d $d
-done
-```
-
-Or map in all the `/dev/video*` devices.
-
-:::
-
 # Community Supported
 
-## NVIDIA Jetson
+## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano\*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
 
-A separate set of docker images is available for Jetson devices. They come with an `ffmpeg` build with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
+A separate set of docker images is available that is based on Jetpack/L4T. They come with an `ffmpeg` build
+with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 6.0+ use the `stable-tensorrt-jp6` tagged image. Note that the Orin Nano has no video encoder, so frigate will use software encoding on this platform, but the image will still allow hardware decoding and tensorrt object detection.
 
 You will need to use the image with the nvidia container runtime:
 
 ### Docker Run CLI - Jetson
 
-```bash {3}
+```bash
 docker run -d \
   ...
   --runtime nvidia
@@ -432,7 +320,7 @@ docker run -d \
 
 ### Docker Compose - Jetson
 
-```yaml {5}
+```yaml
 services:
   frigate:
     ...
@@ -475,22 +363,11 @@ 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`.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `NVIDIA Jetson (H.264)` (or `NVIDIA Jetson (H.265)` for HEVC streams). For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 ffmpeg:
   hwaccel_args: preset-jetson-h264
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
@@ -505,24 +382,13 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
 
 ### Configuration
 
-Set the FFmpeg hwaccel preset to enable hardware video processing.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Rockchip RKMPP`. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
+Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
 
 ```yaml
 ffmpeg:
   hwaccel_args: preset-rkmpp
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::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.
@@ -545,14 +411,14 @@ Restarting ffmpeg...
 
 you should try to uprade to FFmpeg 7. This can be done using this config option:
 
-```yaml
+```
 ffmpeg:
   path: "7.0"
 ```
 
 You can set this option globally to use FFmpeg 7 for all cameras or on camera level to use it only for specific cameras. Do not confuse this option with:
 
-```yaml
+```
 cameras:
   name:
     ffmpeg:
@@ -572,17 +438,9 @@ Make sure to follow the [Synaptics specific installation instructions](/frigate/
 
 ### Configuration
 
-Set the FFmpeg hwaccel args to enable hardware video processing.
+Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and configure the hardware acceleration args and input args manually for Synaptics hardware. For per-camera overrides, navigate to <NavPath path="Settings > Camera configuration > FFmpeg" />.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {2}
+```yaml
 ffmpeg:
   hwaccel_args: -c:v h264_v4l2m2m
   input_args: preset-rtsp-restream
@@ -590,9 +448,6 @@ output_args:
   record: preset-record-generic-audio-aac
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::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.

docs/docs/configuration/index.md

@@ -3,24 +3,13 @@ id: index
 title: Frigate Configuration
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
+For Home Assistant Add-on installations, the config file should be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate Add-on you are running. See the list of directories [here](#accessing-add-on-config-dir).
 
-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.
-
-It is recommended to start with a minimal configuration and add to it as described in [the getting started guide](../guides/getting_started.md).
-
-## Configuration File Location
-
-For users who prefer to edit the YAML configuration file directly:
-
-- **Home Assistant App:** `/addon_configs/<addon_directory>/config.yml` — see [directory list](#accessing-app-config-dir)
-- **All other installations:** Map to `/config/config.yml` inside the container
+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.
 
-A minimal starting configuration:
+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:
@@ -36,24 +25,24 @@ cameras:
             - detect
 ```
 
-## Accessing the Home Assistant App configuration directory {#accessing-app-config-dir}
+## Accessing the Home Assistant Add-on configuration directory {#accessing-add-on-config-dir}
 
-When running Frigate through the HA App, the Frigate `/config` directory is mapped to `/addon_configs/<addon_directory>` in the host, where `<addon_directory>` is specific to the variant of the Frigate App you are running.
+When running Frigate through the HA Add-on, the Frigate `/config` directory is mapped to `/addon_configs/<addon_directory>` in the host, where `<addon_directory>` is specific to the variant of the Frigate Add-on 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` |
+| Add-on 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.
+If for example you are running the standard Add-on variant and use the [VS Code Add-on](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.
+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 Add-on, 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
 
@@ -61,7 +50,6 @@ Frigate supports the use of environment variables starting with `FRIGATE_` **onl
 
 ```yaml
 mqtt:
-  host: "{FRIGATE_MQTT_HOST}"
   user: "{FRIGATE_MQTT_USER}"
   password: "{FRIGATE_MQTT_PASSWORD}"
 ```
@@ -72,7 +60,7 @@ mqtt:
 
 ```yaml
 onvif:
-  host: "192.168.1.12"
+  host: 10.0.10.10
   port: 8000
   user: "{FRIGATE_RTSP_USER}"
   password: "{FRIGATE_RTSP_PASSWORD}"
@@ -92,12 +80,12 @@ genai:
 
 ## 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](./reference.md) for detailed information about all config values.
+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
+### Raspberry Pi Home Assistant Add-on with USB Coral
 
 - Single camera with 720p, 5fps stream for detect
-- MQTT connected to the Home Assistant Mosquitto App
+- MQTT connected to the Home Assistant Mosquitto Add-on
 - 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
@@ -105,20 +93,6 @@ Here are some common starter configuration examples. These can be configured thr
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the MQTT connection to your Home Assistant Mosquitto broker
-2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `Raspberry Pi (H.264)`
-3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
-4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> 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 <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
-7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 mqtt:
   host: core-mosquitto
@@ -135,16 +109,15 @@ detectors:
 
 record:
   enabled: True
-  motion:
+  retain:
     days: 7
+    mode: motion
   alerts:
     retain:
       days: 30
-      mode: motion
   detections:
     retain:
       days: 30
-      mode: motion
 
 snapshots:
   enabled: True
@@ -164,19 +137,13 @@ cameras:
             - 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"
+        - 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
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Standalone Intel Mini PC with USB Coral
 
 - Single camera with 720p, 5fps stream for detect
-- MQTT disabled (not integrated with Home Assistant)
+- 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
@@ -184,20 +151,6 @@ cameras:
 - Save snapshots for 30 days
 - Motion mask for the camera timestamp
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > MQTT" /> and set **Enable MQTT** to off
-2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
-3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`
-4. Navigate to <NavPath path="Settings > Global configuration > Recording" /> 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 <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-6. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
-7. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 mqtt:
   enabled: False
@@ -212,16 +165,15 @@ detectors:
 
 record:
   enabled: True
-  motion:
+  retain:
     days: 7
+    mode: motion
   alerts:
     retain:
       days: 30
-      mode: motion
   detections:
     retain:
       days: 30
-      mode: motion
 
 snapshots:
   enabled: True
@@ -241,41 +193,20 @@ cameras:
             - 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"
+        - 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
 ```
 
-</TabItem>
-</ConfigTabs>
-
-### Home Assistant integrated Intel Mini PC with OpenVINO
+### 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
+- MQTT connected to same mqtt server as home assistant
 - VAAPI hardware acceleration for decoding video
-- OpenVINO detector
+- 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
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > MQTT" /> and configure the connection to your MQTT broker
-2. Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to `VAAPI (Intel/AMD GPU)`
-3. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `openvino` and **Device** `AUTO`
-4. Navigate to <NavPath path="Settings > System > Detection model" /> and configure the OpenVINO model path and settings
-5. Navigate to <NavPath path="Settings > Global configuration > Recording" /> 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 <NavPath path="Settings > Global configuration > Snapshots" /> and set **Enable snapshots** to on, **Snapshot retention > Default retention** to `30`
-7. Navigate to <NavPath path="Settings > Camera configuration > Management" /> and add your camera with the appropriate RTSP stream URL
-8. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> to add a motion mask for the camera timestamp
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 mqtt:
   host: 192.168.X.X # <---- same mqtt broker that home assistant uses
@@ -300,16 +231,15 @@ model:
 
 record:
   enabled: True
-  motion:
+  retain:
     days: 7
+    mode: motion
   alerts:
     retain:
       days: 30
-      mode: motion
   detections:
     retain:
       days: 30
-      mode: motion
 
 snapshots:
   enabled: True
@@ -329,11 +259,5 @@ cameras:
             - 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"
+        - 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
 ```
-
-</TabItem>
-</ConfigTabs>

docs/docs/configuration/license_plate_recognition.md

@@ -3,20 +3,10 @@ id: license_plate_recognition
 title: License Plate Recognition (LPR)
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 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.
 
 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.
@@ -40,41 +30,20 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
 
 ## Minimum System Requirements
 
-License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM and a CPU with AVX + AVX2 instructions is required.
+License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
 
 ## Configuration
 
-License plate recognition is disabled by default and must be enabled before it can be used.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-- Set **Enable LPR** to on
-
-</TabItem>
-<TabItem value="yaml">
+License plate recognition is disabled by default. Enable it in your config file:
 
 ```yaml
 lpr:
   enabled: True
 ```
 
-</TabItem>
-</ConfigTabs>
+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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {4,5}
+```yaml
 cameras:
   garage:
     ...
@@ -82,165 +51,81 @@ cameras:
       enabled: False
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
 
 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. 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 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`.
 
 ### Detection
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-- **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.
+- **`detection_threshold`**: License plate object detection confidence score required before recognition runs.
   - Default: `0.7`
-- **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`
-- **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.
+  - 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.
+  - Default: `CPU`
+  - This 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.
   - Default: `small`
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-lpr:
-  enabled: True
-  detection_threshold: 0.7
-  min_area: 1000
-  device: CPU
-  model_size: small
-```
-
-</TabItem>
-</ConfigTabs>
+  - 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.
 
 ### Recognition
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate 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**: 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.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-lpr:
-  enabled: True
-  recognition_threshold: 0.9
-  min_plate_length: 4
-  format: "^[A-Z]{2}[0-9]{2} [A-Z]{3}$"
-```
-
-</TabItem>
-</ConfigTabs>
+- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
+  - Default: `0.9`.
+- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label` to an object.
+  - Use this to filter out short, incomplete, or incorrect detections.
+- **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
+  - `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` 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.
 
 ### Matching
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-- **Known plates**: Assign custom `sub_label` values 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 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.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-lpr:
-  enabled: True
-  match_distance: 1
-  known_plates:
-    Wife's Car:
-      - "ABC-1234"
-    Johnny:
-      - "J*N-*234"
-```
-
-</TabItem>
-</ConfigTabs>
+- **`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`.
 
 ### Image Enhancement
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-- **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.
+- **`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.
   - Default: `0` (no enhancement)
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-lpr:
-  enabled: True
-  enhancement: 1
-```
-
-</TabItem>
-</ConfigTabs>
-
-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.
+  - 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).
 
 ### Normalization Rules
 
-<ConfigTabs>
-<TabItem value="ui">
+- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially. Each rule must have a `pattern` (which can be a string or a regex, prepended by `r`) 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').
 
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-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) |
-
-</TabItem>
-<TabItem value="yaml">
+These rules must be defined at the global level of your `lpr` config.
 
 ```yaml
 lpr:
   replace_rules:
-    - pattern: "[%#*?]" # Remove noise symbols
+    - pattern: r'[%#*?]' # Remove noise symbols
       replacement: ""
-    - pattern: "[= ]" # Normalize = or space to dash
+    - pattern: r'[= ]' # Normalize = or space to dash
       replacement: "-"
     - pattern: "O" # Swap 'O' to '0' (common OCR error)
       replacement: "0"
-    - pattern: "I" # Swap 'I' to '1'
+    - pattern: r'I' # Swap 'I' to '1'
       replacement: "1"
-    - pattern: '(\w{3})(\w{3})' # Split 6 chars into groups (e.g., ABC123 → ABC-123) - use single quotes to preserve backslashes
-      replacement: '\1-\2'
+    - pattern: r'(\w{3})(\w{3})' # Split 6 chars into groups (e.g., ABC123 → ABC-123)
+      replacement: r'\1-\2'
 ```
 
-</TabItem>
-</ConfigTabs>
-
-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.
@@ -248,50 +133,13 @@ These rules must be defined at the global level of your `lpr` config.
 
 ### Debugging
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-- **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 `<camera>/<event_id>`, and named based on the capture timestamp.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-lpr:
-  enabled: True
-  debug_save_plates: True
-```
-
-</TabItem>
-</ConfigTabs>
-
-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.
+- **`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 `<camera>/<event_id>`, 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.
 
 ## Configuration Examples
 
-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`.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-| Field                          | Description                                                                                           |
-| ------------------------------ | ----------------------------------------------------------------------------------------------------- |
-| **Enable LPR**                 | Set to on                                                                                             |
-| **Minimum plate area**         | Set to `1500` — ignore plates with an area (length x width) smaller than 1500 pixels                  |
-| **Min plate length**           | Set to `4` — 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)                                          |
-
-</TabItem>
-<TabItem value="yaml">
+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`.
 
 ```yaml
 lpr:
@@ -310,21 +158,28 @@ lpr:
       - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
 ```
 
-</TabItem>
-</ConfigTabs>
+```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:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   side_yard:
@@ -333,16 +188,13 @@ cameras:
     ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::
 
 ## 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, set `type: "lpr"` in the camera configuration.
+To mark a camera as a dedicated LPR camera, add `type: "lpr"` the camera configuration.
 
 :::note
 
@@ -358,55 +210,6 @@ 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:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
-
-Navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
-
-Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
-
-| 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 <NavPath path="Settings > Camera configuration > Objects" />.
-
-| Field                                          | Description         |
-| ---------------------------------------------- | ------------------- |
-| **Objects to track**                           | Add `license_plate` |
-| **Object filters > License Plate > Threshold** | Set to `0.7`        |
-
-Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
-
-| 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 <NavPath path="Settings > Camera configuration > Recording" />.
-
-| Field                | Description                                              |
-| -------------------- | -------------------------------------------------------- |
-| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
-
-Navigate to <NavPath path="Settings > Camera configuration > Snapshots" />.
-
-| Field                | Description |
-| -------------------- | ----------- |
-| **Enable snapshots** | Set to on   |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 # LPR global configuration
 lpr:
@@ -445,9 +248,6 @@ cameras:
           - license_plate
 ```
 
-</TabItem>
-</ConfigTabs>
-
 With this setup:
 
 - License plates are treated as normal objects in Frigate.
@@ -459,65 +259,10 @@ With this setup:
 
 ### 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:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> 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 <NavPath path="Settings > Camera configuration > License plate recognition" /> 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 <NavPath path="Settings > Camera configuration > FFmpeg" /> and add your camera streams.
-
-Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
-
-| Field                       | Description                                                                                                                  |
-| --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
-| **Enable object detection** | Set to off — disables 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 <NavPath path="Settings > Camera configuration > Objects" />.
-
-| Field                | Description                                                                            |
-| -------------------- | -------------------------------------------------------------------------------------- |
-| **Objects to track** | Set to an empty list — required when not using a Frigate+ model for dedicated LPR mode |
-
-Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
-
-| 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 <NavPath path="Settings > Camera configuration > Masks / Zones" /> and add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
-
-Navigate to <NavPath path="Settings > Camera configuration > Recording" />.
-
-| Field                | Description                                              |
-| -------------------- | -------------------------------------------------------- |
-| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
-
-Navigate to <NavPath path="Settings > Camera configuration > Review" />.
-
-| Field                                     | Description     |
-| ----------------------------------------- | --------------- |
-| **Detections config > Enable detections** | Set to on       |
-| **Detections config > Retain > Default**  | Set to `7` days |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 # LPR global configuration
 lpr:
@@ -554,9 +299,6 @@ cameras:
           default: 7
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
@@ -632,54 +374,32 @@ Use `match_distance` to allow small character mismatches. Alternatively, define
 
 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.
+1. Enable debug logs to see exactly what Frigate is doing.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
-
-- Set **Enable LPR** to on
-- Set **Device** to `CPU`
-- Set **Save debug plates** to on
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-lpr:
-  enabled: true
-  device: CPU
-  debug_save_plates: true
-```
-
-</TabItem>
-</ConfigTabs>
-
-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.
+   - 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.
 
      ```yaml
      logger:
        default: info
        logs:
-         # highlight-next-line
          frigate.data_processing.common.license_plate: debug
      ```
 
-3. Ensure your plates are being _detected_.
+2. 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.
    - 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.
 
    If you are **not** using a Frigate+ or `license_plate` detecting model:
+
    - Watch the debug logs for messages from the YOLOv9 plate detector.
    - 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_.
+3. Ensure the characters on detected plates are being _recognized_.
+
    - 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.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
@@ -702,6 +422,6 @@ If you are using a model that natively detects `license_plate`, add an _object m
 
 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?
+### I see "Error running ... model" in my logs. 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.

docs/docs/configuration/live.md

@@ -3,10 +3,6 @@ id: live
 title: Live View
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 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
@@ -19,13 +15,7 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
 | ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | jsmpeg | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | Resolution is configurable, but go2rtc is recommended if you want higher resolutions and better frame rates. jsmpeg is Frigate's default without go2rtc configured. |
 | 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.
-
-:::
+| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration, doesn't support h.265. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
 
 ### Camera Settings Recommendations
 
@@ -73,28 +63,21 @@ 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 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 `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.
 
 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 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.
+Configure the `streams` option with a "friendly name" for your stream followed by the go2rtc stream name.
 
-<ConfigTabs>
-<TabItem value="ui">
+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 <NavPath path="Settings > Camera configuration > Live playback" />, then select your camera.
-   - Under **Live stream names**, add entries mapping a friendly name to each go2rtc stream name (e.g., `Main Stream` mapped to `test_cam`, `Sub Stream` mapped to `test_cam_sub`).
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {3,6,8,25-29}
+```yaml
 go2rtc:
   streams:
     test_cam:
@@ -126,17 +109,14 @@ cameras:
         Special Stream: test_cam_another_sub
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### WebRTC extra configuration:
 
 WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
 
 - For external access, over the internet, setup your router to forward port `8555` to port `8555` on the Frigate device, for both TCP and UDP.
-- For internal/local access, unless you are running through the HA App, you will also need to set the WebRTC candidates list in the go2rtc config. For example, if `192.168.1.10` is the local IP of the device running Frigate:
+- For internal/local access, unless you are running through the HA Add-on, you will also need to set the WebRTC candidates list in the go2rtc config. For example, if `192.168.1.10` is the local IP of the device running Frigate:
 
-  ```yaml title="config.yml" {4-7}
+  ```yaml title="config.yml"
   go2rtc:
     streams:
       test_cam: ...
@@ -147,14 +127,13 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
   ```
 
 - For access through Tailscale, the Frigate system's Tailscale IP must be added as a WebRTC candidate. Tailscale IPs all start with `100.`, and are reserved within the `100.64.0.0/10` CIDR block.
-
-- Note that some browsers may not support H.265 (HEVC). You can check your browser's current version for H.265 compatibility [here](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness).
+- Note that WebRTC does not support H.265.
 
