rename component for clarity

CameraSettingsView is now CameraReviewSettingsView
clean up camera edit form
2026-07-21 03:09:02 +03:00 · 2025-11-17 11:34:28 -06:00 · 2025-11-17 11:31:06 -06:00 · 2025-11-17 11:30:48 -06:00 · 2025-11-17 11:23:43 -06:00 · 2025-11-17 08:55:11 -06:00
1592 changed files with 21840 additions and 130577 deletions
@@ -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
@@ -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.
@@ -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.**
@@ -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
@@ -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`)
@@ -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
@@ -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]'];
-            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.');
@@ -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
@@ -55,7 +52,7 @@ jobs:
    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 }}
@@ -78,10 +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
@@ -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
@@ -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
@@ -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"
    }
  ]
 }
@@ -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)
@@ -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.
@@ -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 \
@@ -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" />
@@ -14,7 +12,7 @@

 A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.

-Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported [object detectors](https://docs.frigate.video/configuration/object_detectors/).
+Use of a GPU or AI accelerator such as a [Google Coral](https://coral.ai/products/) or [Hailo](https://hailo.ai/) is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.

 - Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
@@ -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.**
@@ -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.**
@@ -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**
@@ -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: $(modinfo -F version hailo_pci)"
@@ -55,7 +55,7 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
 FROM scratch AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
-ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.13/go2rtc_linux_${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
@@ -237,18 +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}"

@@ -266,12 +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"
@@ -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 \
@@ -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
@@ -105,9 +105,9 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
    # 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/25.13.33276.19/libigdgmm12_22.7.0_amd64.deb
-    dpkg -i libigdgmm12_22.7.0_amd64.deb
-    rm libigdgmm12_22.7.0_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 packages
    wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb
@@ -115,19 +115,18 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
    wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-opencl_1.0.17537.24_amd64.deb
    wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-core_1.0.17537.24_amd64.deb
    # standard packages
-    wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/intel-opencl-icd_25.13.33276.19_amd64.deb
-    wget https://github.com/intel/compute-runtime/releases/download/25.13.33276.19/intel-level-zero-gpu_1.6.33276.19_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.10.10/intel-igc-opencl-2_2.10.10+18926_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.10.10/intel-igc-core-2_2.10.10+18926_amd64.deb
+    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
 fi

 if [[ "${TARGETARCH}" == "arm64" ]]; then
@@ -146,6 +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
@@ -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
@@ -47,8 +47,8 @@ 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.*
@@ -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
@@ -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
@@ -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
@@ -22,31 +22,6 @@ 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"):
@@ -134,26 +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 = stream.format(**FRIGATE_ENV_VARS)
-            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."
@@ -161,33 +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 = stream_item.format(**FRIGATE_ENV_VARS)
-                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")
@@ -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 '';
@@ -323,12 +320,6 @@ http {
                add_header Cache-Control "public";
            }

-            location /fonts/ {
-                access_log off;
-                expires 1y;
-                add_header Cache-Control "public";
-            }
-
            location /locales/ {
                access_log off;
                add_header Cache-Control "public";
@@ -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;
@@ -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))
@@ -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))
@@ -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))
@@ -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";
@@ -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 }}
+
@@ -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/ /

 #######################################################################
@@ -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
@@ -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
@@ -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
@@ -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'
@@ -25,7 +25,7 @@ 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.
@@ -44,32 +44,13 @@ 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.
-
-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.
+This section can be used to set environment variables for those unable to modify the environment of the container, like within Home Assistant OS.

 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}"
-```
-
-#### TensorFlow Thread Configuration
-
-If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
-
-```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)
+  VARIABLE_NAME: variable_value
 ```

 ### `database`
@@ -163,32 +144,33 @@ services:

 ### Enabling IPv6

-IPv6 is disabled by default, to enable IPv6 modify your Frigate configuration as follows:
+IPv6 is disabled by default, to enable IPv6 listen.gotmpl needs to be bind mounted with IPv6 enabled. For example:

-```yaml
-networking:
-  ipv6:
-    enabled: True
+```
+{{ if not .enabled }}
+# intended for external traffic, protected by auth
+listen 8971;
+{{ else }}
+# intended for external traffic, protected by auth
+listen 8971 ssl;
+
+# intended for internal traffic, not protected by auth
+listen 5000;
 ```

-### Listen on different ports
+becomes

-You can change the ports Nginx uses for listening using Frigate's configuration file. 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.
-
-For example:
-
-```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;

-:::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

@@ -241,7 +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:

@@ -265,7 +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
@@ -50,7 +50,7 @@ cameras:

 ### 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

@@ -75,13 +75,7 @@ audio:

 ### 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.
-
-Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
-
-#### Configuration
-
-To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
+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:
@@ -150,28 +144,4 @@ In order to use transcription and translation for past events, you must enable a

 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.
@@ -29,10 +29,6 @@ auth:
  reset_admin_password: true
 ```

-## Password guidance
-
-Constructing secure passwords and managing them properly is important. Frigate requires a minimum length of 12 characters. For guidance on password standards see [NIST SP 800-63B](https://pages.nist.gov/800-63-3/sp800-63b.html). To learn what makes a password truly secure, read this [article](https://medium.com/peerio/how-to-build-a-billion-dollar-password-3d92568d9277).
-
 ## 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).
@@ -86,7 +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.
@@ -166,10 +162,6 @@ 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)**
@@ -232,7 +224,7 @@ The viewer role provides read-only access to all cameras in the UI and API. Cust

 ### Role Configuration Example

-```yaml {11-16}
+```yaml
 cameras:
  front_door:
    # ... camera config
@@ -278,42 +270,3 @@ 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
@@ -52,10 +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:
@@ -24,7 +24,7 @@ A custom icon can be added to the birdseye background by providing a 180x180 ima

 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.

-```yaml {8-10,12-14}
+```yaml
 # Include all cameras by default in Birdseye view
 birdseye:
  enabled: True
@@ -48,7 +48,6 @@ By default birdseye shows all cameras that have had the configured activity in t
 ```yaml
 birdseye:
  enabled: True
-  # highlight-next-line
  inactivity_threshold: 15
 ```

@@ -79,11 +78,9 @@ birdseye:
 cameras:
  front:
    birdseye:
-      # highlight-next-line
      order: 1
  back:
    birdseye:
-      # highlight-next-line
      order: 2
 ```

@@ -95,7 +92,7 @@ It is possible to limit the number of cameras shown on birdseye at one time. Whe

 For example, this can be configured to only show the most recently active camera.

-```yaml {3-4}
+```yaml
 birdseye:
  enabled: True
  layout:
@@ -106,7 +103,7 @@ birdseye:

 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.

