Add docs about backchannel and two way talk takeover

.github/copilot-instructions.md

@@ -1,385 +1,2 @@
-# 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")
-```
-
-### ✅ 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")
-```
-
-## 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
+Never write strings in the frontend directly, always write to and reference the relevant translations file.
+Always conform new and refactored code to the existing coding style in the project.

.github/workflows/pull_request.yml

@@ -19,9 +19,9 @@ jobs:
       - uses: actions/checkout@v6
         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
@@ -35,7 +35,7 @@ jobs:
       - uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
           node-version: 20.x
       - run: npm install
@@ -78,7 +78,7 @@ jobs:
         uses: actions/checkout@v6
         with:
           persist-credentials: false
-      - uses: actions/setup-node@v6
+      - uses: actions/setup-node@master
         with:
           node-version: 20.x
       - name: Install devcontainer cli

.github/workflows/release.yml

@@ -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

LICENSE

@@ -1,6 +1,6 @@
 The MIT License
 
-Copyright (c) 2026 Frigate, Inc. (Frigate™)
+Copyright (c) 2025 Frigate LLC (Frigate™)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.17.2
+VERSION = 0.17.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty

README.md

@@ -40,7 +40,7 @@ If you would like to make a donation to support development, please use [Github
 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.
+- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate LLC** and are **not** covered by the MIT License.
 
 Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of our brand assets.
 
@@ -67,7 +67,7 @@ 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
@@ -80,4 +80,4 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
 
 ---
 
-**Copyright © 2026 Frigate, Inc.**
+**Copyright © 2025 Frigate LLC.**

README_CN.md

@@ -4,14 +4,14 @@
 
 # Frigate NVR™ - 一个具有实时目标检测的本地 NVR
 
-<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) | \[简体中文\]
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
+<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>
+
 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。
@@ -38,10 +38,9 @@
 ## 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。
+**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate LLC 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
@@ -87,4 +86,4 @@ Bilibili：https://space.bilibili.com/3546894915602564
 
 ---
 
-**Copyright © 2026 Frigate, Inc.**
+**Copyright © 2025 Frigate LLC.**

TRADEMARK.md

@@ -6,7 +6,7 @@ This document outlines the policy regarding the use of the trademarks associated
 
 ## 1. Our Trademarks
 
-The following terms and visual assets are trademarks (the "Marks") of **Frigate, Inc.**:
+The following terms and visual assets are trademarks (the "Marks") of **Frigate LLC**:
 
 - **Frigate™**
 - **Frigate NVR™**
@@ -14,7 +14,7 @@ The following terms and visual assets are trademarks (the "Marks") of **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.
+Frigate LLC 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
 
@@ -25,7 +25,7 @@ The software in this repository is licensed under the [MIT License](LICENSE).
 - 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.
+You may not use the Marks in any way that is not explicitly permitted by this policy or by written agreement with Frigate LLC.
 
 ## 3. Acceptable Use
 
@@ -40,7 +40,7 @@ You may use the Marks without prior written permission in the following specific
 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.
+- **Implying Affiliation:** You may not use the Marks in a way that suggests your project is official, sponsored by, or endorsed by Frigate LLC.
 - **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`).
 

docker/hailo8l/user_installation.sh

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

docker/main/Dockerfile

@@ -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}"
 

docker/main/requirements-wheels.txt

@@ -21,7 +21,7 @@ onvif-zeep-async == 4.0.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
-peewee_migrate == 1.14.*
+peewee_migrate == 1.13.*
 psutil == 7.1.*
 pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
@@ -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.*

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

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

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

@@ -17,10 +17,6 @@ from frigate.const import (
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 from frigate.util.config import find_config_file
-from frigate.util.services import (
-    is_go2rtc_arbitrary_exec_allowed,
-    is_restricted_go2rtc_source,
-)
 
 sys.path.remove("/opt/frigate")
 
@@ -113,21 +109,14 @@ if LIBAVFORMAT_VERSION_MAJOR < 59:
     elif go2rtc_config["ffmpeg"].get("rtsp") is None:
         go2rtc_config["ffmpeg"]["rtsp"] = rtsp_args
 
-
-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 is_restricted_go2rtc_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."
@@ -135,47 +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 is_restricted_go2rtc_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]
-
-    elif isinstance(stream, dict):
-        # The map form ({"url": ...}) lets go2rtc resolve the source
-        # recursively, so it is effectively a dynamic way to generate the URL
-        # for a stream. That can only be backed by an exec source, so it cannot
-        # be allowed unless arbitrary exec is explicitly enabled. When it is
-        # enabled, leave the map untouched for go2rtc to resolve.
-        if not is_go2rtc_arbitrary_exec_allowed():
-            print(
-                f"[ERROR] Stream '{name}' uses a dynamic source format 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
-
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
     birdseye: dict[str, Any] = config.get("birdseye")