 :::tip
 
-This extra configuration may not be required if Frigate has been installed as a Home Assistant App, as Frigate uses the Supervisor's API to generate a WebRTC candidate.
+This extra configuration may not be required if Frigate has been installed as a Home Assistant Add-on, as Frigate uses the Supervisor's API to generate a WebRTC candidate.
 
-However, it is recommended if issues occur to define the candidates manually. You should do this if the Frigate App fails to generate a valid candidate. If an error occurs you will see some warnings like the below in the App logs page during the initialization:
+However, it is recommended if issues occur to define the candidates manually. You should do this if the Frigate Add-on fails to generate a valid candidate. If an error occurs you will see some warnings like the below in the Add-on logs page during the initialization:
 
 ```log
 [WARN] Failed to get IP address from supervisor
@@ -174,7 +153,7 @@ If not running in host mode, port 8555 will need to be mapped for the container:
 
 docker-compose.yml
 
-```yaml {4-6}
+```yaml
 services:
   frigate:
     ...
@@ -199,13 +178,11 @@ To use the Reolink Doorbell with two way talk, you should use the [recommended R
 
 As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
 
-To prevent go2rtc from blocking other applications from accessing your camera's two-way audio, you must configure your stream with `#backchannel=0`. See [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream) in the restream documentation.
-
 ### Streaming options on camera group dashboards
 
 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 streams configuration option (see _Setting Streams For Live UI_ above)
+- Stream selection using the `live -> 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.
@@ -223,40 +200,6 @@ Use a camera group if you want to change any of these settings from the defaults
 
 :::
 
-### jsmpeg Stream Quality
-
-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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Live playback" /> for global defaults, or <NavPath path="Settings > Camera configuration > Live playback" /> 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)                                   |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-# Global defaults
-live:
-  height: 720
-  quality: 8
-
-# Per-camera override
-cameras:
-  front_door:
-    live:
-      height: 480
-      quality: 4
-```
-
-</TabItem>
-</ConfigTabs>
-
 ### Disabling cameras
 
 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.
@@ -271,36 +214,6 @@ For restreamed cameras, go2rtc remains active but does not use system resources
 
 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.
 
-### Live player error messages
-
-When your browser runs into problems playing back your camera streams, it will log short error messages to the browser console. They indicate playback, codec, or network issues on the client/browser side, not something server side with Frigate itself. Below are the common messages you may see and simple actions you can take to try to resolve them.
-
-- **startup**
-  - What it means: The player failed to initialize or connect to the live stream (network or startup error).
-  - What to try: Reload the Live view or click _Reset_. Verify `go2rtc` is running and the camera stream is reachable. Try switching to a different stream from the Live UI dropdown (if available) or use a different browser.
-
-  - Possible console messages from the player code:
-    - `Error opening MediaSource.`
-    - `Browser reported a network error.`
-    - `Max error count ${errorCount} exceeded.` (the numeric value will vary)
-
-- **mse-decode**
-  - What it means: The browser reported a decoding error while trying to play the stream, which usually is a result of a codec incompatibility or corrupted frames.
-  - What to try: Check the browser console for the supported and negotiated codecs. Ensure your camera/restream is using H.264 video and AAC audio (these are the most compatible). If your camera uses a non-standard audio codec, configure `go2rtc` to transcode the stream to AAC. Try another browser (some browsers have stricter MSE/codec support) and, for iPhone, ensure you're on iOS 17.1 or newer.
-
-  - Possible console messages from the player code:
-    - `Safari cannot open MediaSource.`
-    - `Safari reported InvalidStateError.`
-    - `Safari reported decoding errors.`
-
-- **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.
-
-  - Possible console messages from the player code:
-    - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
-    - `Media playback has stalled after <n> seconds due to insufficient buffering or a network interruption.` (the seconds value will vary)
-
 ## Live view FAQ
 
 1. **Why don't I have audio in my Live view?**
@@ -318,19 +231,22 @@ When your browser runs into problems playing back your camera streams, it will l
    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)).
+
+   - 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)).
@@ -361,36 +277,3 @@ When your browser runs into problems playing back your camera streams, it will l
 7. **My camera streams have lots of visual artifacts / distortion.**
 
    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.
-
-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.
-
-   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
-   ```

docs/docs/configuration/masks.md

@@ -3,10 +3,6 @@ id: masks
 title: Masks
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 ## 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._
@@ -21,72 +17,35 @@ Object filter masks can be used to filter out stubborn false positives in fixed
 
 ![object mask](/img/bottom-center-mask.jpg)
 
-## Creating masks
+## Using the mask creator
 
-<ConfigTabs>
-<TabItem value="ui">
+To create a poly mask:
 
-Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> 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.
-
-</TabItem>
-<TabItem value="yaml">
+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.
+8. Restart Frigate to apply your changes.
 
 Your config file will be updated with the relative coordinates of the mask/zone:
 
 ```yaml
 motion:
-  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"
+  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"
 ```
 
-Multiple motion masks can be listed in your config:
+Multiple masks can be listed in your config.
 
 ```yaml
 motion:
   mask:
-    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"
+    - 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
 ```
 
-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"
-```
-
-</TabItem>
-</ConfigTabs>
-
-## 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):

docs/docs/configuration/metrics.md

@@ -3,42 +3,19 @@ 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Telemetry" /> to configure metrics and telemetry settings.
-
-</TabItem>
-<TabItem value="yaml">
-
-Metrics are available at `/api/metrics` by default. No additional Frigate configuration is required to expose them.
-
-</TabItem>
-</ConfigTabs>
-
 ## 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
@@ -48,25 +25,21 @@ Metrics are available at `/api/metrics` by default. No additional Frigate config
 - `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
 
 ### 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
@@ -75,10 +48,10 @@ 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
 ```
 

docs/docs/configuration/motion_detection.md

@@ -3,10 +3,6 @@ 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.
@@ -25,7 +21,7 @@ First, mask areas with regular motion not caused by the objects you want to dete
 
 ## Prepare For Testing
 
-The recommended way to tune motion detection is to use the built-in Motion Tuner. Navigate to <NavPath path="Settings > Camera configuration > Motion tuner" /> 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.
+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.
 
 ## Tuning Motion Detection During The Day
 
@@ -41,21 +37,8 @@ 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> to set the threshold globally.
-
-To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera, or use the <NavPath path="Settings > Camera configuration > Motion tuner" /> 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) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```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.
@@ -63,30 +46,14 @@ motion:
   threshold: 30
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
 
 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
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> to set the contour area globally.
-
-To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera, or use the <NavPath path="Settings > Camera configuration > Motion tuner" /> 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) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```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
@@ -98,9 +65,6 @@ motion:
   contour_area: 10
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
@@ -117,83 +81,27 @@ However, if the preferred day settings do not work well at night it is recommend
 
 ## Tuning For Large Changes In Motion
 
-### Lightning Threshold
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> and expand the advanced fields to find the lightning threshold setting.
-
-To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> 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) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```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
 ```
 
-</TabItem>
-</ConfigTabs>
-
-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 recalibrate. 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 be re-calibrated. In this case, it may be desirable to increase the `lightning_threshold` to ensure these objects are not missed.
 
 :::
 
-### Skip Motion On Large Scene Changes
+:::note
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> and expand the advanced fields to find the skip motion threshold setting.
-
-To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> 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). |
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
-
-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.
+Lightning threshold does not stop motion based recordings from being saved.
 
 :::
+
+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.

docs/docs/configuration/notifications.md

@@ -3,20 +3,10 @@ id: notifications
 title: Notifications
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 # 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:
@@ -28,27 +18,15 @@ In order to use notifications the following requirements must be met:
 
 ### Configuration
 
-Enable notifications and fill out the required fields.
+To configure notifications, go to the Frigate WebUI -> Settings -> Notifications and enable, then fill out the fields and save.
 
-Optionally, change the default cooldown period for notifications. The cooldown can also be overridden at the camera level.
+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.
 
 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
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Notifications > Notifications" />.
-   - Set **Email** to your email address
-   - Enable notifications for the desired cameras
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 notifications:
   enabled: True
@@ -56,21 +34,6 @@ notifications:
   cooldown: 10 # wait 10 seconds before sending another notification from any camera
 ```
 
-</TabItem>
-</ConfigTabs>
-
-#### Per-camera notifications
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Notifications" /> 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`)
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   doorbell:
@@ -80,9 +43,6 @@ cameras:
       cooldown: 30 # wait 30 seconds before sending another notification from the doorbell camera
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### 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.

docs/docs/configuration/object_filters.md

@@ -3,15 +3,11 @@ id: object_filters
 title: Filters
 ---
 
-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 rates.
 
 ## Object Scores
 
-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:
+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:
 
 | Frame | Current Score | Score History                     | Computed Score | Detected Object |
 | ----- | ------------- | --------------------------------- | -------------- | --------------- |
@@ -24,12 +20,6 @@ For object filters, any single detection below `min_score` will be ignored as a
 
 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.
@@ -38,46 +28,6 @@ Any detection below `min_score` will be immediately thrown out and never tracked
 
 `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.
 
-## Configuring Object Scores
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Objects" /> 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 <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
-
 ## Object Shape
 
 False positives can also be reduced by filtering a detection based on its shape.
@@ -96,50 +46,6 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 
 :::
 
-### Configuring Shape Filters
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Objects" /> 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 <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
-
 ## Other Tools
 
 ### Zones
@@ -148,4 +54,4 @@ cameras:
 
 ### 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 can be configured in <NavPath path="Settings > Camera configuration > Masks / Zones" />.
+[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.

docs/docs/configuration/objects.md

@@ -3,9 +3,6 @@ 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.
@@ -13,7 +10,7 @@ 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. To track additional objects, configure them in the objects settings.
+- `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.
 
 <ul>
   {labels.split("\n").map((label) => (
@@ -21,135 +18,6 @@ Please note:
   ))}
 </ul>
 
-## Configuring Tracked Objects
-
-By default, Frigate only tracks `person`. To track additional object types, add them to the tracked objects list.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Global configuration > Objects" />.
-   - 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 <NavPath path="Settings > Camera configuration > Objects" />.
-   - Add the desired object types to the **Objects to track** list
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-objects:
-  track:
-    - person
-    - car
-    - dog
-```
-
-To override at the camera level:
-
-```yaml
-cameras:
-  front_door:
-    objects:
-      track:
-        - person
-        - car
-```
-
-</TabItem>
-</ConfigTabs>
-
-## 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Objects" />.
-
-| 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 <NavPath path="Settings > Camera configuration > Objects" />.
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
-
-## 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> 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.
-
-</TabItem>
-<TabItem value="yaml">
-
-```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"
-```
-
-</TabItem>
-</ConfigTabs>
-
-:::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:

docs/docs/configuration/profiles.md

@@ -1,209 +0,0 @@
----
-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
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. **Create a profile** — Navigate to <NavPath path="Settings > Camera configuration > Profiles" />. 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 <NavPath path="Settings > Camera configuration > Profiles" />, 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 <NavPath path="Settings > Camera configuration > Profiles" />, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
-
-</TabItem>
-<TabItem value="yaml">
-
-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
-```
-
-</TabItem>
-</ConfigTabs>
-
-### 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 from 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.
-
-## 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`.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Profiles" /> 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 <NavPath path="Settings > Camera configuration > Profiles" /> or from the **Profiles** option in Frigate's main menu.
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
-
-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.

docs/docs/configuration/pwa.md

@@ -11,7 +11,7 @@ This adds features including the ability to deep link directly into the app.
 
 In order to install Frigate as a PWA, the following requirements must be met:
 
-- Frigate must be accessed via a secure context (localhost, secure https, VPN, etc.)
+- Frigate must be accessed via a secure context (localhost, secure https, etc.)
 - On Android, Firefox, Chrome, Edge, Opera, and Samsung Internet Browser all support installing PWAs.
 - On iOS 16.4 and later, PWAs can be installed from the Share menu in Safari, Chrome, Edge, Firefox, and Orion.
 
@@ -22,7 +22,3 @@ Installation varies slightly based on the device that is being used:
 - Desktop: Use the install button typically found in right edge of the address bar
 - Android: Use the `Install as App` button in the more options menu for Chrome, and the `Add app to Home screen` button for Firefox
 - iOS: Use the `Add to Homescreen` button in the share menu
-
-## Usage
-
-Once setup, the Frigate app can be used wherever it has access to Frigate. This means it can be setup as local-only, VPN-only, or fully accessible depending on your needs.

docs/docs/configuration/record.md

@@ -3,11 +3,7 @@ id: record
 title: Recording
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
-Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/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.
+Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/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.
 
 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.
 
@@ -17,23 +13,7 @@ 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 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Recording" />.
-
-- 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`
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 record:
@@ -52,27 +32,9 @@ record:
       mode: all
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Reduced storage: Only saving video when motion is detected
 
-To reduce storage requirements, configure recording to only retain video where motion or activity was detected.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Recording" />.
-
-- 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`
-
-</TabItem>
-<TabItem value="yaml">
+In order to reduce storage requirements, you can adjust your config to only retain video where motion / activity was detected.
 
 ```yaml
 record:
@@ -89,25 +51,9 @@ record:
       mode: motion
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Minimum: Alerts only
 
-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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Recording" />.
-
-- 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`
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 record:
@@ -120,79 +66,6 @@ record:
       mode: motion
 ```
 
-</TabItem>
-</ConfigTabs>
-
-## 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Recording" /> for global defaults, or <NavPath path="Settings > Camera configuration > (select camera) > Recording" /> 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  |
-
-</TabItem>
-<TabItem value="yaml">
-
-```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
-```
-
-</TabItem>
-</ConfigTabs>
-
-- **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.
-
 ## Will Frigate delete old recordings if my storage runs out?
 
 As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 hours of recordings will be deleted.
@@ -209,21 +82,7 @@ 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 configured. By default, continuous recording is disabled.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Recording" />.
-
-| 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     |
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 record:
@@ -234,28 +93,11 @@ record:
     days: 2 # <- number of days to keep motion recordings
 ```
 
-</TabItem>
-</ConfigTabs>
-
-Continuous recording supports different retention modes [which are described below](#configuring-recording-retention).
+Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
 