-```yaml {3-4}
+```yaml
 birdseye:
  enabled: True
  layout:
@@ -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:
@@ -66,7 +66,7 @@ Not every PTZ supports ONVIF, which is the standard protocol Frigate uses to com

 Add the onvif section to your camera in your configuration file:

-```yaml {4-8}
+```yaml
 cameras:
  back:
    ffmpeg: ...
@@ -79,20 +79,12 @@ cameras:

 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
@@ -102,19 +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            |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
@@ -3,16 +3,14 @@ id: object_classification
 title: Object Classification
 ---

-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.
+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.
-
 ## Classes

 Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
@@ -27,29 +25,15 @@ For object classification:
 ### Classification Type

 - **Sub label**:
+
  - 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

@@ -80,62 +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.
-
 ## 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.
+- **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.
-
-```yaml
-logger:
-  default: info
-  logs:
-    # highlight-next-line
-    frigate.data_processing.real_time.custom_classification: debug
-```
-
-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
@@ -3,16 +3,14 @@ id: state_classification
 title: State Classification
 ---

-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.
+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.
-
 ## Classes

 Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
@@ -48,72 +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.
-
 ## 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.
-
-```yaml
-logger:
-  default: info
-  logs:
-    # highlight-next-line
-    frigate.data_processing.real_time.custom_classification: debug
-```
-
-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.
@@ -32,8 +32,6 @@ 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.
@@ -145,14 +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).

@@ -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: llava:7b
+```
+
+## 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/)
@@ -5,31 +5,27 @@ title: Configuring Generative AI

 ## 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, strong 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. |
-| `Intern3.5VL` | Relatively fast with good vision comprehension                                                                                                                       |
-| `gemma3`      | Slower model with good vision and temporal understanding                                                                                                             |
-| `qwen2.5-vl`  | Fast but capable model with good vision comprehension                                                                                                                |
+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

@@ -37,135 +33,50 @@ Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger s

 :::

+:::tip
+
+If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. https://github.com/skye-harris/ollama-modelfiles contains optimized model configs for this task.
+
+:::
+
+The following models are recommended:
+
+| Model             | Notes                                                       |
+| ----------------- | ----------------------------------------------------------- |
+| `qwen3-vl`        | Strong visual and situational understanding                 |
+| `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       |
+| `llava-phi3`      | Lightweight and fast model with vision comprehension        |
+
 :::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.
+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.

 :::

-### 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.
-
-```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.
-```
-
-### 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.
-
-#### Configuration
+### 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
 ```

-### OpenAI-Compatible
+## Google Gemini

-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.
+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.

-:::tip
+### Supported Models

-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`:
+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`.

-```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
-
-```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
-```
-
-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.
-
-### 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
-
-```yaml
-genai:
-  provider: ollama
-  base_url: http://localhost:11434
-  model: cloud-model-name
-```
-
-### 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).

@@ -174,44 +85,28 @@ 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
+### Configuration

 ```yaml
 genai:
  provider: gemini
  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-2.5-flash
+  model: gemini-1.5-flash
 ```

-:::note
-
-To use a different Gemini-compatible API endpoint, set the `provider_options` with the `base_url` key to your provider's API URL. For example:
-
-```yaml {4,5}
-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
+### Configuration

 ```yaml
 genai:
@@ -226,41 +121,23 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env

 :::

-:::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
+### 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}"
 ```
@@ -11,7 +11,7 @@ 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

@@ -39,10 +39,9 @@ You are also able to define custom prompts in your configuration.
 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."
@@ -7,7 +7,7 @@ Generative AI can be used to automatically generate structured summaries of revi

 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

@@ -16,13 +16,12 @@ 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

@@ -31,43 +30,40 @@ Each installation and even camera can have different parameters for what is cons
 <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>
@@ -80,7 +76,6 @@ By default, review summaries use preview images (cached preview frames) which ha
 review:
  genai:
    enabled: true
-    # highlight-next-line
    image_source: recordings # Options: "preview" (default) or "recordings"
 ```

@@ -105,7 +100,7 @@ If recordings are not available for a given time period, the system will automat

 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:

-```yaml {4,5}
+```yaml
 review:
  genai:
    enabled: true
@@ -113,23 +108,6 @@ review:
      - animals in the garden
 ```

-### Preferred Language
-
-By default, review summaries are generated in English. You can configure Frigate to generate summaries in your preferred language by setting the `preferred_language` option:
-
-```yaml {4}
-review:
-  genai:
-    enabled: true
-    preferred_language: Spanish
-```
-
 ## Review Reports

-Along with individual review item summaries, Generative AI can also produce a single report of review items from all cameras marked "suspicious" over a specified time period (for example, a daily summary of suspicious activity while you're on vacation).
-
-### 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.
@@ -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

@@ -3,72 +3,84 @@ id: hardware_acceleration_video
 title: Video Decoding
 ---

-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 # 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 GPU  | iHD / Xe     | preset-intel-qsv-\* |                                             |
+| 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.

@@ -117,13 +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 \
  ...
@@ -137,7 +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:
    ...
@@ -148,7 +159,7 @@ services:

 ##### Docker Run CLI - CAP_PERFMON

-```bash {4}
+```bash
 docker run -d \
  --name frigate \
  ...
@@ -184,18 +195,16 @@ 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.

+:::note
+
+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).
+
+:::
+
 ```yaml
 ffmpeg:
  hwaccel_args: preset-vaapi
@@ -215,7 +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:
    ...
@@ -232,7 +241,7 @@ services:

 #### Docker Run CLI - Nvidia GPU

-```bash {4}
+```bash
 docker run -d \
  --name frigate \
  ...
@@ -255,7 +264,7 @@ 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).

 :::

@@ -291,69 +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.
-
-```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
-```
-
-:::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
@@ -362,7 +320,7 @@ docker run -d \

 ### Docker Compose - Jetson

-```yaml {5}
+```yaml
 services:
  frigate:
    ...
@@ -453,14 +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:
@@ -482,7 +440,7 @@ Make sure to follow the [Synaptics specific installation instructions](/frigate/

 Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:

-```yaml {2}
+```yaml
 ffmpeg:
  hwaccel_args: -c:v h264_v4l2m2m
  input_args: preset-rtsp-restream
@@ -3,7 +3,7 @@ id: index
 title: Frigate Configuration
 ---

-For Home Assistant App installations, the config file should 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](#accessing-app-config-dir).
+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).

 For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.

@@ -25,24 +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 file editor in the Frigate UI to edit the configuration file.
+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

@@ -50,7 +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}"
 ```
@@ -61,7 +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}"
@@ -83,10 +82,10 @@ genai:

 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
@@ -110,16 +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
@@ -139,10 +137,7 @@ 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
 ```

 ### Standalone Intel Mini PC with USB Coral
@@ -170,16 +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
@@ -199,10 +193,7 @@ 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
 ```

 ### Home Assistant integrated Intel Mini PC with OpenVino
@@ -240,16 +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
@@ -269,8 +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
 ```
@@ -30,7 +30,7 @@ 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

@@ -43,7 +43,7 @@ lpr:

 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:

-```yaml {4,5}
+```yaml
 cameras:
  garage:
    ...
@@ -68,8 +68,8 @@ Fine-tune the LPR feature using these optional parameters at the global level of
  - 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: `None`
-  - This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
+  - 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`
  - This can be `small` or `large`.
@@ -107,23 +107,23 @@ Fine-tune the LPR feature using these optional parameters at the global level of

 ### Normalization Rules

- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially and are applied _before_ the `format` regex, if specified. Each rule must have a `pattern` (which can be a string or a regex) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
+- **`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').

 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'
 ```

 - Rules fire in order: In the example above: clean noise first, then separators, then swaps, then splits.
@@ -374,39 +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.