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

@@ -259,7 +259,6 @@ http {
             include proxy.conf;
 
             proxy_cache api_cache;
-            proxy_cache_key "$scheme$proxy_host$request_uri|$role|$groups|$user";
             proxy_cache_lock on;
             proxy_cache_use_stale updating;
             proxy_cache_valid 200 5s;

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

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

docs/docs/configuration/advanced.md

@@ -44,21 +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}"
+  VARIABLE_NAME: variable_value
 ```
 
 #### TensorFlow Thread Configuration

docs/docs/configuration/audio_detectors.md

@@ -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
 

docs/docs/configuration/authentication.md

@@ -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

docs/docs/configuration/birdseye.md

@@ -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:

docs/docs/configuration/camera_specific.md

@@ -23,7 +23,6 @@ Some cameras support h265 with different formats, but Safari only supports the a
 cameras:
   h265_cam: # <------ Doesn't matter what the camera is called
     ffmpeg:
-      # highlight-next-line
       apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
 ```
 
@@ -31,7 +30,7 @@ cameras:
 
 Note that mjpeg cameras require encoding the video into h264 for recording, and restream roles. This will use significantly more CPU than if the cameras supported h264 feeds directly. It is recommended to use the restream role to create an h264 restream and then use that as the source for ffmpeg.
 
-```yaml {3,10}
+```yaml
 go2rtc:
   streams:
     mjpeg_cam: "ffmpeg:http://your_mjpeg_stream_url#video=h264#hardware" # <- use hardware acceleration to create an h264 stream usable for other components.
@@ -97,7 +96,6 @@ This camera is H.265 only. To be able to play clips on some devices (like MacOs
 cameras:
   annkec800: # <------ Name the camera
     ffmpeg:
-      # highlight-next-line
       apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
       output_args:
         record: preset-record-generic-audio-aac
@@ -190,10 +188,10 @@ go2rtc:
     # example for connectin to a Reolink camera that supports two way talk
     your_reolink_camera_twt:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
     your_reolink_camera_twt_sub:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub"
+      - "rtsp://username:password@reolink_ip/Preview_01_sub
     # example for connecting to a Reolink NVR
     your_reolink_camera_via_nvr:
       - "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
@@ -229,12 +227,6 @@ cameras:
 
 ### Unifi Protect Cameras
 
-:::note 
-
-Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.
-
-:::
-
 Unifi protect cameras require the rtspx stream to be used with go2rtc.
 To utilize a Unifi protect camera, modify the rtsps link to begin with rtspx.
 Additionally, remove the "?enableSrtp" from the end of the Unifi link.
@@ -260,10 +252,6 @@ ffmpeg:
 
 TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.
 
-### Wyze Wireless Cameras
-
-Some community members have found better performance on Wyze cameras by using an alternative firmware known as [Thingino](https://thingino.com/).
-
 ## USB Cameras (aka Webcams)
 
 To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
@@ -276,7 +264,7 @@ To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's
 
 - In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
 
-```yaml {4,11-12}
+```
 go2rtc:
   streams:
     usb_camera:

docs/docs/configuration/cameras.md

@@ -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: ...
@@ -79,12 +79,6 @@ 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: ""`.
@@ -100,19 +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            |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |

docs/docs/configuration/custom_classification/object_classification.md

@@ -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.
@@ -27,20 +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.
-
-:::
+  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
 
 ## Assignment Requirements
 
@@ -80,17 +73,13 @@ 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:
 
 ### 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.
+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. Include a `none` class for objects that don't fit any specific category.
 
 ### Step 2: Assign Training Examples
 
@@ -98,8 +87,6 @@ The system will automatically generate example images from detected objects matc
 
 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.
-
 ### Improving the Model
 
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
@@ -107,24 +94,3 @@ If examples for some of your classes do not appear in the grid, you can continue
 - **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

docs/docs/configuration/custom_classification/state_classification.md

@@ -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.
@@ -48,8 +46,6 @@ 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:
@@ -64,9 +60,11 @@ Choose one or more cameras and draw a rectangle over the area of interest for ea
 
 ### 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.
+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.
 
-Once some images are assigned, training will begin automatically.
+**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.
+
+Once all images are assigned, training will begin automatically.
 