 ### Object Recording
 
-The number of days to retain recordings for review items can be specified for items classified as alerts as well as tracked objects.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Recording" />.
-
-| 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 |
-
-</TabItem>
-<TabItem value="yaml">
+The number of days to record review items can be specified for review items classified as alerts as well as tracked objects.
 
 ```yaml
 record:
@@ -268,11 +110,10 @@ record:
       days: 10 # <- number of days to keep detections recordings
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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.
+
 ## Can I have "continuous" recordings, but only at certain times?
 
 Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
@@ -281,52 +122,25 @@ 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.
 
-### Custom export with FFmpeg arguments
+### Time-lapse export
 
-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:
+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).
 
+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
+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).
-
-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 `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.
+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.
 
 :::
 
@@ -334,18 +148,19 @@ To reduce output file size, add the FFmpeg parameter `-qp n` to `ffmpeg_output_a
 
 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 Media Files With Disk
+## Syncing Recordings With Disk
 
-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.
+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.
 
-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.
+```yaml
+record:
+  sync_recordings: True
+```
 
-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/<job_id>.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).
+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.
 
 :::warning
 
-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.
+The sync operation uses considerable CPU resources and in most cases is not needed, only enable when necessary.
 
 :::

docs/docs/configuration/reference.md

@@ -16,8 +16,6 @@ mqtt:
   # Optional: Enable mqtt server (default: shown below)
   enabled: True
   # Required: host name
-  # NOTE: MQTT host can be specified with an environment variable or docker secrets that must begin with 'FRIGATE_'.
-  #       e.g. host: '{FRIGATE_MQTT_HOST}'
   host: mqtt.server.com
   # Optional: port (default: shown below)
   port: 1883
@@ -75,19 +73,11 @@ tls:
   # Optional: Enable TLS for port 8971 (default: shown below)
   enabled: True
 
-# Optional: Networking configuration
+# Optional: IPv6 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:
@@ -133,7 +123,7 @@ auth:
   # Optional: Refresh time in seconds (default: shown below)
   # When the session is going to expire in less time than this setting,
   # it will be refreshed back to the session_length.
-  refresh_time: 1800 # 30 minutes
+  refresh_time: 43200 # 12 hours
   # Optional: Rate limiting for login failures to help prevent brute force
   # login attacks (default: shown below)
   # See the docs for more information on valid values
@@ -256,7 +246,7 @@ birdseye:
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
-  # Optional: ffmpeg binary path (default: shown below)
+  # Optional: ffmpeg binry path (default: shown below)
   # can also be set to `7.0` 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"
@@ -347,15 +337,7 @@ 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:
-    # 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"
+  mask: 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:
@@ -375,15 +357,7 @@ 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:
-        # 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"
+      mask: 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)
@@ -482,16 +456,12 @@ 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 and motion checks stop for that frame. Recordings are unaffected. (default: shown below)
+  # 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.
+  # 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.
@@ -511,15 +481,7 @@ motion:
   frame_height: 100
   # Optional: motion mask
   # NOTE: see docs for more detailed info on creating masks
-  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"
+  mask: 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.
@@ -548,6 +510,8 @@ 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)
@@ -570,8 +534,6 @@ 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: Recording Preview Settings
   preview:
     # Optional: Quality of recording preview (default: shown below).
@@ -618,12 +580,13 @@ record:
       #       never stored, so setting the mode to "all" here won't bring them back.
       mode: motion
 
-# 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.
+# Optional: Configuration for the jpg snapshots written to the clips directory for each tracked object
 # NOTE: Can be overridden at the camera level
 snapshots:
-  # Optional: Enable writing snapshot images to /media/frigate/clips (default: shown below)
+  # Optional: Enable writing jpg snapshot 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)
@@ -641,8 +604,8 @@ snapshots:
     # Optional: Per object retention days
     objects:
       person: 15
-  # Optional: quality of the encoded snapshot image, 0-100 (default: shown below)
-  quality: 60
+  # Optional: quality of the encoded jpeg, 0-100 (default: shown below)
+  quality: 70
 
 # Optional: Configuration for semantic search capability
 semantic_search:
@@ -733,63 +696,22 @@ genai:
   # 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
 audio_transcription:
-  # Optional: Enable live and speech event audio transcription (default: shown below)
+  # Optional: Enable license plate recognition (default: shown below)
   enabled: False
-  # Optional: The device to run the models on for live transcription. (default: shown below)
+  # Optional: The device to run the models on (default: shown below)
   device: CPU
-  # Optional: Set the model size used for live transcription. (default: shown below)
+  # Optional: Set the model size used for transcription. (default: shown below)
   model_size: small
   # Optional: Set the language used for transcription translation. (default: shown below)
   # List of language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
   language: en
 
-# Optional: Configuration for classification models
-classification:
-  # Optional: Configuration for bird classification
-  bird:
-    # Optional: Enable bird classification (default: shown below)
-    enabled: False
-    # Optional: Minimum classification score required to be considered a match (default: shown below)
-    threshold: 0.9
-  custom:
-    # Required: name of the classification model
-    model_name:
-      # Optional: Enable running the model (default: shown below)
-      enabled: True
-      # Optional: Name of classification model (default: shown below)
-      name: None
-      # Optional: Classification score threshold to change the state (default: shown below)
-      threshold: 0.8
-      # Optional: Number of classification attempts to save in the recent classifications tab (default: shown below)
-      # NOTE: Defaults to 200 for object classification and 100 for state classification if not specified
-      save_attempts: None
-      # Optional: Object classification configuration
-      object_config:
-        # Required: Object types to classify
-        objects: [dog]
-        # Optional: Type of classification that is applied (default: shown below)
-        classification_type: sub_label
-      # Optional: State classification configuration
-      state_config:
-        # Required: Cameras to run classification on
-        cameras:
-          camera_name:
-            # Required: Crop of image frame on this camera to run classification on
-            crop: [0, 180, 220, 400]
-        # 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 (v1.9.13)
+# Uses https://github.com/AlexxIT/go2rtc (v1.9.10)
 # NOTE: The default go2rtc API port (1984) must be used,
 #       changing this port for the integrated go2rtc instance is not supported.
 go2rtc:
@@ -875,11 +797,6 @@ 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
@@ -895,9 +812,6 @@ 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
@@ -951,8 +865,6 @@ cameras:
     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 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).
       port: 8000
@@ -961,15 +873,11 @@ cameras:
       user: admin
       # Optional: password for login.
       password: admin
-      # Optional: Skip TLS verification and disable digest authentication for the ONVIF server (default: shown below)
+      # Optional: Skip TLS verification from the ONVIF server (default: shown below)
       tls_insecure: False
       # 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:
@@ -1033,49 +941,6 @@ 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)
@@ -1099,6 +964,10 @@ ui:
   #    full: 8:15:22 PM Mountain Standard Time
   # (default: shown below).
   time_style: medium
+  # Optional: Ability to manually override the date / time styling to use strftime format
+  # https://www.gnu.org/software/libc/manual/html_node/Formatting-Calendar-Time.html
+  # possible values are shown above (default: not set)
+  strftime_fmt: "%Y/%m/%d %H:%M"
   # Optional: Set the unit system to either "imperial" or "metric" (default: metric)
   # Used in the UI and in MQTT topics
   unit_system: metric
@@ -1142,14 +1011,4 @@ 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
 ```

docs/docs/configuration/restream.md

@@ -3,15 +3,11 @@ 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://<frigate_host>:8554/<camera_name>`. 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/v1.9.13) 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/v1.9.13#configuration) for more advanced configurations and features.
+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.
 
 :::note
 
@@ -28,17 +24,16 @@ birdseye:
   restream: True
 ```
 
-:::tip
+:::tip 
 
-To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`). This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
+To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`).  This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
 
 :::
-
 ### Securing Restream With Authentication
 
 The go2rtc restream can be secured with RTSP based username / password authentication. Ex:
 
-```yaml {2-4}
+```yaml
 go2rtc:
   rtsp:
     username: "admin"
@@ -56,16 +51,6 @@ 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and set the input paths to use the local restream URL (`rtsp://127.0.0.1:8554/<camera_name>`).
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 go2rtc:
   streams:
@@ -101,21 +86,10 @@ cameras:
             - audio # <- only necessary if audio detection is enabled
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### With Sub Stream
 
 Two connections are made to the camera. One for the sub stream, one for the restream, `record` connects to the restream.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > go2rtc streams" /> and add stream entries for each camera and its sub stream. Then navigate to <NavPath path="Settings > Camera configuration > FFmpeg" /> for each camera and configure separate inputs for the main and sub streams using the local restream URLs.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 go2rtc:
   streams:
@@ -163,9 +137,6 @@ cameras:
             - detect
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## Handling Complex Passwords
 
 go2rtc expects URL-encoded passwords in the config, [urlencoder.org](https://urlencoder.org) can be used for this purpose.
@@ -175,7 +146,6 @@ For example:
 ```yaml
 go2rtc:
   streams:
-    # highlight-error-line
     my_camera: rtsp://username:$@foo%@192.168.1.100
 ```
 
@@ -184,71 +154,14 @@ becomes
 ```yaml
 go2rtc:
   streams:
-    # highlight-next-line
     my_camera: rtsp://username:$%40foo%25@192.168.1.100
 ```
 
 See [this comment](https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-2242296489) for more information.
 
-## Preventing go2rtc from blocking two-way audio {#two-way-talk-restream}
-
-For cameras that support two-way talk, go2rtc will automatically establish an audio output backchannel when connecting to an RTSP stream. This backchannel blocks access to the camera's audio output for two-way talk functionality, preventing both Frigate and other applications from using it.
-
-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)
-
-Configuration example:
-
-```yaml
-go2rtc:
-  streams:
-    front_door:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2#backchannel=0
-    front_door_twoway:
-      - rtsp://user:password@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-```
-
-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.
-
-## 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.
-
-If you attempt to use these sources in your configuration, the streams will be removed and an error message will be printed in the logs.
-
-To enable these sources, you must set the environment variable `GO2RTC_ALLOW_ARBITRARY_EXEC=true`. This can be done in your Docker Compose file or container environment:
-
-```yaml
-environment:
-  - GO2RTC_ALLOW_ARBITRARY_EXEC=true
-```
-
-:::warning
-
-Enabling arbitrary exec sources allows execution of arbitrary commands through go2rtc stream configurations. Only enable this if you understand the security implications and trust all sources of your configuration.
-
-:::
-
 ## Advanced Restream Configurations
 
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
-
-:::warning
-
-The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
-
-:::
-
-:::warning
-
-The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
-
-:::
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
 NOTE: The output will need to be passed with two curly braces `{{output}}`
 
@@ -256,4 +169,4 @@ NOTE: The output will need to be passed with two curly braces `{{output}}`
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
-```
+```

docs/docs/configuration/review.md

@@ -3,10 +3,6 @@ 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.
@@ -27,7 +23,7 @@ Not every segment of video captured by Frigate may be of the same level of inter
 
 :::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 more labels via <NavPath path="Settings > Global configuration > Objects" /> or <NavPath path="Settings > Camera configuration > Objects" /> and select your camera. Alternatively, 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 the following to your config:
 
 ```yaml
 objects:
@@ -42,17 +38,7 @@ 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. Configure the alert labels to include any object or audio label.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Review" /> or <NavPath path="Settings > Camera configuration > Review" /> and select your camera.
-
-Expand **Alerts config** and configure which labels and zones should generate alerts.
-
-</TabItem>
-<TabItem value="yaml">
+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:
 
 ```yaml
 # can be overridden at the camera level
@@ -66,23 +52,10 @@ review:
       - speech
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Review" /> or <NavPath path="Settings > Camera configuration > Review" /> and select your camera.
-
-Expand **Detections config** and configure which labels should qualify as detections.
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 # can be overridden at the camera level
 review:
@@ -92,25 +65,13 @@ review:
       - dog
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## Excluding a camera from alerts or detections
 
-To exclude a specific camera from alerts or detections, provide an empty list to the alerts or detections labels field at the camera level.
+To exclude a specific camera from alerts or detections, simply provide an empty list to the alerts or detections field _at the camera level_.
 
-For example, to exclude objects on the camera _gatecamera_ from any detections:
+For example, to exclude objects on the camera _gatecamera_ from any detections, include this in your config:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Review" /> and select the **gatecamera** camera.
-   - Expand **Detections config** and turn off all of the object label switches.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {3-5}
+```yaml
 cameras:
   gatecamera:
     review:
@@ -118,9 +79,6 @@ cameras:
         labels: []
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## 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)

docs/docs/configuration/semantic_search.md

@@ -3,43 +3,23 @@ id: semantic_search
 title: Semantic Search
 ---
 
-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.
 
-A minimum of 8GB of RAM is required to use Semantic Search. A CPU with AVX + AVX2 instructions is required to run Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
+A minimum of 8GB of RAM is required to use Semantic Search. A GPU is not strictly required but will provide a significant performance increase over CPU-only systems.
 
 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 before it can be used. Semantic Search is a global configuration setting.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Semantic search" />.
-
-- Set **Enable semantic search** to on
-
-</TabItem>
-<TabItem value="yaml">
+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.
 
 ```yaml
 semantic_search:
@@ -47,9 +27,6 @@ semantic_search:
   reindex: False
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::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.
@@ -64,20 +41,7 @@ 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Semantic search" />.
-
-| 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) |
-
-</TabItem>
-<TabItem value="yaml">
+Differently weighted versions of the Jina models are available and can be selected by setting the `model_size` config option as `small` or `large`:
 
 ```yaml
 semantic_search:
@@ -86,9 +50,6 @@ semantic_search:
   model_size: small
 ```
 
-</TabItem>
-</ConfigTabs>
-
 - 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.
 
@@ -98,20 +59,7 @@ 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, set the model to `jinav2`.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Semantic search" />.
-
-| 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) |
-
-</TabItem>
-<TabItem value="yaml">
+To use the V2 model, update the `model` parameter in your config:
 
 ```yaml
 semantic_search:
@@ -120,9 +68,6 @@ semantic_search:
   model_size: large
 ```
 
-</TabItem>
-</ConfigTabs>
-
 For most users, especially native English speakers, the V1 model remains the recommended choice.
 
 :::note
@@ -131,74 +76,10 @@ 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Semantic search" />.
-
-| 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 <NavPath path="Settings > Enrichments > Generative AI" />.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-genai:
-  default:
-    provider: llamacpp
-    base_url: http://localhost:8080
-    model: your-model-name
-    roles:
-      - embeddings
-      - vision
-      - tools
-
-semantic_search:
-  enabled: True
-  model: default
-```
-
-</TabItem>
-</ConfigTabs>
-
-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.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Enrichments > Semantic search" />.
-
-| 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`) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 semantic_search:
   enabled: True
@@ -207,9 +88,6 @@ semantic_search:
   device: 0
 ```
 
-</TabItem>
-</ConfigTabs>
-
 :::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.
@@ -241,15 +119,16 @@ Semantic Search must be enabled to use Triggers.
 
 ### Configuration
 
-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 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 best configured through the Frigate UI.
 
 #### Managing Triggers in the UI
 
-1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> 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:
+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:
    - 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`).
@@ -260,14 +139,14 @@ 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.
-4. Save the trigger to update the configuration and store the embedding in the database.
+5. 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.
@@ -282,6 +161,6 @@ 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.

docs/docs/configuration/snapshots.md

@@ -3,144 +3,10 @@ id: snapshots
 title: Snapshots
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
-Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>-clean.webp`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
+Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>.jpg`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
 
 Snapshots are accessible in the UI in the Explore pane. This 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.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Snapshots" />.
-
-- Set **Enable snapshots** to on
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-snapshots:
-  enabled: True
-```
-
-</TabItem>
-</ConfigTabs>
-
-To override snapshot settings for a specific camera:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > Snapshots" /> and select your camera.
-
-- Set **Enable snapshots** to on
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-cameras:
-  front_door:
-    snapshots:
-      enabled: True
-```
-
-</TabItem>
-</ConfigTabs>
-
-## Snapshot Options
-
-Configure how snapshots are rendered and stored. These settings control the defaults applied when snapshots are requested via the API.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Snapshots" />.
-
-| 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                          |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-snapshots:
-  enabled: True
-  timestamp: False
-  bounding_box: True
-  crop: False
-  height: 175
-  required_zones: []
-  quality: 60
-```
-
-</TabItem>
-</ConfigTabs>
-
-## Snapshot Retention
-
-Configure how long snapshots are retained on disk. Per-object retention overrides allow different retention periods for specific object types.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Snapshots" />.
-
-| Field                                              | Description                                                                         |
-| -------------------------------------------------- | ----------------------------------------------------------------------------------- |
-| **Snapshot retention > Default retention**         | Number of days to retain snapshots (default: 10)                                    |
-| **Snapshot retention > Retention mode**            | Retention mode: `all`, `motion`, or `active_objects`                                |
-| **Snapshot retention > Object retention > Person** | Per-object overrides for retention days (e.g., keep `person` snapshots for 15 days) |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml
-snapshots:
-  enabled: True
-  retain:
-    default: 10
-    mode: motion
-    objects:
-      person: 15
-```
-
-</TabItem>
-</ConfigTabs>
-
-## Frame Selection
-
-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 the camera MQTT settings.
-
-## Rendering
-
-Frigate stores a single clean snapshot on disk:
-
-| API / Use                                | Result                                                                                                |
-| ---------------------------------------- | ----------------------------------------------------------------------------------------------------- |
-| Stored file                              | `<camera>-<id>-clean.webp`, always unannotated                                                        |
-| `/api/events/<id>/snapshot.jpg`          | Starts from the camera's `snapshots` defaults, then applies any query param overrides at request time |
-| `/api/events/<id>/snapshot-clean.webp`   | Returns the same stored snapshot without annotations                                                  |
-| [Frigate+](/plus/first_model) submission | Uses the same stored clean snapshot                                                                   |
-
-MQTT snapshots are configured separately under the camera MQTT settings and are unrelated to the stored event snapshot.
+Snapshots sent via MQTT are configured in the [config file](https://docs.frigate.video/configuration/) under `cameras -> your_camera -> mqtt`

docs/docs/configuration/stationary_objects.md

@@ -1,29 +1,14 @@
 # Stationary Objects
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 An object is considered stationary when it is being tracked and has been in a very similar position for a certain number of frames. This number is defined in the configuration under `detect -> stationary -> threshold`, and is 10x the frame rate (or 10 seconds) by default. Once an object is considered stationary, it will remain stationary until motion occurs within the object at which point object detection will start running again. If the object changes location, it will be considered active.
 
 ## Why does it matter if an object is stationary?
 
-Once an object becomes stationary, object detection will not be continually run on that object. This serves to reduce resource usage and redundant detections when there has been no motion near the tracked object. This also means that Frigate is contextually aware, and can for example [filter out recording segments](record.md#configuring-recording-retention) to only when the object is considered active. Motion alone does not determine if an object is "active" for active_objects segment retention. Lighting changes for a parked car won't make an object active.
+Once an object becomes stationary, object detection will not be continually run on that object. This serves to reduce resource usage and redundant detections when there has been no motion near the tracked object. This also means that Frigate is contextually aware, and can for example [filter out recording segments](record.md#what-do-the-different-retain-modes-mean) to only when the object is considered active. Motion alone does not determine if an object is "active" for active_objects segment retention. Lighting changes for a parked car won't make an object active.
 
 ## Tuning stationary behavior
 
-Configure how Frigate handles stationary objects.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > Object detection" />.
-
-- Set **Stationary objects config > Stationary interval** to the frequency for running detection on stationary objects (default: 50). Once stationary, detection runs every nth frame to verify the object is still present. There is no way to disable stationary object tracking with this value.
-- Set **Stationary objects config > Stationary threshold** to the number of frames an object must remain relatively still before it is considered stationary (default: 50)
-
-</TabItem>
-<TabItem value="yaml">
+The default config is:
 
 ```yaml
 detect:
@@ -32,8 +17,11 @@ detect:
     threshold: 50
 ```
 
-</TabItem>
-</ConfigTabs>
+`interval` is defined as the frequency for running detection on stationary objects. This means that by default once an object is considered stationary, detection will not be run on it until motion is detected or until the interval (every 50th frame by default). With `interval >= 1`, every nth frames detection will be run to make sure the object is still there.
+
+NOTE: There is no way to disable stationary object tracking with this value.
+
+`threshold` is the number of frames an object needs to remain relatively still before it is considered stationary.
 
 ## Why does Frigate track stationary objects?
 

docs/docs/configuration/tls.md

@@ -3,41 +3,24 @@ id: tls
 title: TLS
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 # TLS
 
 Frigate's integrated NGINX server supports TLS certificates. By default Frigate will generate a self signed certificate that will be used for port 8971. Frigate is designed to make it easy to use whatever tool you prefer to manage certificates.
 
 Frigate is often running behind a reverse proxy that manages TLS certificates for multiple services. You will likely need to set your reverse proxy to allow self signed certificates or you can disable TLS in Frigate's config. However, if you are running on a dedicated device that's separate from your proxy or if you expose Frigate directly to the internet, you may want to configure TLS with valid certificates.
 
-In many deployments, TLS will be unnecessary. Disable it as follows:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > TLS" />.
-
-- Set **Enable TLS** to off if running behind a reverse proxy that handles TLS (default: on)
-
-</TabItem>
-<TabItem value="yaml">
+In many deployments, TLS will be unnecessary. It can be disabled in the config with the following yaml:
 
 ```yaml
 tls:
   enabled: False
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ## Certificates
 
 TLS certificates can be mounted at `/etc/letsencrypt/live/frigate` using a bind mount or docker volume.
 