-     ```yaml
-     lpr:
-       enabled: true
-       device: CPU
-       debug_save_plates: true
-     ```
-
-2. Enable debug logs to see exactly what Frigate is doing.
-   - Enable debug logs for LPR by adding `frigate.data_processing.common.license_plate: debug` to your `logger` configuration. These logs are _very_ verbose, so only keep this enabled when necessary. Restart Frigate after this change.
+   - 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).
@@ -429,6 +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.
@@ -15,7 +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.                                          |
+| 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

@@ -77,7 +77,7 @@ Configure the `streams` option with a "friendly name" for your stream followed b

 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.

-```yaml {3,6,8,25-29}
+```yaml
 go2rtc:
  streams:
    test_cam:
@@ -114,9 +114,9 @@ cameras:
 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: ...
@@ -127,14 +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
@@ -154,7 +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:
    ...
@@ -179,8 +178,6 @@ 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.
@@ -217,36 +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?**
@@ -264,18 +231,21 @@ 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)).
   - 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).
@@ -307,36 +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
-   ```
@@ -28,60 +28,24 @@ To create a poly mask:
 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 can also be created through the UI or manually in the config. They are configured under the object filters section for each object type:
-
-```yaml
-objects:
-  filters:
-    person:
-      mask:
-        person_filter1:
-          friendly_name: "Roof area"
-          enabled: true
-          coordinates: "0.000,0.000,1.000,0.000,1.000,0.400,0.000,0.400"
-    car:
-      mask:
-        car_filter1:
-          friendly_name: "Sidewalk area"
-          enabled: true
-          coordinates: "0.000,0.700,1.000,0.700,1.000,1.000,0.000,1.000"
-```
-
-## Enabling/Disabling Masks
-
-Both motion masks and object filter masks can be toggled on or off without removing them from the configuration. Disabled masks are completely ignored at runtime - they will not affect motion detection or object filtering. This is useful for temporarily disabling a mask during certain seasons or times of day without modifying the configuration.
-
 ### Further Clarification

 This is a response to a [question posed on reddit](https://www.reddit.com/r/homeautomation/comments/ppxdve/replacing_my_doorbell_with_a_security_camera_a_6/hd876w4?utm_source=share&utm_medium=web2x&context=3):
@@ -38,6 +38,7 @@ 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.

 ```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.
@@ -52,6 +53,7 @@ Watching the motion boxes in the debug view, increase the threshold until you on
 ### Contour Area

 ```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
@@ -79,49 +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
-
 ```yaml
+# default lightning_threshold:
 motion:
-  # Optional: The percentage of the image used to detect lightning or
-  #           other substantial changes where motion detection needs to
-  #           recalibrate. (default: shown below)
-  #           Increasing this value will make motion detection more likely
-  #           to consider lightning or IR mode changes as valid motion.
-  #           Decreasing this value will make motion detection more likely
-  #           to ignore large amounts of motion such as a person
-  #           approaching a doorbell camera.
+  # Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection
+  #           needs to recalibrate. (default: shown below)
+  # Increasing this value will make motion detection more likely to consider lightning or ir mode changes as valid motion.
+  # Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching
+  # a doorbell camera.
  lightning_threshold: 0.8
 ```

-Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. `lightning_threshold` defines the percentage of the image used to detect these substantial changes. Increasing this value makes motion detection more likely to treat large changes (like IR mode switches) as valid motion. Decreasing it makes motion detection more likely to ignore large amounts of motion, such as a person approaching a doorbell camera.
-
-Note that `lightning_threshold` does **not** stop motion-based recordings from being saved — it only prevents additional motion analysis after the threshold is exceeded, reducing false positive object detections during high-motion periods (e.g. storms or PTZ sweeps) without interfering with recordings.
-
 :::warning

-Some cameras, like doorbell cameras, may have missed detections when someone walks directly in front of the camera and the `lightning_threshold` causes motion detection to 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

-```yaml
-motion:
-  # Optional: Fraction of the frame that must change in a single update
-  #           before Frigate will completely ignore any motion in that frame.
-  #           Values range between 0.0 and 1.0, leave unset (null) to disable.
-  #           Setting this to 0.7 would cause Frigate to **skip** reporting
-  #           motion boxes when more than 70% of the image appears to change
-  #           (e.g. during lightning storms, IR/color mode switches, or other
-  #           sudden lighting events).
-  skip_motion_threshold: 0.7
-```
-
-This option is handy when you want to prevent large transient changes from triggering recordings or object detection. It differs from `lightning_threshold` because it completely suppresses motion instead of just forcing a recalibration.
-
-:::warning
-
-When the skip threshold is exceeded, **no motion is reported** for that frame, meaning **nothing is recorded** for that frame. That means you can miss something important, like a PTZ camera auto-tracking an object or activity while the camera is moving. If you prefer to guarantee that every frame is saved, leave this unset and accept occasional recordings containing scene noise — they typically only take up a few megabytes and are quick to scan in the timeline UI.
+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.
@@ -3,8 +3,6 @@ id: object_detectors
 title: Object Detectors
 ---

-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 # Supported Hardware

 :::info
@@ -13,10 +11,10 @@ Frigate supports multiple different detectors that work on different types of ha

 **Most Hardware**

- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
+- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
+- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
+- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).

 **AMD**

@@ -34,26 +32,21 @@ Frigate supports multiple different detectors that work on different types of ha

 **Nvidia GPU**

- [ONNX](#onnx): Nvidia GPUs will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
+- [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.

-**Nvidia Jetson** <CommunityBadge />
+**Nvidia Jetson**

 - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.

-**Rockchip** <CommunityBadge />
+**Rockchip**

 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.

-**Synaptics** <CommunityBadge />
+**Synaptics**

 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.

-**AXERA** <CommunityBadge />
-
- [AXEngine](#axera): axmodels can run on AXERA AI acceleration.
-
-
 **For Testing**

 - [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
@@ -70,14 +63,16 @@ This does not affect using hardware for accelerating other tasks such as [semant

 # Officially Supported Detectors

-Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `memryx`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.

 ## Edge TPU Detector

-The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
+The Edge TPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.

 The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.

+A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+
 :::tip

 See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edge TPU is not detected.
@@ -149,50 +144,6 @@ detectors:
    device: pci
 ```

-### EdgeTPU Supported Models
-
-| Model                   | Notes                                       |
-| ----------------------- | ------------------------------------------- |
-| [Mobiledet](#mobiledet) | Default model                               |
-| [YOLOv9](#yolov9)       | More accurate but slower than default model |
-
-#### Mobiledet
-
-A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
-
-#### YOLOv9
-
-YOLOv9 models that are compiled for TensorFlow Lite and properly quantized are supported, but not included by default. [Instructions](#yolov9-for-google-coral-support) for downloading a model with support for the Google Coral.
-
-:::tip
-
-**Frigate+ Users:** Follow the [instructions](/integrations/plus#use-models) to set a model ID in your config file.
-
-:::
-
-<details>
-  <summary>YOLOv9 Setup & Config</summary>
-
-After placing the downloaded files for the tflite model and labels in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
-
-model:
-  model_type: yolo-generic
-  width: 320 # <--- should match the imgsize of the model, typically 320
-  height: 320 # <--- should match the imgsize of the model, typically 320
-  path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
-  labelmap_path: /config/labels-coco17.txt
-```
-
-Note that due to hardware limitations of the Coral, the labelmap is a subset of the COCO labels and includes only 17 object classes.
-
-</details>
-
 ---

 ## Hailo-8
@@ -411,7 +362,7 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv

 :::warning

-If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
+If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.

 :::

@@ -488,7 +439,7 @@ After placing the downloaded onnx model in your config/model_cache folder, you c
 detectors:
  ov:
    type: openvino
-    device: CPU
+    device: GPU

 model:
  model_type: dfine
@@ -577,13 +528,13 @@ $ docker run --device=/dev/kfd --device=/dev/dri  \

 When using Docker Compose:

-```yaml {4-6}
+```yaml
 services:
  frigate:
-    ...
-    devices:
-      - /dev/dri
-      - /dev/kfd
+---
+devices:
+  - /dev/dri
+  - /dev/kfd
 ```