 ### Improving the Model
 
@@ -74,35 +72,3 @@ Once some images are assigned, training will begin automatically.
 - **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
 - **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
 - **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
-
-## 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.

docs/docs/configuration/face_recognition.md

@@ -9,7 +9,7 @@ Face recognition identifies known individuals by matching detected faces with pr
 
 ### Face Detection
 
-When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/index.md#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
+When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
 
 When running a default COCO model or another model that does not include `face` as a detectable label, face detection will run via CV2 using a lightweight DNN model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
 
@@ -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).
 

docs/docs/configuration/genai.md

@@ -0,0 +1,231 @@
+---
+id: genai
+title: Generative AI
+---
+
+Generative AI can be used to automatically generate descriptive text based on the thumbnails of your tracked objects. This helps with [Semantic Search](/configuration/semantic_search) in Frigate to provide more context about your tracked objects. Descriptions are accessed via the _Explore_ view in the Frigate UI by clicking on a tracked object's thumbnail.
+
+Requests for a description are sent off automatically to your AI provider at the end of the tracked object's lifecycle, or can optionally be sent earlier after a number of significantly changed frames, for example in use in more real-time notifications. Descriptions can also be regenerated manually via the Frigate UI. Note that if you are manually entering a description for tracked objects prior to its end, this will be overwritten by the generated response.
+
+## Configuration
+
+Generative AI can be enabled for all cameras or only for specific cameras. If GenAI is disabled for a camera, you can still manually generate descriptions for events using the HTTP API. There are currently 3 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI section below.
+
+To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+
+```yaml
+genai:
+  provider: gemini
+  api_key: "{FRIGATE_GEMINI_API_KEY}"
+  model: gemini-2.0-flash
+
+cameras:
+  front_camera:
+    genai:
+      enabled: True # <- enable GenAI for your front camera
+      use_snapshot: True
+      objects:
+        - person
+      required_zones:
+        - steps
+  indoor_camera:
+    objects:
+      genai:
+        enabled: False # <- disable GenAI for your indoor camera
+```
+
+By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
+
+Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
+
+Generative AI can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
+
+## Ollama
+
+:::warning
+
+Using Ollama on CPU is not recommended, high inference times make using Generative AI impractical.
+
+:::
+
+[Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It provides a nice API over [llama.cpp](https://github.com/ggerganov/llama.cpp). It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
+
+Most of the 7b parameter 4-bit vision models will fit inside 8GB of VRAM. There is also a [Docker container](https://hub.docker.com/r/ollama/ollama) available.
+
+Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_PARALLEL=1` and choose a `OLLAMA_MAX_QUEUE` and `OLLAMA_MAX_LOADED_MODELS` values that are appropriate for your hardware and preferences. See the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests).
+
+### Supported Models
+
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+
+:::note
+
+You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
+
+:::
+
+### Configuration
+
+```yaml
+genai:
+  provider: ollama
+  base_url: http://localhost:11434
+  model: qwen3-vl:4b
+```
+
+## Google Gemini
+
+Google Gemini has a free tier allowing [15 queries per minute](https://ai.google.dev/pricing) to the API, which is more than sufficient for standard Frigate usage.
+
+### Supported Models
+
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
+
+### Get API Key
+
+To start using Gemini, you must first get an API key from [Google AI Studio](https://aistudio.google.com).
+
+1. Accept the Terms of Service
+2. Click "Get API Key" from the right hand navigation
+3. Click "Create API key in new project"
+4. Copy the API key for use in your config
+
+### Configuration
+
+```yaml
+genai:
+  provider: gemini
+  api_key: "{FRIGATE_GEMINI_API_KEY}"
+  model: gemini-2.0-flash
+```
+
+:::note
+
+To use a different Gemini-compatible API endpoint, set the `GEMINI_BASE_URL` environment variable to your provider's API URL.
+
+:::
+
+## OpenAI
+
+OpenAI does not have a free tier for their API. With the release of gpt-4o, pricing has been reduced and each generation should cost fractions of a cent if you choose to go this route.
+
+### Supported Models
+
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
+
+### Get API Key
+
+To start using OpenAI, you must first [create an API key](https://platform.openai.com/api-keys) and [configure billing](https://platform.openai.com/settings/organization/billing/overview).
+
+### Configuration
+
+```yaml
+genai:
+  provider: openai
+  api_key: "{FRIGATE_OPENAI_API_KEY}"
+  model: gpt-4o
+```
+
+:::note
+
+To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` environment variable to your provider's API URL.
+
+:::
+
+## Azure OpenAI
+
+Microsoft offers several vision models through Azure OpenAI. A subscription is required.
+
+### Supported Models
+
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
+
+### Create Resource and Get API Key
+
+To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
+
+### Configuration
+
+```yaml
+genai:
+  provider: azure_openai
+  base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
+  model: gpt-5-mini
+  api_key: "{FRIGATE_OPENAI_API_KEY}"
+```
+
+## Usage and Best Practices
+
+Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
+
+While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
+
+### Using GenAI for notifications
+
+Frigate provides an [MQTT topic](/integrations/mqtt), `frigate/tracked_object_update`, that is updated with a JSON payload containing `event_id` and `description` when your AI provider returns a description for a tracked object. This description could be used directly in notifications, such as sending alerts to your phone or making audio announcements. If additional details from the tracked object are needed, you can query the [HTTP API](/integrations/api/event-events-event-id-get) using the `event_id`, eg: `http://frigate_ip:5000/api/events/<event_id>`.
+
+If looking to get notifications earlier than when an object ceases to be tracked, an additional send trigger can be configured of `after_significant_updates`.
+
+```yaml
+genai:
+  send_triggers:
+    tracked_object_end: true # default
+    after_significant_updates: 3 # how many updates to a tracked object before we should send an image
+```
+
+## Custom Prompts
+
+Frigate sends multiple frames from the tracked object along with a prompt to your Generative AI provider asking it to generate a description. The default prompt is as follows:
+
+```
+Analyze the sequence of images containing the {label}. Focus on the likely intent or behavior of the {label} based on its actions and movement, rather than describing its appearance or the surroundings. Consider what the {label} is doing, why, and what it might do next.
+```
+
+:::tip
+
+Prompts can use variable replacements `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
+
+:::
+
+You are also able to define custom prompts in your configuration.
+
+```yaml
+genai:
+  provider: ollama
+  base_url: http://localhost:11434
+  model: llava
+
+objects:
+  prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
+  object_prompts:
+    person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
+    car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
+```
+
+Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.
+
+```yaml
+cameras:
+  front_door:
+    objects:
+      genai:
+        enabled: True
+        use_snapshot: True
+        prompt: "Analyze the {label} in these images from the {camera} security camera at the front door. Focus on the actions and potential intent of the {label}."
+        object_prompts:
+          person: "Examine the person in these images. What are they doing, and how might their actions suggest their purpose (e.g., delivering something, approaching, leaving)? If they are carrying or interacting with a package, include details about its source or destination."
+          cat: "Observe the cat in these images. Focus on its movement and intent (e.g., wandering, hunting, interacting with objects). If the cat is near the flower pots or engaging in any specific actions, mention it."
+        objects:
+          - person
+          - cat
+        required_zones:
+          - steps
+```
+
+### Experiment with prompts
+
+Many providers also have a public facing chat interface for their models. Download a couple of different thumbnails or snapshots from Frigate and try new things in the playground to get descriptions to your liking before updating the prompt in Frigate.
+
+- OpenAI - [ChatGPT](https://chatgpt.com)
+- Gemini - [Google AI Studio](https://aistudio.google.com)
+- Ollama - [Open WebUI](https://docs.openwebui.com/)