-```yaml {3-4}
+```yaml
 frigate:
   ...
   volumes:
@@ -49,7 +32,7 @@ Within the folder, the private key is expected to be named `privkey.pem` and the
 
 Note that certbot uses symlinks, and those can't be followed by the container unless it has access to the targets as well, so if using certbot you'll also have to mount the `archive` folder for your domain, e.g.:
 
-```yaml {3-5}
+```yaml
 frigate:
   ...
   volumes:
@@ -63,7 +46,7 @@ Frigate automatically compares the fingerprint of the certificate at `/etc/letse
 
 If you issue Frigate valid certificates you will likely want to configure it to run on port 443 so you can access it without a port number like `https://your-frigate-domain.com` by mapping 8971 to 443.
 
-```yaml {3-4}
+```yaml
 frigate:
   ...
   ports:

docs/docs/configuration/zones.md

@@ -3,10 +3,6 @@ id: zones
 title: Zones
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Presence in a zone is evaluated based on the bottom center of the bounding box for the object. It does not matter how much of the bounding box overlaps with the zone.
 
 For example, the cat in this image is currently in Zone 1, but **not** Zone 2.
@@ -14,59 +10,15 @@ For example, the cat in this image is currently in Zone 1, but **not** Zone 2.
 
 Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.
 
-## Enabling/Disabling Zones
-
-Zones can be toggled on or off without removing them from the configuration. Disabled zones are completely ignored at runtime - objects will not be tracked for zone presence, and zones will not appear in the debug view. This is useful for temporarily disabling a zone during certain seasons or times of day without modifying the configuration.
-
 During testing, enable the Zones option for the Debug view of your camera (Settings --> Debug) so you can adjust as needed. The zone line will increase in thickness when any object enters the zone.
 
-## Creating a Zone
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Under the **Zones** section, click the plus icon to add a new zone.
-3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
-4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
-5. Press **Save** when finished.
-
-</TabItem>
-<TabItem value="yaml">
-
-Follow [the steps for creating a mask](masks.md), but use the zone section of the web UI instead. Alternatively, define zones directly in your configuration file:
-
-```yaml
-cameras:
-  name_of_your_camera:
-    zones:
-      entire_yard:
-        friendly_name: Entire yard
-        coordinates: 0.123,0.456,0.789,0.012,...
-```
-
-</TabItem>
-</ConfigTabs>
+To create a zone, follow [the steps for a "Motion mask"](masks.md), but use the section of the web UI for creating a zone instead.
 
 ### Restricting alerts and detections to specific zones
 
-Often you will only want alerts to be created when an object enters areas of interest. This is done by combining zones with required zones for review items.
+Often you will only want alerts to be created when an object enters areas of interest. This is done using zones along with setting required_zones. Let's say you only want to have an alert created when an object enters your entire_yard zone, the config would be:
 
-To create an alert only when an object enters the `entire_yard` zone:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > Review" />.
-
-| Field                              | Description                                                                               |
-| ---------------------------------- | ----------------------------------------------------------------------------------------- |
-| **Alerts config > Required zones** | Zones that an object must enter to be considered an alert; leave empty to allow any zone. |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {6,8}
+```yaml
 cameras:
   name_of_your_camera:
     review:
@@ -79,23 +31,7 @@ cameras:
         coordinates: ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
-You may also want to filter detections to only be created when an object enters a secondary area of interest. For example, to trigger alerts when an object enters the inner area of the yard but detections when an object enters the edge of the yard:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Camera configuration > Review" />.
-
-| Field                                  | Description                                                                                  |
-| -------------------------------------- | -------------------------------------------------------------------------------------------- |
-| **Alerts config > Required zones**     | Zones that an object must enter to be considered an alert; leave empty to allow any zone.    |
-| **Detections config > Required zones** | Zones that an object must enter to be considered a detection; leave empty to allow any zone. |
-
-</TabItem>
-<TabItem value="yaml">
+You may also want to filter detections to only be created when an object enters a secondary area of interest. This is done using zones along with setting required_zones. Let's say you want alerts when an object enters the inner area of the yard but detections when an object enters the edge of the yard, the config would be
 
 ```yaml
 cameras:
@@ -116,22 +52,8 @@ cameras:
         coordinates: ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Restricting snapshots to specific zones
 
-To only save snapshots when an object enters a specific zone:
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Snapshots" /> and select your camera.
-   - Set **Required zones** to `entire_yard`
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   name_of_your_camera:
@@ -144,24 +66,9 @@ cameras:
         coordinates: ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Restricting zones to specific objects
 
-Sometimes you want to limit a zone to specific object types to have more granular control of when alerts, detections, and snapshots are saved. The following example limits one zone to person objects and the other to cars.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Create a zone named `entire_yard` covering everywhere you want to track a person.
-   - Under **Objects**, add `person`
-3. Create a second zone named `front_yard_street` covering just the street.
-   - Under **Objects**, add `car`
-
-</TabItem>
-<TabItem value="yaml">
+Sometimes you want to limit a zone to specific object types to have more granular control of when alerts, detections, and snapshots are saved. The following example will limit one zone to person objects and the other to cars.
 
 ```yaml
 cameras:
@@ -177,11 +84,9 @@ cameras:
           - car
 ```
 
-</TabItem>
-</ConfigTabs>
-
 Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
 
+
 ### Zone Loitering
 
 Sometimes objects are expected to be passing through a zone, but an object loitering in an area is unexpected. Zones can be configured to have a minimum loitering time after which the object will be considered in the zone.
@@ -189,91 +94,47 @@ Sometimes objects are expected to be passing through a zone, but an object loite
 :::note
 
 When using loitering zones, a review item will behave in the following way:
-
 - When a person is in a loitering zone, the review item will remain active until the person leaves the loitering zone, regardless of if they are stationary.
 - When any other object is in a loitering zone, the review item will remain active until the loitering time is met. Then if the object is stationary the review item will end.
 
 :::
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Edit or create the zone (e.g., `sidewalk`).
-   - Set **Loitering time** to the desired number of seconds (e.g., `4`)
-   - Under **Objects**, add the relevant object types (e.g., `person`)
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   name_of_your_camera:
     zones:
       sidewalk:
-        # highlight-next-line
         loitering_time: 4 # unit is in seconds
         objects:
           - person
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Zone Inertia
 
-Sometimes an objects bounding box may be slightly incorrect and the bottom center of the bounding box is inside the zone while the object is not actually in the zone. Zone inertia helps guard against this by requiring an object's bounding box to be within the zone for multiple consecutive frames.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Edit or create the zone (e.g., `front_yard`).
-   - Set **Inertia** to the desired number of consecutive frames (e.g., `3`)
-
-</TabItem>
-<TabItem value="yaml">
+Sometimes an objects bounding box may be slightly incorrect and the bottom center of the bounding box is inside the zone while the object is not actually in the zone. Zone inertia helps guard against this by requiring an object's bounding box to be within the zone for multiple consecutive frames. This value can be configured:
 
 ```yaml
 cameras:
   name_of_your_camera:
     zones:
       front_yard:
-        # highlight-next-line
         inertia: 3
         objects:
           - person
 ```
 
-</TabItem>
-</ConfigTabs>
-
 There may also be cases where you expect an object to quickly enter and exit a zone, like when a car is pulling into the driveway, and you may want to have the object be considered present in the zone immediately:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Edit or create the zone (e.g., `driveway_entrance`).
-   - Set **Inertia** to `1`
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   name_of_your_camera:
     zones:
       driveway_entrance:
-        # highlight-next-line
         inertia: 1
         objects:
           - car
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Speed Estimation
 
 Frigate can be configured to estimate the speed of objects moving through a zone. This works by combining data from Frigate's object tracker and "real world" distance measurements of the edges of the zone. The recommended use case for this feature is to track the speed of vehicles on a road as they move through the zone.
@@ -284,19 +145,7 @@ Your zone must be defined with exactly 4 points and should be aligned to the gro
 
 Speed estimation requires a minimum number of frames for your object to be tracked before a valid estimate can be calculated, so create your zone away from places where objects enter and exit for the best results. The object's bounding box must be stable and remain a constant size as it enters and exits the zone. _Your zone should not take up the full frame, and the zone does **not** need to be the same size or larger than the objects passing through it._ An object's speed is tracked while it passes through the zone and then saved to Frigate's database.
 
-Accurate real-world distance measurements are required to estimate speeds. These distances can be specified through the `distances` field. Each number represents the real-world distance between consecutive points in the `coordinates` list. The fastest and most accurate way to configure this is through the Zone Editor in the Frigate UI.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Create or edit a zone with exactly 4 points aligned to the ground plane.
-3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
-   - For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
-4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
-
-</TabItem>
-<TabItem value="yaml">
+Accurate real-world distance measurements are required to estimate speeds. These distances can be specified in your zone config through the `distances` field.
 
 ```yaml
 cameras:
@@ -307,34 +156,16 @@ cameras:
         distances: 10,12,11,13.5 # in meters or feet
 ```
 
-So in the example above, the distance between the first two points ([0.033,0.306] and [0.324,0.138]) is 10. The distance between the second and third set of points ([0.324,0.138] and [0.439,0.185]) is 12, and so on.
-
-</TabItem>
-</ConfigTabs>
+Each number in the `distance` field represents the real-world distance between the points in the `coordinates` list. So in the example above, the distance between the first two points ([0.033,0.306] and [0.324,0.138]) is 10. The distance between the second and third set of points ([0.324,0.138] and [0.439,0.185]) is 12, and so on. The fastest and most accurate way to configure this is through the Zone Editor in the Frigate UI.
 
 The `distance` values are measured in meters (metric) or feet (imperial), depending on how `unit_system` is configured in your `ui` config:
 
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > UI" />.
-
-| Field           | Description                                                          |
-| --------------- | -------------------------------------------------------------------- |
-| **Unit system** | Set to `metric` (kilometers per hour) or `imperial` (miles per hour) |
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 ui:
   # can be "metric" or "imperial", default is metric
   unit_system: metric
 ```
 
-</TabItem>
-</ConfigTabs>
-
 The average speed of your object as it moved through your zone is saved in Frigate's database and can be seen in the UI in the Tracked Object Details pane in Explore. Current estimated speed can also be seen on the debug view as the third value in the object label (see the caveats below). Current estimated speed, average estimated speed, and velocity angle (the angle of the direction the object is moving relative to the frame) of tracked objects is also sent through the `events` MQTT topic. See the [MQTT docs](../integrations/mqtt.md#frigateevents).
 
 These speed values are output as a number in miles per hour (mph) or kilometers per hour (kph). For miles per hour, set `unit_system` to `imperial`. For kilometers per hour, set `unit_system` to `metric`.
@@ -353,17 +184,6 @@ These speed values are output as a number in miles per hour (mph) or kilometers
 
 Zones can be configured with a minimum speed requirement, meaning an object must be moving at or above this speed to be considered inside the zone. Zone `distances` must be defined as described above.
 
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
-2. Edit or create the zone with distances configured.
-   - Set **Speed threshold** to the desired minimum speed (e.g., `20`)
-   - The unit is kph or mph, depending on the **Unit system** setting
-
-</TabItem>
-<TabItem value="yaml">
-
 ```yaml
 cameras:
   name_of_your_camera:
@@ -372,9 +192,5 @@ cameras:
         coordinates: ...
         distances: ...
         inertia: 1
-        # highlight-next-line
         speed_threshold: 20 # unit is in kph or mph, depending on how unit_system is set (see above)
 ```
-
-</TabItem>
-</ConfigTabs>

docs/docs/development/contributing.md

@@ -17,15 +17,15 @@ From here, follow the guides for:
 - [Web Interface](#web-interface)
 - [Documentation](#documentation)
 
-### Frigate Home Assistant App
+### Frigate Home Assistant Add-on
 
-This repository holds the Home Assistant App, for use with Home Assistant OS and compatible installations. It is the piece that allows you to run Frigate from your Home Assistant Supervisor tab.
+This repository holds the Home Assistant Add-on, for use with Home Assistant OS and compatible installations. It is the piece that allows you to run Frigate from your Home Assistant Supervisor tab.
 
 Fork [blakeblackshear/frigate-hass-addons](https://github.com/blakeblackshear/frigate-hass-addons) to your own Github profile, then clone the forked repo to your local machine.
 
 ### Frigate Home Assistant Integration
 
-This repository holds the custom integration that allows your Home Assistant installation to automatically create entities for your Frigate instance, whether you are running Frigate as a standalone Docker container or as a [Home Assistant App](#frigate-home-assistant-app).
+This repository holds the custom integration that allows your Home Assistant installation to automatically create entities for your Frigate instance, whether you are running Frigate as a standalone Docker container or as a [Home Assistant Add-on](#frigate-home-assistant-add-on).
 
 Fork [blakeblackshear/frigate-hass-integration](https://github.com/blakeblackshear/frigate-hass-integration) to your own GitHub profile, then clone the forked repo to your local machine.
 
@@ -89,14 +89,6 @@ After closing VS Code, you may still have containers running. To close everythin
 
 ### Testing
 
-#### Unit Tests
-
-GitHub will execute unit tests on new PRs. You must ensure that all tests pass.
-
-```shell
-python3 -u -m unittest
-```
-
 #### FFMPEG Hardware Acceleration
 