 For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
@@ -608,12 +559,12 @@ $ docker run -e HSA_OVERRIDE_GFX_VERSION=10.0.0 \

 When using Docker Compose:

-```yaml {4-5}
+```yaml
 services:
  frigate:
-    ...
-    environment:
-      HSA_OVERRIDE_GFX_VERSION: "10.0.0"
+
+environment:
+  HSA_OVERRIDE_GFX_VERSION: "10.0.0"
 ```

 Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
@@ -665,9 +616,11 @@ ONNX is an open format for building machine learning models, Frigate supports ru
 If the correct build is used for your GPU then the GPU will be detected and used automatically.

 - **AMD**
+
  - ROCm will automatically be detected and used with the ONNX detector in the `-rocm` Frigate image.

 - **Intel**
+
  - OpenVINO will automatically be detected and used with the ONNX detector in the default Frigate image.

 - **Nvidia**
@@ -749,7 +702,7 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv

 :::warning

-If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
+If you are using a Frigate+ YOLOv9 model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.

 :::

@@ -1009,6 +962,7 @@ model:
  # path: /config/yolov9.zip
  # The .zip file must contain:
  # ├── yolov9.dfp          (a file ending with .dfp)
+  # └── yolov9_post.onnx    (optional; only if the model includes a cropped post-processing network)
 ```

 #### YOLOX
@@ -1483,42 +1437,6 @@ model:
  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
 ```

-## AXERA
-
-Hardware accelerated object detection is supported on the following SoCs:
-
- AX650N
- AX8850N
-
-This implementation uses the [AXera Pulsar2 Toolchain](https://huggingface.co/AXERA-TECH/Pulsar2).
-
-See the [installation docs](../frigate/installation.md#axera) for information on configuring the AXEngine hardware.
-
-### Configuration
-
-When configuring the AXEngine detector, you have to specify the model name.
-
-#### yolov9
-
-A yolov9 model is provided in the container at `/axmodels` and is used by this detector type by default.
-
-Use the model configuration shown below when using the axengine detector with the default axmodel:
-
-```yaml
-detectors:
-  axengine:
-    type: axengine
-
-model:
-  path: frigate-yolov9-tiny
-  model_type: yolo-generic
-  width: 320
-  height: 320
-  input_dtype: int
-  input_pixel_format: bgr
-  labelmap_path: /labelmap/coco-80.txt
-```
-
 # Models

 Some model types are not included in Frigate by default.
@@ -1553,17 +1471,17 @@ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL
 EOF
 ```

-### Downloading RF-DETR Model
+### Download RF-DETR Model

 RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.

 ```sh
-docker build . --build-arg MODEL_SIZE=Nano --rm --output . -f- <<'EOF'
-FROM python:3.12 AS build
+docker build . --build-arg MODEL_SIZE=Nano --output . -f- <<'EOF'
+FROM python:3.11 AS build
 RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
 WORKDIR /rfdetr
-RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnx==1.19.1 transformers==4.57.6 onnxscript
+RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnxscript
 ARG MODEL_SIZE
 RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
 FROM scratch
@@ -1601,23 +1519,19 @@ cd tensorrt_demos/yolo
 python3 yolo_to_onnx.py -m yolov7-320
 ```

-#### YOLOv9 for Google Coral Support
-
-[Download the model](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite), bind mount the file into the container, and provide the path with `model.path`. Note that the linked model requires a 17-label [labelmap file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) that includes only 17 COCO classes.
-
-#### YOLOv9 for other detectors
+#### YOLOv9

 YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).

 ```sh
 docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
 FROM python:3.11 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
+RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
+COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
 WORKDIR /yolov9
 ADD https://github.com/WongKinYiu/yolov9.git .
 RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
+RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
 ARG MODEL_SIZE
 ARG IMG_SIZE
 ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
@@ -1,188 +0,0 @@
---
-id: profiles
-title: Profiles
---
-
-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/.active_profile` file).
-
-:::
-
-## Configuration
-
-The easiest way to define profiles is to use the Frigate UI. Profiles can also be configured manually in your configuration file.
-
-### Using the UI
-
-To create and manage profiles from the UI, open **Settings**. From there you can:
-
-1. **Create a profile** — Navigate to **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
-3. **Activate a profile** — Use the **Profiles** option in Frigate's main menu to choose a profile. Alternatively, in Settings, navigate to **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 **Profiles**, then click the trash icon for a profile. This removes the profile definition and all camera overrides associated with it.
-
-### Defining Profiles in 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
-```
-
-### Camera Profile Overrides
-
-Under each camera, add a `profiles` section with overrides for each profile. You only need to include the settings you want to change.
-
-```yaml
-cameras:
-  front_door:
-    ffmpeg:
-      inputs:
-        - path: rtsp://camera:554/stream
-          roles:
-            - detect
-            - record
-    detect:
-      enabled: true
-    record:
-      enabled: true
-    profiles:
-      away:
-        detect:
-          enabled: true
-        notifications:
-          enabled: true
-        objects:
-          track:
-            - person
-            - car
-            - package
-        review:
-          alerts:
-            labels:
-              - person
-              - car
-              - package
-      home:
-        detect:
-          enabled: true
-        notifications:
-          enabled: false
-        objects:
-          track:
-            - person
-```
-
-### Supported Override Sections
-
-The following camera configuration sections can be overridden in a profile:
-
-| Section            | Description                               |
-| ------------------ | ----------------------------------------- |
-| `enabled`          | Enable or disable the camera entirely     |
-| `audio`            | Audio detection settings                  |
-| `birdseye`         | Birdseye view settings                    |
-| `detect`           | Object detection settings                 |
-| `face_recognition` | Face recognition settings                 |
-| `lpr`              | License plate recognition settings        |
-| `motion`           | Motion detection settings                 |
-| `notifications`    | Notification settings                     |
-| `objects`          | Object tracking and filter settings       |
-| `record`           | Recording settings                        |
-| `review`           | Review alert and detection settings       |
-| `snapshots`        | Snapshot settings                         |
-| `zones`            | Zone definitions (merged with base zones) |
-
-:::note
-
-Only the fields you explicitly set in a profile override are applied. All other fields retain their base configuration values. For zones, profile zones are merged with the camera's base zones — any zone defined in the profile will override or add to the base zones.
-
-:::
-
-## 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.
-
-```yaml
-profiles:
-  home:
-    friendly_name: Home
-  away:
-    friendly_name: Away
-
-cameras:
-  front_door:
-    ffmpeg:
-      inputs:
-        - path: rtsp://camera:554/stream
-          roles:
-            - detect
-            - record
-    detect:
-      enabled: true
-    record:
-      enabled: true
-    notifications:
-      enabled: false
-    profiles:
-      away:
-        notifications:
-          enabled: true
-        review:
-          alerts:
-            labels:
-              - person
-              - car
-      home:
-        notifications:
-          enabled: false
-
-  indoor_cam:
-    ffmpeg:
-      inputs:
-        - path: rtsp://camera:554/indoor
-          roles:
-            - detect
-            - record
-    detect:
-      enabled: false
-    record:
-      enabled: false
-    profiles:
-      away:
-        enabled: true
-        detect:
-          enabled: true
-        record:
-          enabled: true
-      home:
-        enabled: false
-```
-
-In this example:
-
- **Away profile**: The front door camera enables notifications and tracks specific alert labels. The indoor camera is fully enabled with detection and recording.
- **Home profile**: The front door camera disables notifications. The indoor camera is completely disabled for privacy.
- **No profile active**: All cameras use their base configuration values.
@@ -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.
@@ -130,7 +130,7 @@ When exporting a time-lapse the default speed-up is 25x with 30 FPS. This means

 To configure the speed-up factor, the frame rate and further custom settings, the configuration parameter `timelapse_args` can be used. The below configuration example would change the time-lapse speed to 60x (for fitting 1 hour of recording into 1 minute of time-lapse) with 25 FPS:

-```yaml {3-4}
+```yaml
 record:
  enabled: True
  export:
@@ -139,13 +139,7 @@ record:

 :::tip

-When using `hwaccel_args`, hardware encoding is used for timelapse generation. This setting can be overridden for a specific camera (e.g., when camera resolution exceeds hardware encoder limits); set `cameras.<camera>.record.export.hwaccel_args` with the appropriate settings. Using an unrecognized value or empty string will fall back to software encoding (libx264).
-
-:::
-
-:::tip
-
-The encoder determines its own behavior so the resulting file size may be undesirably large.
+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.

 :::
@@ -154,18 +148,19 @@ To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (whe

 Apple devices running the Safari browser may fail to playback h.265 recordings. The [apple compatibility option](../configuration/camera_specific.md#h265-cameras-via-safari) should be used to ensure seamless playback on Apple devices.

-## Syncing 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.

 :::
@@ -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
 ```
@@ -7,7 +7,7 @@ title: Restream