 The following commands are used inside the container to ensure hardware acceleration is working properly.
@@ -133,28 +125,6 @@ ffmpeg -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -hwaccel_output_format
 ffmpeg -c:v h264_qsv -re -stream_loop -1 -i https://streams.videolan.org/ffmpeg/incoming/720p60.mp4 -f rawvideo -pix_fmt yuv420p pipe: > /dev/null
 ```
 
-### Submitting a pull request
-
-Code must be formatted, linted and type-tested. GitHub will run these checks on pull requests, so it is advised to run them yourself prior to opening.
-
-**Formatting**
-
-```shell
-ruff format frigate migrations docker *.py
-```
-
-**Linting**
-
-```shell
-ruff check frigate migrations docker *.py
-```
-
-**MyPy Static Typing**
-
-```shell
-python3 -u -m mypy --config-file frigate/mypy.ini frigate
-```
-
 ## Web Interface
 
 ### Prerequisites

docs/docs/frigate/camera_setup.md

@@ -11,12 +11,6 @@ Cameras configured to output H.264 video and AAC audio will offer the most compa
 
 - **Stream Viewing**: This stream will be rebroadcast as is to Home Assistant for viewing with the stream component. Setting this resolution too high will use significant bandwidth when viewing streams in Home Assistant, and they may not load reliably over slower connections.
 
-:::tip
-
-For the best experience in Frigate's UI, configure your camera so that the detection and recording streams use the same aspect ratio. For example, if your main stream is 3840x2160 (16:9), set your substream to 640x360 (also 16:9) instead of 640x480 (4:3). While not strictly required, matching aspect ratios helps ensure seamless live stream display and preview/recordings playback.
-
-:::
-
 ### Choosing a detect resolution
 
 The ideal resolution for detection is one where the objects you want to detect fit inside the dimensions of the model used by Frigate (320x320). Frigate does not pass the entire camera frame to object detection. It will crop an area of motion from the full frame and look in that portion of the frame. If the area being inspected is larger than 320x320, Frigate must resize it before running object detection. Higher resolutions do not improve the detection accuracy because the additional detail is lost in the resize. Below you can see a reference for how large a 320x320 area is against common resolutions.
@@ -34,7 +28,7 @@ For the Dahua/Loryta 5442 camera, I use the following settings:
 - Encode Mode: H.264
 - Resolution: 2688\*1520
 - Frame Rate(FPS): 15
-- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](/configuration/live#camera-settings-recommendations) for more info)
+- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](/configuration/live#camera_settings_recommendations) for more info)
 
 **Sub Stream (Detection)**
 

docs/docs/frigate/hardware.md

@@ -3,8 +3,6 @@ id: hardware
 title: Recommended hardware
 ---
 
-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 ## Cameras
 
 Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
@@ -20,13 +18,13 @@ Here are some of the cameras I recommend:
 - <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
 - <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
 - <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
-- <a href="https://www.bhphotovideo.com/c/product/1705511-REG/hikvision_colorvu_ds_2cd2387g2p_lsu_sl_8mp_network.html" target="_blank" rel="nofollow noopener">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
+- <a href="https://amzn.to/4ltOpaC" target="_blank" rel="nofollow noopener sponsored">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
 
 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 ## Server
 
-My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU (with AVX + AVX2 instructions) and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral, Hailo, or other AI accelerators.
+My current favorite is the Beelink EQ13 because of the efficient N100 CPU and dual NICs that allow you to setup a dedicated private network for your cameras where they can be blocked from accessing the internet. There are many used workstation options on eBay that work very well. Anything with an Intel CPU and capable of running Debian should work fine. As a bonus, you may want to look for devices with a M.2 or PCIe express slot that is compatible with the Google Coral, Hailo, or other AI accelerators.
 
 Note that many of these mini PCs come with Windows pre-installed, and you will need to install Linux according to the [getting started guide](../guides/getting_started.md).
 
@@ -38,11 +36,9 @@ If the EQ13 is out of stock, the link below may take you to a suggested alternat
 
 :::
 
-| Name                                                                                                          | Capabilities                                                               | Notes                                               |
-| ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
-| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | Can run object detection on several 1080p cameras with low-medium activity | Dual gigabit NICs for easy isolated camera network. |
-| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP))        | Can handle a large number of 1080p cameras with high activity              |                                                     |
-| Intel 125H ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM))     | Can handle a significant number of 1080p cameras with high activity        | Includes NPU for more efficient detection in 0.17+  |
+| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -55,13 +51,15 @@ Frigate supports multiple different detectors that work on different types of ha
 **Most Hardware**
 
 - [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.
+
   - [Supports many model architectures](../../configuration/object_detectors#configuration)
   - Runs best with tiny or small size models
 
 - [Google Coral EdgeTPU](#google-coral-tpu): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
+
   - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
 
-- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
+- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
   - [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
   - Runs best with tiny, small, or medium-size models
 
@@ -86,29 +84,32 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Nvidia**
 
-- [Nvidia GPU](#nvidia-gpus): Nvidia GPUs can provide efficient object detection.
+- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices.
   - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
   - Runs well with any size models including large
 
-- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
-
-**Rockchip** <CommunityBadge />
+**Rockchip**
 
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
-  - [Supports limited model architectures](../../configuration/object_detectors#rockchip-supported-models)
+  - [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
   - Runs best with tiny or small size models
   - Runs efficiently on low power hardware
 
-**Synaptics** <CommunityBadge />
+**Synaptics**
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
 
-**AXERA** <CommunityBadge />
-
-- [AXEngine](#axera): axera models can run on AXERA NPUs via AXEngine, delivering highly efficient object detection.
-
 :::
 
+### Synaptics
+
+- **Synaptics** Default model is **mobilenet**
+
+| Name             | Synaptics SL1680 Inference Time |
+| ---------------- | ------------------------------- |
+| ssd mobilenet    | ~ 25 ms                         |
+| yolov5m          | ~ 118 ms                        |
+
 ### Hailo-8
 
 Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
@@ -128,16 +129,10 @@ In real-world deployments, even with multiple cameras running concurrently, Frig
 
 ### Google Coral TPU
 
-:::warning
-
-The Coral is no longer recommended for new Frigate installations, except in deployments with particularly low power requirements or hardware incapable of utilizing alternative AI accelerators for object detection. Instead, we suggest using one of the numerous other supported object detectors. Frigate will continue to provide support for the Coral TPU for as long as practicably possible given its still one of the most power-efficient devices for executing object detection models.
-
-:::
-
 Frigate supports both the USB and M.2 versions of the Google Coral.
 
 - The USB version is compatible with the widest variety of hardware and does not require a driver on the host machine. However, it does lack the automatic throttling features of the other versions.
-- The PCIe and M.2 versions require installation of a driver on the host. https://github.com/jnicolson/gasket-builder should be used.
+- The PCIe and M.2 versions require installation of a driver on the host. Follow the instructions for your version from https://coral.ai
 
 A single Coral can handle many cameras using the default model and will be sufficient for the majority of users. You can calculate the maximum performance of your Coral based on the inference speed reported by Frigate. With an inference speed of 10, your Coral will top out at `1000/10=100`, or 100 frames per second. If your detection fps is regularly getting close to that, you should first consider tuning motion masks. If those are already properly configured, a second Coral may be needed.
 
@@ -146,11 +141,19 @@ A single Coral can handle many cameras using the default model and will be suffi
 The OpenVINO detector type is able to run on:
 
 - 6th Gen Intel Platforms and newer that have an iGPU
-- x86 hosts with an Intel Arc GPU (including Arc A-series and B-series Battlemage)
+- x86 hosts with an Intel Arc GPU
 - Intel NPUs
 - Most modern AMD CPUs (though this is officially not supported by Intel)
 - x86 & Arm64 hosts via CPU (generally not recommended)
 
+:::note
+
+Intel NPUs have seen [limited success in community deployments](https://github.com/blakeblackshear/frigate/discussions/13248#discussioncomment-12347357), although they remain officially unsupported.
+
+In testing, the NPU delivered performance that was only comparable to — or in some cases worse than — the integrated GPU.
+
+:::
+
 More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
 
 Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
@@ -160,17 +163,17 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
 | Intel HD 530   | 15 - 35 ms                 |                                                   |                           |                        | Can only run one detector instance |
 | Intel HD 620   | 15 - 25 ms                 |                                                   | 320: ~ 35 ms              |                        |                                    |
 | Intel HD 630   | ~ 15 ms                    |                                                   | 320: ~ 30 ms              |                        |                                    |
-| Intel UHD 730  | ~ 10 ms                    | t-320: 14ms s-320: 24ms t-640: 34ms s-640: 65ms   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
+| Intel UHD 730  | ~ 10 ms                    |                                                   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
 | Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
 | Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
 | Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
 | Intel Iris XE  | ~ 10 ms                    | t-320: 6 ms t-640: 14 ms s-320: 8 ms s-640: 16 ms | 320: ~ 10 ms 640: ~ 20 ms | 320-n: 33 ms           |                                    |
-| Intel NPU      | ~ 6 ms                     | s-320: 11 ms s-640: 30 ms                         | 320: ~ 14 ms 640: ~ 34 ms | 320-n: 40 ms           |                                    |
+| Intel NPU      | ~ 6 ms                     | s-320: 11 ms                                      | 320: ~ 14 ms 640: ~ 34 ms | 320-n: 40 ms           |                                    |
 | Intel Arc A310 | ~ 5 ms                     | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms | 320: ~ 8 ms 640: ~ 14 ms  |                        |                                    |
 | Intel Arc A380 | ~ 6 ms                     |                                                   | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms  |                                    |
 | Intel Arc A750 | ~ 4 ms                     |                                                   | 320: ~ 8 ms               |                        |                                    |
 
-### Nvidia GPUs
+### TensorRT - Nvidia GPU
 
 Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA libraries.
 
@@ -180,28 +183,29 @@ Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA
 
 Make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
 
+There are improved capabilities in newer GPU architectures that TensorRT can benefit from, such as INT8 operations and Tensor cores. The features compatible with your hardware will be optimized when the model is converted to a trt file. Currently the script provided for generating the model provides a switch to enable/disable FP16 operations. If you wish to use newer features such as INT8 optimization, more work is required.
+
 #### Compatibility References:
 
-[NVIDIA TensorRT Support Matrix](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/getting-started/support-matrix.html)
+[NVIDIA TensorRT Support Matrix](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-841/support-matrix/index.html)
 
 [NVIDIA CUDA Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html)
 
 [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus)
 
-Inference is done with the `onnx` detector type. Speeds will vary greatly depending on the GPU and the model used.
+Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny (t)` variants are faster than the equivalent non-tiny model, some known examples are below:
 
 ✅ - Accelerated with CUDA Graphs
 ❌ - Not accelerated with CUDA Graphs
 
-| Name        | ✅ YOLOv9 Inference Time              | ✅ RF-DETR Inference Time | ❌ YOLO-NAS Inference Time |
-| ----------- | ------------------------------------- | ------------------------- | -------------------------- |
-| GTX 1070    | s-320: 16 ms                          |                           | 320: 14 ms                 |
-| RTX 3050    | t-320: 8 ms s-320: 10 ms s-640: 28 ms | Nano-320: ~ 12 ms         | 320: ~ 10 ms 640: ~ 16 ms  |
-| RTX 3070    | t-320: 6 ms s-320: 8 ms s-640: 25 ms  | Nano-320: ~ 9 ms          | 320: ~ 8 ms 640: ~ 14 ms   |
-| RTX 5060 Ti | t-320: 5 ms s-320: 7 ms s-640: 22 ms  | Nano-320: ~ 4 ms          |                            |
-| RTX A4000   |                                       |                           | 320: ~ 15 ms               |
-| Tesla P40   |                                       |                           | 320: ~ 105 ms              |
+| Name      | ✅ YOLOv9 Inference Time              | ✅ RF-DETR Inference Time | ❌ YOLO-NAS Inference Time |
+| --------- | ------------------------------------- | ------------------------- | -------------------------- |
+| GTX 1070  | s-320: 16 ms                          |                           | 320: 14 ms                 |
+| RTX 3050  | t-320: 8 ms s-320: 10 ms s-640: 28 ms | Nano-320: ~ 12 ms         | 320: ~ 10 ms 640: ~ 16 ms  |
+| RTX 3070  | t-320: 6 ms s-320: 8 ms s-640: 25 ms  | Nano-320: ~ 9 ms          | 320: ~ 8 ms 640: ~ 14 ms   |
+| RTX A4000 |                                       |                           | 320: ~ 15 ms               |
+| Tesla P40 |                                       |                           | 320: ~ 105 ms              |
 
 ### Apple Silicon
 
@@ -257,7 +261,7 @@ Inference speeds may vary depending on the host platform. The above data was mea
 
 ### Nvidia Jetson
 
-Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
+Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
@@ -278,23 +282,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
-### Synaptics
-
-- **Synaptics** Default model is **mobilenet**
-
-| Name          | Synaptics SL1680 Inference Time |
-| ------------- | ------------------------------- |
-| ssd mobilenet | ~ 25 ms                         |
-| yolov5m       | ~ 118 ms                        |
-
-### AXERA
-
-- **AXEngine** Default model is **yolov9**
-
-| Name             | AXERA AX650N/AX8850N Inference Time |
-| ---------------- | ----------------------------------- |
-| yolov9-tiny      | ~ 4 ms                              |
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.

docs/docs/frigate/installation.md

@@ -3,13 +3,11 @@ id: installation
 title: Installation
 ---
 
-import ShmCalculator from '@site/src/components/ShmCalculator'
-
-Frigate is a Docker container that can be run on any Docker host including as a [Home Assistant App](https://www.home-assistant.io/apps/). Note that the Home Assistant App is **not** the same thing as the integration. The [integration](/integrations/home-assistant) is required to integrate Frigate into Home Assistant, whether you are running Frigate as a standalone Docker container or as a Home Assistant App.
+Frigate is a Docker container that can be run on any Docker host including as a [Home Assistant Add-on](https://www.home-assistant.io/addons/). Note that the Home Assistant Add-on is **not** the same thing as the integration. The [integration](/integrations/home-assistant) is required to integrate Frigate into Home Assistant, whether you are running Frigate as a standalone Docker container or as a Home Assistant Add-on.
 
 :::tip
 
-If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
+If you already have Frigate installed as a Home Assistant Add-on, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
 
 :::
 
@@ -58,7 +56,7 @@ services:
     volumes:
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
+      - type: tmpfs # Recommended: 1GB of memory
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -79,13 +77,22 @@ The default shm size of **128MB** is fine for setups with **2 cameras** detectin
 
 The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
 
-<ShmCalculator/>
+You can calculate the **minimum** shm size for each camera with the following formula using the resolution specified for detect:
 
-The shm size cannot be set per container for Home Assistant Apps. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
+```console
+# Template for one camera without logs, replace <width> and <height>
+$ python -c 'print("{:.2f}MB".format((<width> * <height> * 1.5 * 20 + 270480) / 1048576))'
 
-## Extra Steps for Specific Hardware
+# Example for 1280x720, including logs
+$ python -c 'print("{:.2f}MB".format((1280 * 720 * 1.5 * 20 + 270480) / 1048576 + 40))'
+66.63MB
 
-The following sections contain additional setup steps that are only required if you are using specific hardware. If you are not using any of these hardware types, you can skip to the [Docker](#docker) installation section.
+# Example for eight cameras detecting at 1280x720, including logs
+$ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576) * 8 + 40))'
+253MB
+```
+
+The shm size cannot be set per container for Home Assistant add-ons. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
 
 ### Raspberry Pi 3/4
 
@@ -99,162 +106,14 @@ The Hailo-8 and Hailo-8L AI accelerators are available in both M.2 and HAT form
 
 #### Installation
 
-:::warning
+For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simply follow this [guide](https://www.raspberrypi.com/documentation/accessories/ai-kit.html#ai-kit-installation) to install the driver and software.
 
-On Raspberry Pi OS **Bookworm**, the kernel includes an older version of the Hailo driver that is incompatible with Frigate. You **must** follow the installation steps below to install the correct driver version, and you **must** disable the built-in kernel driver as described in step 1.
+For other installations, follow these steps for installation:
 
-On Raspberry Pi OS **Trixie**, the Hailo driver is no longer shipped with the kernel. It is installed via DKMS, and the conflict described below does not apply. You can simply run the installation script.
-
-:::
-
-1. **Disable the built-in Hailo driver (Raspberry Pi Bookworm OS only)**:
-
-   :::note
-
-   If you are **not** using a Raspberry Pi with **Bookworm OS**, skip this step and proceed directly to step 2.
-
-   If you are using Raspberry Pi with **Trixie OS**, also skip this step and proceed directly to step 2.
-
-   :::
-
-   First, check if the driver is currently loaded:
-
-   ```bash
-   lsmod | grep hailo
-   ```
-
-   If it shows `hailo_pci`, unload it:
-
-   ```bash
-   sudo modprobe -r hailo_pci
-   ```
-
-   Then locate the built-in kernel driver and rename it so it cannot be loaded.
-   Renaming allows the original driver to be restored later if needed.
-   First, locate the currently installed kernel module:
-
-   ```bash
-   modinfo -n hailo_pci
-   ```
-
-   Example output:
-
-   ```
-   /lib/modules/6.6.31+rpt-rpi-2712/kernel/drivers/media/pci/hailo/hailo_pci.ko.xz
-   ```
-
-   Save the module path to a variable:
-
-   ```bash
-   BUILTIN=$(modinfo -n hailo_pci)
-   ```
-
-   And rename the module by appending .bak:
-
-   ```bash
-   sudo mv "$BUILTIN" "${BUILTIN}.bak"
-   ```
-
-   Now refresh the kernel module map so the system recognizes the change:
-
-   ```bash
-   sudo depmod -a
-   ```
-
-   Reboot your Raspberry Pi:
-
-   ```bash
-   sudo reboot
-   ```
-
-   After rebooting, verify the built-in driver is not loaded:
-
-   ```bash
-   lsmod | grep hailo
-   ```
-
-   This command should return no results.
-
-2. **Run the installation script**:
-
-   Download the installation script:
-
-   ```bash
-   wget https://raw.githubusercontent.com/blakeblackshear/frigate/dev/docker/hailo8l/user_installation.sh
-   ```
-
-   Make it executable:
-
-   ```bash
-   sudo chmod +x user_installation.sh
-   ```
-
-   Run the script:
-
-   ```bash
-   ./user_installation.sh
-   ```
-
-   The script will:
-   - Install necessary build dependencies
-   - Clone and build the Hailo driver from the official repository
-   - Install the driver
-   - Download and install the required firmware
-   - Set up udev rules
-
-3. **Reboot your system**:
-
-   After the script completes successfully, reboot to load the firmware:
-
-   ```bash
-   sudo reboot
-   ```
-
-4. **Verify the installation**:
-
-   After rebooting, verify that the Hailo device is available:
-
-   ```bash
-   ls -l /dev/hailo0
-   ```
-
-   You should see the device listed. You can also verify the driver is loaded:
-
-   ```bash
-   lsmod | grep hailo_pci
-   ```
-
-   Verify the driver version:
-
-   ```bash
-   cat /sys/module/hailo_pci/version
-   ```
-
-   Verify that the firmware was installed correctly:
-
-   ```bash
-   ls -l /lib/firmware/hailo/hailo8_fw.bin
-   ```
-
-   **Optional: Fix PCIe descriptor page size error**
-
-   If you encounter the following error:
-
-   ```
-   [HailoRT] [error] CHECK failed - max_desc_page_size given 16384 is bigger than hw max desc page size 4096
-   ```
-
-   Create a configuration file to force the correct descriptor page size:
-
-   ```bash
-   echo 'options hailo_pci force_desc_page_size=4096' | sudo tee /etc/modprobe.d/hailo_pci.conf
-   ```
-
-   and reboot:
-
-   ```bash
-   sudo reboot
-   ```
+1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
+2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
+3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
+4. Run the script with `./user_installation.sh`
 
 #### Setup
 
@@ -271,12 +130,11 @@ If you are using `docker run`, add this option to your command `--device /dev/ha
 
 #### Configuration
 
-Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8) to complete the setup.
+Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8l) to complete the setup.
 
 ### MemryX MX3
 
 The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVMe SSD), and supports a variety of configurations:
-
 - x86 (Intel/AMD) PCs
 - Raspberry Pi 5
 - Orange Pi 5 Plus/Max
@@ -284,6 +142,7 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
 
 #### Configuration
 
+
 #### Installation
 
 To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/get_started/hardware_setup.html).
@@ -297,7 +156,7 @@ Then follow these steps for installing the correct driver/runtime configuration:
 
 #### Setup
 
-To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
+To set up Frigate, follow the default installation instructions, for example:   `ghcr.io/blakeblackshear/frigate:stable`
 
 Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
 
@@ -314,7 +173,7 @@ In your `docker-compose.yml`, also add:
 privileged: true
 
 volumes:
-  - /run/mxa_manager:/run/mxa_manager
+    /run/mxa_manager:/run/mxa_manager
 ```
 
 If you can't use Docker Compose, you can run the container with something similar to this:
@@ -428,42 +287,6 @@ or add these options to your `docker run` command:
 
 Next, you should configure [hardware object detection](/configuration/object_detectors#synaptics) and [hardware video processing](/configuration/hardware_acceleration_video#synaptics).
 