 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

@@ -24,17 +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"
@@ -147,7 +146,6 @@ For example:
 ```yaml
 go2rtc:
  streams:
-    # highlight-error-line
    my_camera: rtsp://username:$@foo%@192.168.1.100
 ```

@@ -156,71 +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}}`

@@ -228,4 +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}}
-```
+```
@@ -71,7 +71,7 @@ To exclude a specific camera from alerts or detections, simply provide an empty

 For example, to exclude objects on the camera _gatecamera_ from any detections, include this in your config:

-```yaml {3-5}
+```yaml
 cameras:
  gatecamera:
    review:
@@ -13,7 +13,7 @@ Semantic Search is accessed via the _Explore_ view in the Frigate UI.

 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.

@@ -76,40 +76,6 @@ 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 in your config with `embeddings` in its `roles`.
-2. Set `semantic_search.model` to the GenAI config key (e.g. `default`).
-3. Start the llama.cpp server with `--embeddings` and `--mmproj` for image support:
-
-```yaml
-genai:
-  default:
-    provider: llamacpp
-    base_url: http://localhost:8080
-    model: your-model-name
-    roles:
-      - embeddings
-      - vision
-      - tools
-
-semantic_search:
-  enabled: True
-  model: default
-```
-
-The llama.cpp server must be started with `--embeddings` for the embeddings API, and a multi-modal embeddings model. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for details.
-
-:::note
-
-Switching between Jina models and a GenAI provider requires reindexing. Embeddings from different backends are incompatible.
-
-:::
-
 ### GPU Acceleration

 The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
@@ -3,29 +3,10 @@ id: snapshots
 title: Snapshots
 ---

-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 in the [config file](/configuration) under `cameras -> your_camera -> mqtt`
-
-## 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 `cameras -> your_camera -> mqtt`.
-
-## 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 `cameras -> your_camera -> mqtt` 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`
@@ -20,7 +20,7 @@ tls:

 TLS certificates can be mounted at `/etc/letsencrypt/live/frigate` using a bind mount or docker volume.

-```yaml {3-4}
+```yaml
 frigate:
  ...
  volumes:
@@ -32,7 +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:
@@ -46,7 +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:
@@ -10,10 +10,6 @@ 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.

 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.
@@ -22,7 +18,7 @@ To create a zone, follow [the steps for a "Motion mask"](masks.md), but use the

 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:

-```yaml {6,8}
+```yaml
 cameras:
  name_of_your_camera:
    review:
@@ -90,6 +86,7 @@ cameras:

 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.
@@ -97,7 +94,6 @@ 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.

@@ -108,7 +104,6 @@ cameras:
  name_of_your_camera:
    zones:
      sidewalk:
-        # highlight-next-line
        loitering_time: 4 # unit is in seconds
        objects:
          - person
@@ -123,7 +118,6 @@ cameras:
  name_of_your_camera:
    zones:
      front_yard:
-        # highlight-next-line
        inertia: 3
        objects:
          - person
@@ -136,7 +130,6 @@ cameras:
  name_of_your_camera:
    zones:
      driveway_entrance:
-        # highlight-next-line
        inertia: 1
        objects:
          - car
@@ -199,6 +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)
 ```
@@ -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
@@ -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.
@@ -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#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.

@@ -153,7 +148,9 @@ The OpenVINO detector type is able to run on:

 :::note

-Intel B-series (Battlemage) GPUs are not officially supported with Frigate 0.17, though a user has [provided steps to rebuild the Frigate container](https://github.com/blakeblackshear/frigate/discussions/21257) with support for them.
+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.

 :::

@@ -166,17 +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.

@@ -186,28 +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

@@ -263,7 +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-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).
+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.

@@ -284,23 +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.
@@ -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 # Optional: 1GB of memory, reduces SSD/SD Card wear
        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

@@ -276,7 +135,6 @@ Finally, configure [hardware object detection](/configuration/object_detectors#h
 ### 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.
@@ -479,7 +302,7 @@ 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/accel:/dev/accel # Intel NPU
@@ -487,7 +310,7 @@ services:
      - /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 # Optional: 1GB of memory, reduces SSD/SD Card wear
        target: /tmp/cache
        tmpfs:
          size: 1000000000
@@ -527,15 +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

@@ -545,8 +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.

 :::

@@ -556,27 +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

@@ -589,7 +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

@@ -714,43 +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"
-```
@@ -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.
@@ -5,9 +5,9 @@ title: Updating

 # Updating Frigate

-The current stable version of Frigate is **0.17.0**. 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/v0.17.0).
+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., `0.17.0` instead of `0.16.4`). 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:0.17.0
+           image: ghcr.io/blakeblackshear/frigate:0.16.2
       ```
     - Then pull the image:
       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.17.0
+       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., `0.17.0`, `0.17.0-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:0.17.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
       ```

 3. **Start the Container**:
+
   - If using Docker Compose:
     ```bash
     docker compose up -d
@@ -67,30 +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**:
-   - 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

@@ -99,9 +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:0.16.4`) 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
@@ -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
 ```
@@ -11,7 +11,7 @@ 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

@@ -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`.
@@ -9,7 +9,7 @@ title: Getting started

 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.

 :::

@@ -81,7 +81,7 @@ 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

@@ -119,7 +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
@@ -134,13 +134,31 @@ Now you should be able to start Frigate by running `docker compose up -d` from w

 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

-You can click the `Add Camera` button 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)

@@ -150,12 +168,12 @@ Here is an example configuration with hardware acceleration configured to work w

 `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
    ...
 ```

@@ -168,67 +186,27 @@ cameras:
  name_of_your_camera:
    ffmpeg:
      inputs: ...
-      # highlight-next-line
      hwaccel_args: preset-vaapi
    detect: ...
 ```

 ### Step 4: Configure detectors

-By default, Frigate will use a single CPU detector.
-
-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.
-
-<details>
-  <summary>Use Intel OpenVINO detector</summary>
-
-You need to refer to **Configure hardware acceleration** above to enable the container to use the GPU.
-
-```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
-      ...
-```
-
-</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>
+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.

 `docker-compose.yml` (after modifying, you will need to run `docker compose up -d` to apply changes)

-```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
    ...
 ```

-```yaml {3-6,11-12}
+```yaml
 mqtt: ...

 detectors: # <---- add detectors
@@ -244,8 +222,6 @@ cameras:
      ...
 ```

-</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 will need to be added according to the [configuration file reference](../configuration/reference.md).
@@ -264,7 +240,7 @@ Note that motion masks should not be used to mark out areas where you do not wan

 Your configuration should look similar to this now.

-```yaml {16-18}
+```yaml
 mqtt:
  enabled: False

@@ -282,10 +258,7 @@ 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
@@ -294,7 +267,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.

 To enable recording video, add the `record` role to a stream and enable it in the config. If record is disabled in the config, it won't be possible to enable it in the UI.

-```yaml {16-17}
+```yaml
 mqtt: ...

 detectors: ...
@@ -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
@@ -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
@@ -11,8 +11,7 @@ These are the MQTT messages generated by Frigate. The default topic_prefix is `f

 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`

@@ -121,7 +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

@@ -135,14 +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
 }
@@ -150,59 +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.
@@ -276,14 +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`.
@@ -294,7 +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:

@@ -355,11 +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`.
@@ -439,30 +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`.
@@ -19,11 +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.

 :::