-### AXERA
-
-AXERA accelerators are available in an M.2 form factor, compatible with both Raspberry Pi and Orange Pi. This form factor has also been successfully tested on x86 platforms, making it a versatile choice for various computing environments.
-
-#### Installation
-
-Using AXERA accelerators requires the installation of the AXCL driver. We provide a convenient Linux script to complete this installation.
-
-Follow these steps for installation:
-
-1. Copy or download [this script](https://github.com/ivanshi1108/assets/releases/download/v0.16.2/user_installation.sh).
-2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
-3. Run the script with `./user_installation.sh`
-
-#### Setup
-
-To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
-
-Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
-
-```yaml
-devices:
-  - /dev/axcl_host
-  - /dev/ax_mmb_dev
-  - /dev/msg_userdev
-volumes:
-  - /usr/bin/axcl:/usr/bin/axcl
-  - /usr/lib/axcl:/usr/lib/axcl
-```
-
-If you are using `docker run`, add this option to your command `--device /dev/axcl_host --device /dev/ax_mmb_dev --device /dev/msg_userdev`
-
-#### Configuration
-
-Finally, configure [hardware object detection](/configuration/object_detectors#axera) to complete the setup.
-
 ## Docker
 
 Running through Docker with Docker Compose is the recommended install method.
@@ -479,16 +302,15 @@ services:
     shm_size: "512mb" # update for your cameras based on calculation above
     devices:
       - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
-      - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://github.com/jnicolson/gasket-builder
+      - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
       - /dev/video11:/dev/video11 # For Raspberry Pi 4B
       - /dev/dri/renderD128:/dev/dri/renderD128 # AMD / Intel GPU, needs to be updated for your hardware
-      - /dev/kfd:/dev/kfd # AMD Kernel Fusion Driver for ROCm
-      - /dev/accel:/dev/accel # AMD / Intel NPU
+      - /dev/accel:/dev/accel # Intel NPU
     volumes:
       - /etc/localtime:/etc/localtime:ro
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
+      - type: tmpfs # Recommended: 1GB of memory
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -528,15 +350,15 @@ The official docker image tags for the current stable version are:
 
 - `stable` - Standard Frigate build for amd64 & RPi Optimized Frigate build for arm64. This build includes support for Hailo devices as well.
 - `stable-standard-arm64` - Standard Frigate build for arm64
-- `stable-tensorrt` - Frigate build specific for amd64 devices running an Nvidia GPU
+- `stable-tensorrt` - Frigate build specific for amd64 devices running an nvidia GPU
 - `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
 
 The community supported docker image tags for the current stable version are:
 
-- `stable-tensorrt-jp6` - Frigate build optimized for Nvidia Jetson devices running Jetpack 6
+- `stable-tensorrt-jp6` - Frigate build optimized for nvidia Jetson devices running Jetpack 6
 - `stable-rk` - Frigate build for SBCs with Rockchip SoC
 
-## Home Assistant App
+## Home Assistant Add-on
 
 :::warning
 
@@ -546,8 +368,7 @@ There are important limitations in HA OS to be aware of:
 
 - Separate local storage for media is not yet supported by Home Assistant
 - AMD GPUs are not supported because HA OS does not include the mesa driver.
-- Intel NPUs are not supported because HA OS does not include the NPU firmware.
-- Nvidia GPUs are not supported because HA Apps do not support the Nvidia runtime.
+- Nvidia GPUs are not supported because addons do not support the nvidia runtime.
 
 :::
 
@@ -557,27 +378,27 @@ See [the network storage guide](/guides/ha_network_storage.md) for instructions
 
 :::
 
-Home Assistant OS users can install via the App repository.
+Home Assistant OS users can install via the Add-on repository.
 
-1. In Home Assistant, navigate to _Settings_ > _Apps_ > _App Store_ > _Repositories_
+1. In Home Assistant, navigate to _Settings_ > _Add-ons_ > _Add-on Store_ > _Repositories_
 2. Add `https://github.com/blakeblackshear/frigate-hass-addons`
-3. Install the desired variant of the Frigate App (see below)
+3. Install the desired variant of the Frigate Add-on (see below)
 4. Setup your network configuration in the `Configuration` tab
-5. Start the App
+5. Start the Add-on
 6. Use the _Open Web UI_ button to access the Frigate UI, then click in the _cog icon_ > _Configuration editor_ and configure Frigate to your liking
 
-There are several variants of the App available:
+There are several variants of the Add-on available:
 
-| App Variant                | Description                                                |
+| Add-on Variant             | Description                                                |
 | -------------------------- | ---------------------------------------------------------- |
 | Frigate                    | Current release with protection mode on                    |
 | Frigate (Full Access)      | Current release with the option to disable protection mode |
 | Frigate Beta               | Beta release with protection mode on                       |
 | Frigate Beta (Full Access) | Beta release with the option to disable protection mode    |
 
-If you are using hardware acceleration for ffmpeg, you **may** need to use the _Full Access_ variant of the App. This is because the Frigate App runs in a container with limited access to the host system. The _Full Access_ variant allows you to disable _Protection mode_ and give Frigate full access to the host system.
+If you are using hardware acceleration for ffmpeg, you **may** need to use the _Full Access_ variant of the Add-on. This is because the Frigate Add-on runs in a container with limited access to the host system. The _Full Access_ variant allows you to disable _Protection mode_ and give Frigate full access to the host system.
 
-You can also edit the Frigate configuration file through the [VS Code App](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate App you are running. See the list of directories [here](../configuration/index.md#accessing-app-config-dir).
+You can also edit the Frigate configuration file through the [VS Code Add-on](https://github.com/hassio-addons/addon-vscode) or similar. In that case, the configuration file will be at `/addon_configs/<addon_directory>/config.yml`, where `<addon_directory>` is specific to the variant of the Frigate Add-on you are running. See the list of directories [here](../configuration/index.md#accessing-add-on-config-dir).
 
 ## Kubernetes
 
@@ -590,7 +411,7 @@ To install make sure you have the [community app plugin here](https://forums.unr
 
 ## Proxmox
 
-[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers. Ensure that ballooning is **disabled**, especially if you are passing through a GPU to the VM.
+[According to Proxmox documentation](https://pve.proxmox.com/pve-docs/pve-admin-guide.html#chapter_pct) it is recommended that you run application containers like Frigate inside a Proxmox QEMU VM. This will give you all the advantages of application containerization, while also providing the benefits that VMs offer, such as strong isolation from the host and the ability to live-migrate, which otherwise isn’t possible with containers.
 
 :::warning
 
@@ -715,43 +536,3 @@ docker run \
 ```
 
 Log into QNAP, open Container Station. Frigate docker container should be listed under 'Overview' and running. Visit Frigate Web UI by clicking Frigate docker, and then clicking the URL shown at the top of the detail page.
-
-## macOS - Apple Silicon
-
-:::warning
-
-macOS uses port 5000 for its Airplay Receiver service. If you want to expose port 5000 in Frigate for local app and API access the port will need to be mapped to another port on the host e.g. 5001
-
-Failure to remap port 5000 on the host will result in the WebUI and all API endpoints on port 5000 being unreachable, even if port 5000 is exposed correctly in Docker.
-
-:::
-
-Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
-
-To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
-
-#### Docker Compose example
-
-```yaml
-services:
-  frigate:
-    container_name: frigate
-    image: ghcr.io/blakeblackshear/frigate:stable-standard-arm64
-    restart: unless-stopped
-    shm_size: "512mb" # update for your cameras based on calculation above
-    volumes:
-      - /etc/localtime:/etc/localtime:ro
-      - /path/to/your/config:/config
-      - /path/to/your/recordings:/recordings
-    ports:
-      - "8971:8971"
-      # If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts
-      # - "5001:5000" # Internal unauthenticated access. Expose carefully.
-      - "8554:8554" # RTSP feeds
-    extra_hosts:
-      # This is very important
-      # It allows frigate access to the NPU on Apple Silicon via Apple Silicon Detector
-      - "host.docker.internal:host-gateway" # Required to talk to the NPU detector
-    environment:
-      - FRIGATE_RTSP_PASSWORD: "password"
-```

docs/docs/frigate/network_requirements.md

@@ -1,155 +0,0 @@
----
-id: network_requirements
-title: Network Requirements
----
-
-# Network Requirements
-
-Frigate is designed to run locally and does not require a persistent internet connection for core functionality. However, certain features need internet access for initial setup or ongoing operation. This page describes what connects to the internet, when, and how to control it.
-
-## How Frigate Uses the Internet
-
-Frigate's internet usage falls into three categories:
-
-1. **One-time model downloads** — ML models are downloaded the first time a feature is enabled, then cached locally. No internet is needed on subsequent startups.
-2. **Optional cloud services** — Features like Frigate+ and Generative AI connect to external APIs only when explicitly configured.
-3. **Build-time dependencies** — Components bundled into the Docker image during the build process. These require no internet at runtime.
-
-:::tip
-
-After initial setup, Frigate can run fully offline as long as all required models have been downloaded and no cloud-dependent features are enabled.
-
-:::
-
-## One-Time Model Downloads
-
-The following models are downloaded automatically the first time their associated feature is enabled. Once cached in `/config/model_cache/`, they do not require internet again.
-
-| Feature                                                                                       | Models Downloaded                                                          | Source               |
-| --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | -------------------- |
-| [Semantic search](/configuration/semantic_search)                                             | Jina CLIP v1 or v2 (ONNX) + tokenizer                                      | HuggingFace          |
-| [Face recognition](/configuration/face_recognition)                                           | FaceNet, ArcFace, face detection model                                     | GitHub               |
-| [License plate recognition](/configuration/license_plate_recognition)                         | PaddleOCR (detection, classification, recognition) + YOLOv9 plate detector | GitHub               |
-| [Bird classification](/configuration/bird_classification)                                     | MobileNetV2 bird model + label map                                         | GitHub               |
-| [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
-| [Audio transcription](/configuration/advanced)                                                | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
-
-### Hardware-Specific Detector Models
-
-If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
-
-| Detector                                                           | Model Downloaded     | Source                   |
-| ------------------------------------------------------------------ | -------------------- | ------------------------ |
-| [Rockchip RKNN](/configuration/object_detectors#rockchip-platform) | RKNN detection model | GitHub                   |
-| [Hailo 8 / 8L](/configuration/object_detectors#hailo-8)            | YOLOv6n (.hef)       | Hailo Model Zoo (AWS S3) |
-| [AXERA AXEngine](/configuration/object_detectors)                  | Detection model      | HuggingFace              |
-
-:::note
-
-The default CPU, EdgeTPU, and OpenVINO object detection models are bundled into the Docker image and do not require any download at runtime.
-
-:::
-
-### Preventing Model Downloads
-
-If you have already downloaded all required models and want to prevent Frigate from attempting any outbound connections to HuggingFace or the Transformers library, set the following environment variables on your Frigate container:
-
-```yaml
-environment:
-  HF_HUB_OFFLINE: "1"
-  TRANSFORMERS_OFFLINE: "1"
-```
-
-:::warning
-
-Setting these variables without having the correct model files already cached in `/config/model_cache/` will cause failures. Only use these after a successful initial setup with internet access.
-
-:::
-
-### Mirror Support
-
-If your Frigate instance has restricted internet access, you can point model downloads at internal mirrors using environment variables:
-
-| Environment Variable                | Default                             | Used By                                       |
-| ----------------------------------- | ----------------------------------- | --------------------------------------------- |
-| `HF_ENDPOINT`                       | `https://huggingface.co`            | Semantic search, Sherpa-ONNX, AXEngine models |
-| `GITHUB_ENDPOINT`                   | `https://github.com`                | Face recognition, LPR, RKNN models            |
-| `GITHUB_RAW_ENDPOINT`               | `https://raw.githubusercontent.com` | Bird classification                           |
-| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default)      | Custom classification training                |
-
-## Optional Cloud Services
-
-These features connect to external services during normal operation and require internet whenever they are active.
-
-### Frigate+
-
-When a Frigate+ API key is configured, Frigate communicates with `https://api.frigate.video` to download models, upload snapshots for training, submit annotations, and report false positives. Remove the API key to disable all Frigate+ network activity.
-
-See [Frigate+](/integrations/plus) for details.
-
-### Generative AI
-
-When a Generative AI provider is configured, Frigate sends images and prompts to the configured provider for event descriptions, chat, and camera monitoring. Available providers:
-
-| Provider      | Internet Required                                                |
-| ------------- | ---------------------------------------------------------------- |
-| OpenAI        | Yes — connects to OpenAI API (or custom base URL)                |
-| Google Gemini | Yes — connects to Google Generative AI API                       |
-| Azure OpenAI  | Yes — connects to your Azure endpoint                            |
-| Ollama        | Depends — typically local (`localhost:11434`), but can be remote |
-| llama.cpp     | No — runs entirely locally                                       |
-
-Disable Generative AI by removing the `genai` configuration from your cameras. See [Generative AI](/configuration/genai/genai_config) for details.
-
-### Version Check
-
-Frigate checks GitHub for the latest release version on startup by querying `https://api.github.com`. This can be disabled:
-
-```yaml
-telemetry:
-  version_check: false
-```
-
-### Push Notifications
-
-When [notifications](/configuration/notifications) are enabled and users have registered for push notifications in the web UI, Frigate sends push messages through the browser vendor's push service (e.g., Google FCM, Mozilla autopush). This requires internet access from the Frigate server to these push endpoints.
-
-### MQTT
-
-If an [MQTT broker](/integrations/mqtt) is configured, Frigate maintains a connection to the broker's host and port. This is typically a local network connection, but will require internet if you use a cloud-hosted MQTT broker.
-
-### DeepStack / CodeProject.AI
-
-When using the [DeepStack detector plugin](/configuration/object_detectors), Frigate sends images to the configured API endpoint for inference. This is typically local but depends on where the service is hosted.
-
-## WebRTC (STUN)
-
-For [WebRTC live streaming](/configuration/live), Frigate uses STUN for NAT traversal:
-
-- **go2rtc** defaults to a local STUN listener (`stun:8555`) — no internet required.
-- **The web UI's WebRTC player** includes a fallback to Google's public STUN server (`stun:stun.l.google.com:19302`), which requires internet.
-
-## Home Assistant Supervisor
-
-When running as a Home Assistant add-on, the go2rtc startup script queries the local Supervisor API (`http://supervisor/`) to discover the host IP address and WebRTC port. This is a local network call to the Home Assistant host, not an internet connection.
-
-## What Does NOT Require Internet
-
-- **Object detection** — CPU, EdgeTPU, OpenVINO, and other bundled detector models are included in the Docker image.
-- **Recording and playback** — All video is stored and served locally.
-- **Live streaming** — Camera streams are pulled over your local network. MSE and HLS streaming work without any external connections.
-- **The web interface** — Fully self-contained with no external fonts, scripts, analytics, or CDN dependencies. All translations are bundled locally.
-- **Custom classification inference** — After training, custom models run entirely locally.
-- **Audio detection** — The YAMNet audio classification model is bundled in the Docker image.
-
-## Running Frigate Offline
-
-To run Frigate in an air-gapped or offline environment:
-
-1. **Pre-download models** — Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check** — Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests** — Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features** — Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors** — If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
-
-After these steps, Frigate will operate with no outbound internet connections.

docs/docs/frigate/planning_setup.md

@@ -34,14 +34,11 @@ For commercial installations it is important to verify the number of supported c
 
 There are many different hardware options for object detection depending on priorities and available hardware. See [the recommended hardware page](./hardware.md#detectors) for more specifics on what hardware is recommended for object detection.
 
-### CPU
-
-Frigate requires a CPU with AVX + AVX2 instructions. Most modern CPUs (post-2011) support AVX and AVX2, but it is generally absent in low-power or budget-oriented processors, particularly older Intel Pentium, Celeron, and Atom-based chips. Specifically, Intel Celeron and Pentium models prior to the 2020 Tiger Lake generation typically lack AVX. Older Intel Xeon models may have AVX, but may lack AVX2.
-
 ### Storage
 
 Storage is an important consideration when planning a new installation. To get a more precise estimate of your storage requirements, you can use an IP camera storage calculator. Websites like [IPConfigure Storage Calculator](https://calculator.ipconfigure.com/) can help you determine the necessary disk space based on your camera settings.
 
+
 #### SSDs (Solid State Drives)
 
 SSDs are an excellent choice for Frigate, offering high speed and responsiveness. The older concern that SSDs would quickly "wear out" from constant video recording is largely no longer valid for modern consumer and enterprise-grade SSDs.
@@ -74,4 +71,4 @@ While supported, using network-attached storage (NAS) for recordings can introdu
 
 - **Basic Minimum: 4GB RAM**: This is generally sufficient for a very basic Frigate setup with a few cameras and a dedicated object detection accelerator, without running any enrichments. Performance might be tight, especially with higher resolution streams or numerous detections.
 - **Minimum for Enrichments: 8GB RAM**: If you plan to utilize Frigate's enrichment features (e.g., facial recognition, license plate recognition, or other AI models that run alongside standard object detection), 8GB of RAM should be considered the minimum. Enrichments require additional memory to load and process their respective models and data.
-- **Recommended: 16GB RAM**: For most users, especially those with many cameras (8+) or who plan to heavily leverage enrichments, 16GB of RAM is highly recommended. This provides ample headroom for smooth operation, reduces the likelihood of swapping to disk (which can impact performance), and allows for future expansion.
+- **Recommended: 16GB RAM**: For most users, especially those with many cameras (8+) or who plan to heavily leverage enrichments, 16GB of RAM is highly recommended. This provides ample headroom for smooth operation, reduces the likelihood of swapping to disk (which can impact performance), and allows for future expansion.

docs/docs/frigate/updating.md

@@ -5,9 +5,9 @@ title: Updating
 
 # Updating Frigate
 
-The current stable version of Frigate is **0.18.0**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.18.0).
+The current stable version of Frigate is **0.16.2**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.2).
 
-Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant App, etc.). Below are instructions for the most common setups.
+Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
 
 ## Before You Begin
 
@@ -20,6 +20,7 @@ Keeping Frigate up to date ensures you benefit from the latest features, perform
 If you’re running Frigate via Docker (recommended method), follow these steps:
 
 1. **Stop the Container**:
+
    - If using Docker Compose:
      ```bash
      docker compose down frigate
@@ -30,25 +31,27 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
      ```
 
 2. **Update and Pull the Latest Image**:
+
    - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.18.0` instead of `0.17.1`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
        ```yaml
        services:
          frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.18.0
+           image: ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - Then pull the image:
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.18.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
    - If using `docker run`:
-     - Pull the image with the appropriate tag (e.g., `0.18.0`, `0.18.0-tensorrt`, or `stable`):
+     - Pull the image with the appropriate tag (e.g., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.18.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
        ```
 
 3. **Start the Container**:
+
    - If using Docker Compose:
      ```bash
      docker compose up -d
@@ -67,31 +70,33 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
 - If you’ve customized other settings (e.g., `shm-size`), ensure they’re still appropriate after the update.
 - Docker will automatically use the updated image when you restart the container, as long as you pulled the correct version.
 
-## Updating the Home Assistant App (formerly Addon)
+## Updating the Home Assistant Addon
 
-For users running Frigate as a Home Assistant App:
+For users running Frigate as a Home Assistant Addon:
 
 1. **Check for Updates**:
-   - Navigate to **Settings > Apps** in Home Assistant.
-   - Find your installed Frigate app (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
+
+   - Navigate to **Settings > Add-ons** in Home Assistant.
+   - Find your installed Frigate addon (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
    - If an update is available, you’ll see an "Update" button.
 
-2. **Update the App**:
-   - Make a backup of the current version of the app.
-   - Click the "Update" button next to the Frigate app.
+2. **Update the Addon**:
+
+   - Click the "Update" button next to the Frigate addon.
    - Wait for the process to complete. Home Assistant will handle downloading and installing the new version.
 
-3. **Restart the App**:
-   - After updating, go to the app’s page and click "Restart" to apply the changes.
+3. **Restart the Addon**:
+
+   - After updating, go to the addon’s page and click "Restart" to apply the changes.
 
 4. **Verify the Update**:
-   - Check the app logs (under the "Log" tab) to ensure Frigate starts without errors.
+   - Check the addon logs (under the "Log" tab) to ensure Frigate starts without errors.
    - Access the Frigate Web UI to confirm the new version is running.
 
 ### Notes
 
 - Ensure your `/config/frigate.yml` is compatible with the new version by reviewing the [Release notes](https://github.com/blakeblackshear/frigate/releases).
-- If using custom hardware (e.g., Coral or GPU), verify that configurations still work, as app updates don’t modify your hardware settings.
+- If using custom hardware (e.g., Coral or GPU), verify that configurations still work, as addon updates don’t modify your hardware settings.
 
 ## Rolling Back
 
@@ -100,9 +105,9 @@ If an update causes issues:
 1. Stop Frigate.
 2. Restore your backed-up config file and database.
 3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.17.1`) in your `docker run` command.
-   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.16.4`), and re-run `docker compose up -d`.
-   - For Home Assistant: Restore from the app/addon backup you took before you updated.
+   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`) in your `docker run` command.
+   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`), and re-run `docker compose up -d`.
+   - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
 4. Verify the old version is running again.
 
 ## Troubleshooting

docs/docs/frigate/video_pipeline.md

@@ -37,18 +37,18 @@ The following diagram adds a lot more detail than the simple view explained befo
 %%{init: {"themeVariables": {"edgeLabelBackground": "transparent"}}}%%
 
 flowchart TD
-    RecStore[(Recording<br>store)]
-    SnapStore[(Snapshot<br>store)]
+    RecStore[(Recording\nstore)]
+    SnapStore[(Snapshot\nstore)]
 
     subgraph Acquisition
         Cam["Camera"] -->|FFmpeg supported| Stream
-        Cam -->|"Other streaming<br>protocols"| go2rtc
+        Cam -->|"Other streaming\nprotocols"| go2rtc
         go2rtc("go2rtc") --> Stream
-        Stream[Capture main and<br>sub streams] --> |detect stream|Decode(Decode and<br>downscale)
+        Stream[Capture main and\nsub streams] --> |detect stream|Decode(Decode and\ndownscale)
     end
     subgraph Motion
-        Decode --> MotionM(Apply<br>motion masks)
-        MotionM --> MotionD(Motion<br>detection)
+        Decode --> MotionM(Apply\nmotion masks)
+        MotionM --> MotionD(Motion\ndetection)
     end
     subgraph Detection
         MotionD --> |motion regions| ObjectD(Object detection)
@@ -60,8 +60,8 @@ flowchart TD
     MotionD --> |motion event|Birdseye
     ObjectZ --> |object event|Birdseye
 
-    MotionD --> |"video segments<br>(retain motion)"|RecStore
+    MotionD --> |"video segments\n(retain motion)"|RecStore
     ObjectZ --> |detection clip|RecStore
-    Stream -->|"video segments<br>(retain all)"| RecStore
+    Stream -->|"video segments\n(retain all)"| RecStore
     ObjectZ --> |detection snapshot|SnapStore
 ```