@@ -54,8 +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>
 ```
@@ -38,11 +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.
@@ -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
 ```
@@ -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>
 ```
@@ -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.

@@ -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.
@@ -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.
@@ -1,6 +1,6 @@
 ---
 id: edgetpu
-title: EdgeTPU Errors
+title: Troubleshooting EdgeTPU
 ---

 ## USB Coral Not Detected
@@ -32,7 +32,7 @@ The USB coral can draw up to 900mA and this can be too much for some on-device U
 The USB coral has different IDs when it is uninitialized and initialized.

 - When running Frigate in a VM, Proxmox lxc, etc. you must ensure both device IDs are mapped.
- When running through the Home Assistant OS you may need to run the Full Access variant of the Frigate App with the _Protection mode_ switch disabled so that the coral can be accessed.
+- When running through the Home Assistant OS you may need to run the Full Access variant of the Frigate Add-on with the _Protection mode_ switch disabled so that the coral can be accessed.

 ### Synology 716+II running DSM 7.2.1-69057 Update 5

@@ -68,7 +68,8 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n

 The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run.

- In most cases https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
+- In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
+- For some newer Linux distros (for example, Ubuntu 22.04+), https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.

 ## Attempting to load TPU as pci & Fatal Python error: Illegal instruction

@@ -1,6 +1,6 @@
 ---
 id: gpu
-title: GPU Errors
+title: Troubleshooting GPU
 ---

 ## OpenVINO
@@ -1,134 +0,0 @@
---
-id: memory
-title: Memory Usage
---
-
-Frigate includes built-in memory profiling using [memray](https://bloomberg.github.io/memray/) to help diagnose memory issues. This feature allows you to profile specific Frigate modules to identify memory leaks, excessive allocations, or other memory-related problems.
-
-## Enabling Memory Profiling
-
-Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
-
-```yaml
-# docker-compose example
-services:
-  frigate:
-    ...
-    environment:
-      - FRIGATE_MEMRAY_MODULES=frigate.embeddings,frigate.capture
-```
-
-```bash
-# docker run example
-docker run -e FRIGATE_MEMRAY_MODULES="frigate.embeddings" \
-   ...
-   --name frigate <frigate_image>
-```
-
-### Module Names
-
-Frigate processes are named using a module-based naming scheme. Common module names include:
-
- `frigate.review_segment_manager` - Review segment processing
- `frigate.recording_manager` - Recording management
- `frigate.capture` - Camera capture processes (all cameras with this module name)
- `frigate.process` - Camera processing/tracking (all cameras with this module name)
- `frigate.output` - Output processing
- `frigate.audio_manager` - Audio processing
- `frigate.embeddings` - Embeddings processing
-
-You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
-
-```bash
-FRIGATE_MEMRAY_MODULES=frigate.capture:front_door
-```
-
-When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
-
-## How It Works
-
-1. **Binary File Creation**: When profiling is enabled, memray creates a binary file (`.bin`) in `/config/memray_reports/` that is updated continuously in real-time as the process runs.
-
-2. **Automatic HTML Generation**: On normal process exit, Frigate automatically:
-
-   - Stops memray tracking
-   - Generates an HTML flamegraph report
-   - Saves it to `/config/memray_reports/<module_name>.html`
-
-3. **Crash Recovery**: If a process crashes (SIGKILL, segfault, etc.), the binary file is preserved with all data up to the crash point. You can manually generate the HTML report from the binary file.
-
-## Viewing Reports
-
-### Automatic Reports
-
-After a process exits normally, you'll find HTML reports in `/config/memray_reports/`. Open these files in a web browser to view interactive flamegraphs showing memory usage patterns.
-
-### Manual Report Generation
-
-If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
-
- Run `memray` inside the Frigate container:
-
-```bash
-docker-compose exec frigate memray flamegraph /config/memray_reports/<module_name>.bin
-# or
-docker exec -it <container_name_or_id> memray flamegraph /config/memray_reports/<module_name>.bin
-```
-
- You can also copy the `.bin` file to the host and run `memray` locally if you have it installed:
-
-```bash
-docker cp <container_name_or_id>:/config/memray_reports/<module_name>.bin /tmp/
-memray flamegraph /tmp/<module_name>.bin
-```
-
-## Understanding the Reports
-
-Memray flamegraphs show:
-
- **Memory allocations over time**: See where memory is being allocated in your code
- **Call stacks**: Understand the full call chain leading to allocations
- **Memory hotspots**: Identify functions or code paths that allocate the most memory
- **Memory leaks**: Spot patterns where memory is allocated but not freed
-
-The interactive HTML reports allow you to:
-
- Zoom into specific time ranges
- Filter by function names
- View detailed allocation information
- Export data for further analysis
-
-## Best Practices
-
-1. **Profile During Issues**: Enable profiling when you're experiencing memory issues, not all the time, as it adds some overhead.
-
-2. **Profile Specific Modules**: Instead of profiling everything, focus on the modules you suspect are causing issues.
-
-3. **Let Processes Run**: Allow processes to run for a meaningful duration to capture representative memory usage patterns.
-
-4. **Check Binary Files**: If HTML reports aren't generated automatically (e.g., after a crash), check for `.bin` files in `/config/memray_reports/` and generate reports manually.
-
-5. **Compare Reports**: Generate reports at different times to compare memory usage patterns and identify trends.
-
-## Troubleshooting
-
-### No Reports Generated
-
- Check that the environment variable is set correctly
- Verify the module name matches exactly (case-sensitive)
- Check logs for memray-related errors
- Ensure `/config/memray_reports/` directory exists and is writable
-
-### Process Crashed Before Report Generation
-
- Look for `.bin` files in `/config/memray_reports/`
- Manually generate HTML reports using: `memray flamegraph <file>.bin`
- The binary file contains all data up to the crash point
-
-### Reports Show No Data
-
- Ensure the process ran long enough to generate meaningful data
- Check that memray is properly installed (included by default in Frigate)
- Verify the process actually started and ran (check process logs)
-
-For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).
@@ -1,6 +1,6 @@
 ---
 id: recordings
-title: Recordings Errors
+title: Troubleshooting Recordings
 ---

 ## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?
@@ -10,7 +10,7 @@ const config: Config = {
  baseUrl: "/",
  onBrokenLinks: "throw",
  onBrokenMarkdownLinks: "warn",
-  favicon: "img/branding/favicon.ico",
+  favicon: "img/favicon.ico",
  organizationName: "blakeblackshear",
  projectName: "frigate",
  themes: [
@@ -83,17 +83,6 @@ const config: Config = {
      },
    },
    prism: {
-      magicComments:[
-        {
-          className: 'theme-code-block-highlighted-line',
-          line: 'highlight-next-line',
-          block: {start: 'highlight-start', end: 'highlight-end'},
-        },
-        {
-          className: 'code-block-error-line',
-          line: 'highlight-error-line',
-        },
-      ],
      additionalLanguages: ["bash", "json"],
    },
    languageTabs: [
@@ -127,8 +116,8 @@ const config: Config = {
      title: "Frigate",
      logo: {
        alt: "Frigate",
-        src: "img/branding/logo.svg",
-        srcDark: "img/branding/logo-dark.svg",
+        src: "img/logo.svg",
+        srcDark: "img/logo-dark.svg",
      },
      items: [
        {
@@ -181,7 +170,7 @@ const config: Config = {
          ],
        },
      ],
-      copyright: `Copyright © ${new Date().getFullYear()} Frigate, Inc.`,
+      copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
    },
  },
  plugins: [
@@ -18,14 +18,14 @@
  },
  "dependencies": {
    "@docusaurus/core": "^3.7.0",
-    "@docusaurus/plugin-content-docs": "^3.7.0",
+    "@docusaurus/plugin-content-docs": "^3.6.3",
    "@docusaurus/preset-classic": "^3.7.0",
-    "@docusaurus/theme-mermaid": "^3.7.0",
+    "@docusaurus/theme-mermaid": "^3.6.3",
    "@inkeep/docusaurus": "^2.0.16",
    "@mdx-js/react": "^3.1.0",
    "clsx": "^2.1.1",
-    "docusaurus-plugin-openapi-docs": "^4.5.1",
-    "docusaurus-theme-openapi-docs": "^4.5.1",
+    "docusaurus-plugin-openapi-docs": "^4.3.1",
+    "docusaurus-theme-openapi-docs": "^4.3.1",
    "prism-react-renderer": "^2.4.1",
    "raw-loader": "^4.0.2",
    "react": "^18.3.1",
@@ -44,9 +44,9 @@
    ]
  },
  "devDependencies": {
-    "@docusaurus/module-type-aliases": "^3.7.0",
-    "@docusaurus/types": "^3.7.0",
-    "@types/react": "^18.3.27"
+    "@docusaurus/module-type-aliases": "^3.4.0",
+    "@docusaurus/types": "^3.4.0",
+    "@types/react": "^18.3.7"
  },
  "engines": {
    "node": ">=18.0"
@@ -28,7 +28,7 @@ const sidebars: SidebarsConfig = {
        {
          type: "link",
          label: "Go2RTC Configuration Reference",
-          href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.13#configuration",
+          href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.10#configuration",
        } as PropSidebarItemLink,
      ],
      Detectors: [
@@ -94,7 +94,6 @@ const sidebars: SidebarsConfig = {
      "Extra Configuration": [
        "configuration/authentication",
        "configuration/notifications",
-        "configuration/profiles",
        "configuration/ffmpeg_presets",
        "configuration/pwa",
        "configuration/tls",
@@ -130,27 +129,8 @@ const sidebars: SidebarsConfig = {
    Troubleshooting: [
      "troubleshooting/faqs",
      "troubleshooting/recordings",
-      "troubleshooting/dummy-camera",
-      {
-        type: "category",
-        label: "Troubleshooting Hardware",
-        link: {
-          type: "generated-index",
-          title: "Troubleshooting Hardware",
-          description: "Troubleshooting Problems with Hardware",
-        },
-        items: ["troubleshooting/gpu", "troubleshooting/edgetpu"],
-      },
-      {
-        type: "category",
-        label: "Troubleshooting Resource Usage",
-        link: {
-          type: "generated-index",
-          title: "Troubleshooting Resource Usage",
-          description: "Troubleshooting issues with resource usage",
-        },
-        items: ["troubleshooting/cpu", "troubleshooting/memory"],
-      },
+      "troubleshooting/gpu",
+      "troubleshooting/edgetpu",
    ],
    Development: [
      "development/contributing",
@@ -1,23 +0,0 @@
-import React from "react";
-
-export default function CommunityBadge() {
-  return (
-    <span
-      title="This detector is maintained by community members who provide code, maintenance, and support. See the contributing boards documentation for more information."
-      style={{
-        display: "inline-block",
-        backgroundColor: "#f1f3f5",
-        color: "#24292f",
-        fontSize: "11px",
-        fontWeight: 600,
-        padding: "2px 6px",
-        borderRadius: "3px",
-        border: "1px solid #d1d9e0",
-        marginLeft: "4px",
-        cursor: "help",
-      }}
-    >
-      Community Supported
-    </span>
-  );
-}
@@ -1,18 +1,13 @@
 .alert {
-  padding: 12px;
-  background: #fff8e6;
-  border-bottom: 1px solid #ffd166;
-  text-align: center;
-  font-size: 15px;
-}
-
-[data-theme="dark"] .alert {
-  background: #3b2f0b;
-  border-bottom: 1px solid #665c22;
-}
-
-.alert a {
-  color: #1890ff;
-  font-weight: 500;
-  margin-left: 6px;
-}
+    padding: 12px;
+    background: #fff8e6;
+    border-bottom: 1px solid #ffd166;
+    text-align: center;
+    font-size: 15px;
+  }
+  
+  .alert a {
+    color: #1890ff;
+    font-weight: 500;
+    margin-left: 6px;
+  }
@@ -1,201 +0,0 @@
-import React, { useState, useEffect } from "react";
-import Admonition from "@theme/Admonition";
-import styles from "./styles.module.css";
-
-const ShmCalculator = () => {
-  const [width, setWidth] = useState(1280);
-  const [height, setHeight] = useState(720);
-  const [cameraCount, setCameraCount] = useState(1);
-  const [result, setResult] = useState("26.32MB");
-  const [singleCameraShm, setSingleCameraShm] = useState("26.32MB");
-  const [totalShm, setTotalShm] = useState("26.32MB");
-
-  const calculate = () => {
-    if (!width || !height || !cameraCount) {
-      setResult("Please enter valid values");
-      setSingleCameraShm("-");
-      setTotalShm("-");
-      return;
-    }
-
-    // Single camera base SHM calculation (excluding logs)
-    // Formula: (width * height * 1.5 * 20 + 270480) / 1048576
-    const singleCameraBase =
-      (width * height * 1.5 * 20 + 270480) / 1048576;
-    setSingleCameraShm(`${singleCameraBase.toFixed(2)}mb`);
-
-    // Total SHM calculation (multiple cameras, including logs)
-    const totalBase = singleCameraBase * cameraCount;
-    const finalResult = totalBase + 40; // Default includes logs +40mb
-
-    setTotalShm(`${(totalBase + 40).toFixed(2)}mb`);
-
-    // Format result
-    if (finalResult < 1) {
-      setResult(`${(finalResult * 1024).toFixed(2)}kb`);
-    } else if (finalResult >= 1024) {
-      setResult(`${(finalResult / 1024).toFixed(2)}gb`);
-    } else {
-      setResult(`${finalResult.toFixed(2)}mb`);
-    }
-  };
-
-  const formatWithUnit = (value) => {
-    const match = value.match(/^([\d.]+)(mb|kb|gb)$/i);
-    if (match) {
-      return (
-        <>
-          {match[1]}<span className={styles.unit}>{match[2]}</span>
-        </>
-      );
-    }
-    return value;
-  };
-
-  const applyPreset = (w, h, count) => {
-    setWidth(w);
-    setHeight(h);
-    setCameraCount(count);
-    calculate();
-  };
-
-  useEffect(() => {
-    calculate();
-  }, [width, height, cameraCount]);
-
-  return (
-    <div className={styles.shmCalculator}>
-      <div className={styles.card}>
-        <h3 className={styles.title}>SHM Calculator</h3>
-        <p className={styles.description}>
-          Calculate required shared memory (SHM) based on camera resolution and
-          count
-        </p>
-
-        <Admonition type="note">
-          The resolution below is the <strong>detect</strong> stream resolution,
-          not the <strong>record</strong> stream resolution. SHM size is
-          determined by the detect resolution used for object detection.{" "}
-          <a href="/frigate/camera_setup#choosing-a-detect-resolution">
-            Learn more about choosing a detect resolution.
-          </a>
-        </Admonition>
-
-        {width * height > 1280 * 720 && (
-          <Admonition type="warning">
-            Using a detect resolution higher than 720p is not recommended.
-            Higher resolutions do not improve object detection accuracy and will
-            consume significantly more resources.
-          </Admonition>
-        )}
-
-        <div className="row">
-          <div className="col col--6">
-            <div className={styles.formGroup}>
-              <label htmlFor="width" className={styles.label}>
-                Width:
-              </label>
-              <input
-                id="width"
-                type="number"
-                min="1"
-                placeholder="e.g.: 1280"
-                className={styles.input}
-                value={width}
-                onChange={(e) => setWidth(Number(e.target.value))}
-              />
-            </div>
-          </div>
-
-          <div className="col col--6">
-            <div className={styles.formGroup}>
-              <label htmlFor="height" className={styles.label}>
-                Height:
-              </label>
-              <input
-                id="height"
-                type="number"
-                min="1"
-                placeholder="e.g.: 720"
-                className={styles.input}
-                value={height}
-                onChange={(e) => setHeight(Number(e.target.value))}
-              />
-            </div>
-          </div>
-        </div>
-
-        <div className={styles.formGroup}>
-          <label htmlFor="cameraCount" className={styles.label}>
-            Camera Count:
-          </label>
-          <input
-            id="cameraCount"
-            type="number"
-            min="1"
-            placeholder="e.g.: 8"
-            className={styles.input}
-            value={cameraCount}
-            onChange={(e) => setCameraCount(Number(e.target.value))}
-          />
-        </div>
-
-        <div className={styles.resultSection}>
-          <h4>Calculation Result</h4>
-          <div className={styles.resultValue}>
-            <span className={styles.resultNumber}>{formatWithUnit(result)}</span>
-          </div>
-          <div className={styles.formulaDisplay}>
-            <p>
-              <strong>Single Camera:</strong> {formatWithUnit(singleCameraShm)}
-            </p>
-            <p>
-              <strong>Formula:</strong> (width × height × 1.5 × 20 + 270480) ÷
-              1048576
-            </p>
-            {cameraCount > 1 && (
-              <p>
-                <strong>Total ({cameraCount} cameras):</strong> {formatWithUnit(totalShm)}
-              </p>
-            )}
-            <p>
-              <strong>With Logs:</strong> + 40<span className={styles.unit}>mb</span>
-            </p>
-          </div>
-        </div>
-
-        <div className={styles.presets}>
-          <h4>Common Presets</h4>
-          <div className={styles.presetButtons}>
-            <button
-              className="button button--outline button--primary button--sm"
-              onClick={() => applyPreset(640, 360, 1)}
-            >
-              640x360 × 1
-            </button>
-            <button
-              className="button button--outline button--primary button--sm"
-              onClick={() => applyPreset(1280, 720, 1)}
-            >
-              1280x720 × 1
-            </button>
-            <button
-              className="button button--outline button--primary button--sm"
-              onClick={() => applyPreset(1280, 720, 4)}
-            >
-              1280x720 × 4
-            </button>
-            <button
-              className="button button--outline button--primary button--sm"
-              onClick={() => applyPreset(1280, 720, 8)}
-            >
-              1280x720 × 8
-            </button>
-          </div>
-        </div>
-      </div>
-    </div>
-  );
-};
-
-export default ShmCalculator;
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Josh Hawkins	ab3c12b89e	rename component for clarity CameraSettingsView is now CameraReviewSettingsView	2025-11-17 11:34:28 -06:00
Josh Hawkins	5272b83959	clean up camera edit form	2025-11-17 11:31:06 -06:00
Josh Hawkins	afe7fbad14	remove camera edit dropdown pushing camera editing from the UI to 0.18	2025-11-17 11:30:48 -06:00
Josh Hawkins	05973f658a	admin-only endpoint to return unmaksed camera paths and go2rtc streams	2025-11-17 11:23:43 -06:00
Josh Hawkins	ff90dbf208	ensure viewer roles are available in create user dialog	2025-11-17 08:55:11 -06:00