docs/docs/guides/configuring_go2rtc.md

@@ -11,13 +11,13 @@ Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect
 
 ## Setup a go2rtc stream
 
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. 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/v1.9.13#module-streams), not just rtsp.
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. 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/v1.9.10#module-streams), not just rtsp.
 
 :::tip
 
 For the best experience, you should 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.md#setting-streams-for-live-ui) for more information.
+See [the live view docs](../configuration/live.md#setting-stream-for-live-ui) for more information.
 
 :::
 
@@ -33,19 +33,22 @@ After adding this to the config, restart Frigate and try to watch the live strea
 ### What if my video doesn't play?
 
 - Check Logs:
+
   - Access the go2rtc logs in the Frigate UI under Logs in the sidebar.
   - If go2rtc is having difficulty connecting to your camera, you should see some error messages in the log.
 
 - Check go2rtc Web Interface: if you don't see any errors in the logs, try viewing the camera through go2rtc's web interface.
+
   - Navigate to port 1984 in your browser to access go2rtc's web interface.
     - If using Frigate through Home Assistant, enable the web interface at port 1984.
     - If using Docker, forward port 1984 before accessing the web interface.
   - Click `stream` for the specific camera to see if the camera's stream is being received.
 
 - Check Video Codec:
+
   - If the camera stream works in go2rtc but not in your browser, the video codec might be unsupported.
-  - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#codecs-madness) in go2rtc documentation.
-  - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.13#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
+  - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#codecs-madness) in go2rtc documentation.
+  - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
     ```yaml
     go2rtc:
       streams:
@@ -55,6 +58,7 @@ After adding this to the config, restart Frigate and try to watch the live strea
     ```
 
 - Switch to FFmpeg if needed:
+
   - Some camera streams may need to use the ffmpeg module in go2rtc. This has the downside of slower startup times, but has compatibility with more stream types.
 
     ```yaml
@@ -97,9 +101,9 @@ After adding this to the config, restart Frigate and try to watch the live strea
 
 :::warning
 
-To access the go2rtc stream externally when utilizing the Frigate App (for
+To access the go2rtc stream externally when utilizing the Frigate Add-On (for
 instance through VLC), you must first enable the RTSP Restream port.
-You can do this by visiting the Frigate App configuration page within Home
+You can do this by visiting the Frigate Add-On configuration page within Home
 Assistant and revealing the hidden options under the "Show disabled ports"
 section.
 
@@ -109,8 +113,7 @@ section.
 
 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.
 
 ## Homekit Configuration
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.

docs/docs/guides/getting_started.md

@@ -3,17 +3,13 @@ id: getting_started
 title: Getting started
 ---
 
-import ConfigTabs from "@site/src/components/ConfigTabs";
-import TabItem from "@theme/TabItem";
-import NavPath from "@site/src/components/NavPath";
-
 # Getting Started
 
 :::tip
 
 If you already have an environment with Linux and Docker installed, you can continue to [Installing Frigate](#installing-frigate) below.
 
-If you already have Frigate installed through Docker or through a Home Assistant App, you can continue to [Configuring Frigate](#configuring-frigate) below.
+If you already have Frigate installed through Docker or through a Home Assistant Add-on, you can continue to [Configuring Frigate](#configuring-frigate) below.
 
 :::
 
@@ -85,11 +81,11 @@ Now you have a minimal Debian server that requires very little maintenance.
 
 ## Installing Frigate
 
-This section shows how to create a minimal directory structure for a Docker installation on Debian. If you have installed Frigate as a Home Assistant App or another way, you can continue to [Configuring Frigate](#configuring-frigate).
+This section shows how to create a minimal directory structure for a Docker installation on Debian. If you have installed Frigate as a Home Assistant Add-on or another way, you can continue to [Configuring Frigate](#configuring-frigate).
 
 ### Setup directories
 
-Frigate will create a config file if one does not exist on the initial startup. The following directory structure is the bare minimum to get started.
+Frigate will create a config file if one does not exist on the initial startup. The following directory structure is the bare minimum to get started. Once Frigate is running, you can use the built-in config editor which supports config validation.
 
 ```
 .
@@ -123,7 +119,7 @@ services:
     volumes:
       - ./config:/config
       - ./storage:/media/frigate
-      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -132,48 +128,56 @@ services:
       - "8554:8554" # RTSP feeds
 ```
 
-Now you should be able to start Frigate by running `docker compose up -d` from within the folder containing `docker-compose.yml`. On startup, an admin user and password will be created and outputted in the logs. You can see this by running `docker logs frigate`. Frigate should now be accessible at `https://server_ip:8971` where you can login with the `admin` user and finish configuration using the Settings UI.
+Now you should be able to start Frigate by running `docker compose up -d` from within the folder containing `docker-compose.yml`. On startup, an admin user and password will be created and outputted in the logs. You can see this by running `docker logs frigate`. Frigate should now be accessible at `https://server_ip:8971` where you can login with the `admin` user and finish the configuration using the built-in configuration editor.
 
 ## Configuring Frigate
 
 This section assumes that you already have an environment setup as described in [Installation](../frigate/installation.md). You should also configure your cameras according to the [camera setup guide](/frigate/camera_setup). Pay particular attention to the section on choosing a detect resolution.
 
-### Step 1: Start Frigate
+### Step 1: Add a detect stream
 
-At this point you should be able to start Frigate and a basic config will be created automatically.
+First we will add the detect stream for the camera:
 
-### Step 2: Add a camera
+```yaml
+mqtt:
+  enabled: False
 
-Click the **Add Camera** button in <NavPath path="Settings > Camera configuration > Management" /> to use the camera setup wizard to get your first camera added into Frigate.
+cameras:
+  name_of_your_camera: # <------ Name the camera
+    enabled: True
+    ffmpeg:
+      inputs:
+        - path: rtsp://10.0.10.10:554/rtsp # <----- The stream you want to use for detection
+          roles:
+            - detect
+```
+
+### Step 2: Start Frigate
+
+At this point you should be able to start Frigate and see the video feed in the UI.
+
+If you get an error image from the camera, this means ffmpeg was not able to get the video feed from your camera. Check the logs for error messages from ffmpeg. The default ffmpeg arguments are designed to work with H264 RTSP cameras that support TCP connections.
+
+FFmpeg arguments for other types of cameras can be found [here](../configuration/camera_specific.md).
 
 ### Step 3: Configure hardware acceleration (recommended)
 
-Now that you have a working camera configuration, set up hardware acceleration to minimize the CPU required to decode your video streams. See the [hardware acceleration](../configuration/hardware_acceleration_video.md) docs for examples applicable to your hardware.
+Now that you have a working camera configuration, you want to setup hardware acceleration to minimize the CPU required to decode your video streams. See the [hardware acceleration](../configuration/hardware_acceleration_video.md) config reference for examples applicable to your hardware.
 
-:::note
+Here is an example configuration with hardware acceleration configured to work with most Intel processors with an integrated GPU using the [preset](../configuration/ffmpeg_presets.md):
 
-Hardware acceleration requires passing the appropriate device to the Docker container. For Intel and AMD GPUs, add the device to your `docker-compose.yml`:
+`docker-compose.yml` (after modifying, you will need to run `docker compose up -d` to apply changes)
 
-```yaml {4,5}
+```yaml
 services:
   frigate:
     ...
     devices:
-      - /dev/dri/renderD128:/dev/dri/renderD128 # for intel & amd hwaccel, needs to be updated for your hardware
+      - /dev/dri/renderD128:/dev/dri/renderD128 # for intel hwaccel, needs to be updated for your hardware
     ...
 ```
 
-After modifying, run `docker compose up -d` to apply changes.
-
-:::
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > Global configuration > FFmpeg" /> and set **Hardware acceleration arguments** to the appropriate preset for your hardware (e.g., `VAAPI (Intel/AMD GPU)` for most Intel processors).
-
-</TabItem>
-<TabItem value="yaml">
+`config.yml`
 
 ```yaml
 mqtt: ...
@@ -182,103 +186,27 @@ cameras:
   name_of_your_camera:
     ffmpeg:
       inputs: ...
-      # highlight-next-line
       hwaccel_args: preset-vaapi
     detect: ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
 ### Step 4: Configure detectors
 
-By default, Frigate will use a single CPU detector.
+By default, Frigate will use a single CPU detector. If you have a USB Coral, you will need to add a detectors section to your config.
 
-In many cases, the integrated graphics on Intel CPUs provides sufficient performance for typical Frigate setups. If you have an Intel processor, you can follow the configuration below.
+`docker-compose.yml` (after modifying, you will need to run `docker compose up -d` to apply changes)
 
-<details>
-  <summary>Use Intel OpenVINO detector</summary>
-
-You need to refer to **Configure hardware acceleration** above to enable the container to use the GPU.
-
-<ConfigTabs>
-<TabItem value="ui">
-
-1. Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `OpenVINO` and **Device** `GPU`
-2. Navigate to <NavPath path="Settings > System > Detection model" /> and configure the model settings for OpenVINO:
-
-| Field                                    | Value                                      |
-| ---------------------------------------- | ------------------------------------------ |
-| **Object detection model input width**   | `300`                                      |
-| **Object detection model input height**  | `300`                                      |
-| **Model Input Tensor Shape**             | `nhwc`                                     |
-| **Model Input Pixel Color Format**       | `bgr`                                      |
-| **Custom object detector model path**    | `/openvino-model/ssdlite_mobilenet_v2.xml` |
-| **Label map for custom object detector** | `/openvino-model/coco_91cl_bkgr.txt`       |
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {3-6,9-15,20-21}
-mqtt: ...
-
-detectors: # <---- add detectors
-  ov:
-    type: openvino  # <---- use openvino detector
-    device: GPU
-
-# We will use the default MobileNet_v2 model from OpenVINO.
-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
-
-cameras:
-  name_of_your_camera:
-    ffmpeg: ...
-    detect:
-      enabled: True # <---- turn on detection
-      ...
-```
-
-</TabItem>
-</ConfigTabs>
-
-</details>
-
-If you have a USB Coral, you will need to add a detectors section to your config.
-
-<details>
-   <summary>Use USB Coral detector</summary>
-
-:::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}
+```yaml
 services:
   frigate:
     ...
     devices:
       - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
-      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://github.com/jnicolson/gasket-builder
+      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
     ...
 ```
 
-:::
-
-<ConfigTabs>
-<TabItem value="ui">
-
-Navigate to <NavPath path="Settings > System > Detector hardware" /> and add a detector with **Type** `EdgeTPU` and **Device** `usb`.
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {3-6,11-12}
+```yaml
 mqtt: ...
 
 detectors: # <---- add detectors
@@ -294,20 +222,15 @@ cameras:
       ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
-</details>
-
 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 can be configured in <NavPath path="Settings > Global configuration > Objects" /> or via 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 will need to be added according to the [configuration file reference](../configuration/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. Click on the camera from the main dashboard, then select the gear icon in the top right, enable Debug 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 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.
 
-Use the mask editor to draw polygon masks directly on the camera feed. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and set up a motion mask over the area. More information about masks can be found [here](../configuration/masks.md).
+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).
 
 :::warning
 
@@ -315,9 +238,9 @@ Note that motion masks should not be used to mark out areas where you do not wan
 
 :::
 
-If you are using YAML to configure Frigate instead of the UI, your configuration should look similar to this now:
+Your configuration should look similar to this now.
 
-```yaml {16-18}
+```yaml
 mqtt:
   enabled: False
 
@@ -335,26 +258,16 @@ cameras:
             - detect
     motion:
       mask:
-        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"
+        - 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.
 
-<ConfigTabs>
-<TabItem value="ui">
+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 <NavPath path="Settings > Camera configuration > FFmpeg" />, select your camera, and add a second input with the `record` role pointing to your high-resolution stream
-2. Navigate to <NavPath path="Settings > Global configuration > Recording" /> (or <NavPath path="Settings > Camera configuration > Recording" /> for a specific camera) and set **Enable recording** to on
-
-</TabItem>
-<TabItem value="yaml">
-
-```yaml {16-17}
+```yaml
 mqtt: ...
 
 detectors: ...
@@ -375,9 +288,6 @@ cameras:
     motion: ...
 ```
 
-</TabItem>
-</ConfigTabs>
-
 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

docs/docs/guides/ha_network_storage.md

@@ -3,7 +3,7 @@ id: ha_network_storage
 title: Home Assistant network storage
 ---
 
-As of Home Assistant 2023.6, Network Mounted Storage is supported for Apps.
+As of Home Assistant 2023.6, Network Mounted Storage is supported for Add-ons.
 
 ## Setting Up Remote Storage For Frigate
 
@@ -14,7 +14,7 @@ As of Home Assistant 2023.6, Network Mounted Storage is supported for Apps.
 
 ### Initial Setup
 
-1. Stop the Frigate App
+1. Stop the Frigate Add-on
 
 ### Move current data
 
@@ -37,4 +37,4 @@ Keeping the current data is optional, but the data will need to be moved regardl
 4. Fill out the additional required info for your particular NAS
 5. Connect
 6. Move files from `/media/frigate_tmp` to `/media/frigate` if they were kept in previous step
-7. Start the Frigate App
+7. Start the Frigate Add-on

docs/docs/integrations/home-assistant.md

@@ -16,15 +16,7 @@ See the [MQTT integration
 documentation](https://www.home-assistant.io/integrations/mqtt/) for more
 details.
 
-In addition, MQTT must be enabled in your Frigate configuration file and Frigate must be connected to the same MQTT server as Home Assistant for many of the entities created by the integration to function, e.g.:
-
-```yaml
-mqtt:
-  enabled: True
-  host: mqtt.server.com # the address of your HA server that's running the MQTT integration
-  user: your_mqtt_broker_username
-  password: your_mqtt_broker_password
-```
+In addition, MQTT must be enabled in your Frigate configuration file and Frigate must be connected to the same MQTT server as Home Assistant for many of the entities created by the integration to function.
 
 ### Integration installation
 
@@ -99,16 +91,16 @@ services:
       ...
 ```
 
-### Home Assistant App
+### Home Assistant Add-on
 
-If you are using Home Assistant App, the URL should be one of the following depending on which App variant you are using. Note that if you are using the Proxy App, you should NOT point the integration at the proxy URL. Just enter the same URL used to access Frigate directly from your network.
+If you are using Home Assistant Add-on, the URL should be one of the following depending on which Add-on variant you are using. Note that if you are using the Proxy Add-on, you should NOT point the integration at the proxy URL. Just enter the same URL used to access Frigate directly from your network.
 
-| App Variant                | URL                                    |
-| -------------------------- | -------------------------------------- |
-| Frigate                    | `http://ccab4aaf-frigate:5000`         |
-| Frigate (Full Access)      | `http://ccab4aaf-frigate-fa:5000`      |
-| Frigate Beta               | `http://ccab4aaf-frigate-beta:5000`    |
-| Frigate Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000` |
+| Add-on Variant             | URL                                       |
+| -------------------------- | ----------------------------------------- |
+| Frigate                    | `http://ccab4aaf-frigate:5000`            |
+| Frigate (Full Access)      | `http://ccab4aaf-frigate-fa:5000`         |
+| Frigate Beta               | `http://ccab4aaf-frigate-beta:5000`       |
+| Frigate Beta (Full Access) | `http://ccab4aaf-frigate-fa-beta:5000`    |
 
 ### Frigate running on a separate machine
 
@@ -253,12 +245,6 @@ To load a preview gif of a review item:
 https://HA_URL/api/frigate/notifications/<review-id>/review_preview.gif
 ```
 
-To load the thumbnail of a review item:
-
-```
-https://HA_URL/api/frigate/notifications/<review-id>/<camera>/review_thumbnail.webp
-```
-
 <a name="streams"></a>
 
 ## RTSP stream

docs/docs/integrations/mqtt.md

@@ -5,20 +5,13 @@ 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)
-"stopped": published when Frigate is stopped normally
-"offline": published automatically by the MQTT broker if Frigate disconnects unexpectedly (via MQTT Will Message)
+"offline": published after Frigate has stopped
 
 ### `frigate/restart`
 
@@ -127,7 +120,7 @@ Message published for each changed tracked object. The first message is publishe
 
 ### `frigate/tracked_object_update`
 
-Message published for updates to tracked object metadata. All messages include an `id` field which is the tracked object's event ID, and can be used to look up the event via the API or match it to items in the UI.
+Message published for updates to tracked object metadata, for example:
 
 #### Generative AI Description Update
 
@@ -141,14 +134,12 @@ Message published for updates to tracked object metadata. All messages include a
 
 #### Face Recognition Update
 
-Published after each recognition attempt, regardless of whether the score meets `recognition_threshold`. See the [Face Recognition](/configuration/face_recognition) documentation for details on how scoring works.
-
 ```json
 {
   "type": "face",
   "id": "1607123955.475377-mxklsc",
-  "name": "John", // best matching person, or null if no match
-  "score": 0.95, // running weighted average across all recognition attempts
+  "name": "John",
+  "score": 0.95,
   "camera": "front_door_cam",
   "timestamp": 1607123958.748393
 }
@@ -156,59 +147,23 @@ Published after each recognition attempt, regardless of whether the score meets
 
 #### License Plate Recognition Update
 
-Published when a license plate is recognized on a car object. See the [License Plate Recognition](/configuration/license_plate_recognition) documentation for details.
-
 ```json
 {
   "type": "lpr",
   "id": "1607123955.475377-mxklsc",
-  "name": "John's Car", // known name for the plate, or null
+  "name": "John's Car",
   "plate": "123ABC",
   "score": 0.95,
   "camera": "driveway_cam",
-  "timestamp": 1607123958.748393,
-  "plate_box": [917, 487, 1029, 529] // box coordinates of the detected license plate in the frame
-}
-```
-
-#### Object Classification Update
-
-Message published when [object classification](/configuration/custom_classification/object_classification) reaches consensus on a classification result.
-
-**Sub label type:**
-
-```json
-{
-  "type": "classification",
-  "id": "1607123955.475377-mxklsc",
-  "camera": "front_door_cam",
-  "timestamp": 1607123958.748393,
-  "model": "person_classifier",
-  "sub_label": "delivery_person",
-  "score": 0.87
-}
-```
-
-**Attribute type:**
-
-```json
-{
-  "type": "classification",
-  "id": "1607123955.475377-mxklsc",
-  "camera": "front_door_cam",
-  "timestamp": 1607123958.748393,
-  "model": "helmet_detector",
-  "attribute": "yes",
-  "score": 0.92
+  "timestamp": 1607123958.748393
 }
 ```
 
 ### `frigate/reviews`
 
-Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated.
+Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. 
 
 An `update` with the same ID will be published when:
-
 - The severity changes from `detection` to `alert`
 - Additional objects are detected
 - An object is recognized via face, lpr, etc.
@@ -282,14 +237,6 @@ Same data available at `/api/stats` published at a configurable interval.
 
 Returns data about each camera, its current features, and if it is detecting motion, objects, etc. Can be triggered by publising 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`.
@@ -300,7 +247,7 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
 
 ## Frigate Camera Topics
 
-### `frigate/<camera_name>/status/<role>`
+### `frigate/<camera_name>/<role>/status`
 
 Publishes the current health status of each role that is enabled (`audio`, `detect`, `record`). Possible values are:
 
@@ -361,11 +308,6 @@ Publishes transcribed text for audio detected on this camera.
 
 **NOTE:** Requires audio detection and transcription to be enabled
 
-### `frigate/<camera_name>/classification/<model_name>`
-
-Publishes the current state detected by a state classification model for the camera. The topic name includes the model name as configured in your classification settings.
-The published value is the detected state class name (e.g., `open`, `closed`, `on`, `off`). The state is only published when it changes, helping to reduce unnecessary MQTT traffic.
-
 ### `frigate/<camera_name>/enabled/set`
 
 Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
@@ -445,30 +387,6 @@ 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/<camera_name>/motion_mask/<mask_name>/set`
-
-Topic to turn a specific motion mask for a camera on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/motion_mask/<mask_name>/state`
-
-Topic with current state of a specific motion mask for a camera. Published values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/object_mask/<mask_name>/set`
-
-Topic to turn a specific object mask for a camera on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/object_mask/<mask_name>/state`
-
-Topic with current state of a specific object mask for a camera. Published values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/zone/<zone_name>/set`
-
-Topic to turn a specific zone for a camera on and off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/zone/<zone_name>/state`
-
-Topic with current state of a specific zone for a camera. Published values are `ON` and `OFF`.
-
 ### `frigate/<camera_name>/review_status`
 
 Topic with current activity status of the camera. Possible values are `NONE`, `DETECTION`, or `ALERT`.

docs/docs/integrations/plus.md

@@ -5,12 +5,6 @@ title: Frigate+
 
 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
@@ -25,11 +19,11 @@ Once logged in, you can generate an API key for Frigate in Settings.
 
 ### Set your API key
 
-In Frigate, you can use an environment variable or a docker secret named `PLUS_API_KEY` to enable the `Frigate+` buttons on the Explore page. Home Assistant App users can set it under Settings > Apps > Frigate > Configuration > Options (be sure to toggle the "Show unused optional configuration options" switch).
+In Frigate, you can use an environment variable or a docker secret named `PLUS_API_KEY` to enable the `Frigate+` buttons on the Explore page. Home Assistant Addon users can set it under Settings > Add-ons > Frigate > Configuration > Options (be sure to toggle the "Show unused optional configuration options" switch).
 
 :::warning
 
-You cannot use the `environment_vars` section of your Frigate configuration file to set this environment variable. It must be defined as an environment variable in the docker config or Home Assistant App config.
+You cannot use the `environment_vars` section of your Frigate configuration file to set this environment variable. It must be defined as an environment variable in the docker config or Home Assistant Add-on config.
 
 :::
 
@@ -60,8 +54,6 @@ Once you have [requested your first model](../plus/first_model.md) and gotten yo
 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:
 
 ```yaml
-detectors: ...
-
 model:
   path: plus://<your_model_id>
 ```

docs/docs/integrations/third_party_extensions.md

@@ -17,10 +17,6 @@ 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.
@@ -42,11 +38,3 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
 ## [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.
-
-## [Strix](https://github.com/eduard256/Strix)
-
-[Strix](https://github.com/eduard256/Strix) auto-discovers working stream URLs for IP cameras and generates ready-to-use Frigate configs. It tests thousands of URL patterns against your camera and supports cameras without RTSP or ONVIF. 67K+ camera models from 3.6K+ brands.

docs/docs/plus/faq.md

@@ -25,9 +25,10 @@ Yes. Subscriptions to Frigate+ provide access to the infrastructure used to trai
 
 ### 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 snapshots are enabled for the cameras you'd like to submit images for.
+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.
 
 ```yaml
 snapshots:
   enabled: true
+  clean_copy: true
 ```

docs/docs/plus/first_model.md

@@ -24,8 +24,6 @@ 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.
 
 ```yaml
-detectors: ...
-
 model:
   path: plus://<your_model_id>
 ```

docs/docs/plus/index.md

@@ -15,15 +15,17 @@ There are three model types offered in Frigate+, `mobiledet`, `yolonas`, and `yo
 
 Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
 
-| Model Type  | Description                                                                                                                                                    |
-| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                              |
-| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                   |
-| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on most hardware. |
+| Model Type  | Description                                                                                                                                                                                                                        |
+| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                                                                                                  |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                                                                                       |
+| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX\*, Apple Silicon\*, and Rockchip NPUs. |
+
+_\* Support coming in 0.17_
 
 ### YOLOv9 Details
 
-YOLOv9 models are available in `s`, `t`, `edgetpu` variants. When requesting a `yolov9` model, you will be prompted to choose a variant. If you want the model to be compatible with a Google Coral, you will need to choose the `edgetpu` variant. If you are unsure what variant to choose, you should perform some tests with the base models to find the performance level that suits you. The `s` size is most similar to the current `yolonas` models in terms of inference times and accuracy, and a good place to start is the `320x320` resolution model for `yolov9s`.
+YOLOv9 models are available in `s` and `t` sizes. When requesting a `yolov9` model, you will be prompted to choose a size. If you are unsure what size to choose, you should perform some tests with the base models to find the performance level that suits you. The `s` size is most similar to the current `yolonas` models in terms of inference times and accuracy, and a good place to start is the `320x320` resolution model for `yolov9s`.
 
 :::info
 
@@ -37,21 +39,23 @@ If you have a Hailo device, you will need to specify the hardware you have when
 
 #### Rockchip (RKNN) Support
 
-Rockchip models are automatically converted as of 0.17. For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it.
+For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it. Automatic conversion is coming in 0.17.
 
 ## Supported detector types
 
-Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo8l`), and Rockchip (`rknn`) detectors.
+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo8l`), and Rockchip\* (`rknn`) detectors.
 
 | Hardware                                                                         | Recommended Detector Type | Recommended Model Type |
 | -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
 | [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)           | `cpu`                     | `mobiledet`            |
-| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu`                 | `yolov9`               |
+| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu`                 | `mobiledet`            |
 | [Intel](/configuration/object_detectors.md#openvino-detector)                    | `openvino`                | `yolov9`               |
 | [NVidia GPU](/configuration/object_detectors#onnx)                               | `onnx`                    | `yolov9`               |
 | [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector)             | `onnx`                    | `yolov9`               |
 | [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo-8)                | `hailo8l`                 | `yolov9`               |
-| [Rockchip NPU](/configuration/object_detectors#rockchip-platform)                | `rknn`                    | `yolov9`               |
+| [Rockchip NPU](/configuration/object_detectors#rockchip-platform)\*              | `rknn`                    | `yolov9`               |
+
+_\* Requires manual conversion in 0.16. Automatic conversion coming in 0.17._
 
 ## Improving your model
 
@@ -79,7 +83,7 @@ Candidate labels are also available for annotation. These labels don't have enou
 
 Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
 
-The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`
+The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`
 
 Candidate labels are not available for automatic suggestions.
 

docs/docs/troubleshooting/cpu.md

@@ -1,73 +0,0 @@
----
-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.
-
-## 1. Hardware Acceleration for Video Decoding
-
-**Priority: Critical**
-
-Video decoding is one of the most CPU-intensive tasks in Frigate. While an AI accelerator handles object detection, it does not assist with decoding video streams. Hardware acceleration (hwaccel) offloads this work to your GPU or specialized video decode hardware, significantly reducing CPU usage and enabling you to support more cameras on the same hardware.
-
-### Key Concepts
-
-**Resolution & FPS Impact:** The decoding burden grows exponentially with resolution and frame rate. A 4K stream at 30 FPS requires roughly 4 times the processing power of a 1080p stream at the same frame rate, and doubling the frame rate doubles the decode workload. This is why hardware acceleration becomes critical when working with multiple high-resolution cameras.
-
-**Hardware Acceleration Benefits:** By using dedicated video decode hardware, you can:
-
-- Significantly reduce CPU usage per camera stream
-- Support 2-3x more cameras on the same hardware
-- Free up CPU resources for motion detection and other Frigate processes
-- Reduce system heat and power consumption
-
-### 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).
-
-### Troubleshooting Hardware Acceleration
-
-If hardware acceleration isn't working:
-
-1. Check Frigate logs for FFmpeg errors related to hwaccel
-2. Verify the hardware device is accessible inside the container
-3. Ensure your camera streams use H.264 or H.265 codecs (most common)
-4. Try different presets if the automatic detection fails
-5. Check that your GPU drivers are properly installed on the host system
-
-## 2. Detector Selection and Configuration
-
-**Priority: Critical**
-
-Choosing the right detector for your hardware is the single most important factor for detection performance. The detector is responsible for running the AI model that identifies objects in video frames. Different detector types have vastly different performance characteristics and hardware requirements, as detailed in the [hardware documentation](../frigate/hardware).
-
-### 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.
-
-**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.
-
-### Choosing the Right Detector
-
-Different detectors have vastly different performance characteristics, see the expected performance for object detectors in [the hardware docs](../frigate/hardware)
-
-### Multiple Detector Instances
-
-When a single detector cannot keep up with your camera count, some detector types (`openvino`, `onnx`) allow you to define multiple detector instances to share the workload. This is particularly useful with GPU-based detectors that have sufficient VRAM to run multiple inference processes.
-
-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
-
-### Model Selection and Optimization
-
-The model you use significantly impacts detector performance. Frigate provides default models optimized for each detector type, but you can customize them as described in the [detector documentation](../configuration/object_detectors).
-
-**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.

docs/docs/troubleshooting/dummy-camera.md

@@ -1,102 +0,0 @@
----
-id: dummy-camera
-title: Analyzing Object Detection
----
-
-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.
-
-## Reviewing Detections in the UI
-
-Before setting up a replay, you can often diagnose detection issues by reviewing existing recordings directly in the Frigate UI.
-
-### 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.
-
-### 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.
-
-:::
-
-### 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.
-
-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:
-  test:
-    ffmpeg:
-      inputs:
-        - path: /media/frigate/car-stopping.mp4
-          input_args: -re -stream_loop -1 -fflags +genpts
-          roles:
-            - detect
-    detect:
-      enabled: true
-    record:
-      enabled: false
-    snapshots:
-      enabled: false
-```
-
-- `-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
-
-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.
-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.
-
-### Troubleshooting
-
-- **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.