docs/docs/configuration/genai/config.md

@@ -17,23 +17,11 @@ Using Ollama on CPU is not recommended, high inference times make using Generati
 
 :::
 
-[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.
+[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://docs.ollama.com/faq#how-does-ollama-handle-concurrent-requests).
-
-### 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.
-- **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, Frigate will always use instruct-style prompts and specifically disables thinking-mode behaviors to ensure concise, useful responses.
-
-**Recommendation:**
-Always select the `-instruct` or documented instruct/tagged variant of any model you use in your Frigate configuration. If in doubt, refer to your model provider’s documentation or model library for guidance on the correct model variant to use.
+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
 
@@ -53,12 +41,12 @@ If you are trying to use a single model for Frigate and HomeAssistant, it will n
 
 The following models are recommended:
 
-| Model         | Notes                                                                |
-| ------------- | -------------------------------------------------------------------- |
-| `qwen3-vl`    | Strong visual and situational understanding, higher vram requirement |
-| `Intern3.5VL` | Relatively fast with good vision comprehension                       |
-| `gemma3`      | Strong frame-to-frame understanding, slower inference times          |
-| `qwen2.5-vl`  | Fast but capable model with good vision comprehension                |
+| Model             | Notes                                                                |
+| ----------------- | -------------------------------------------------------------------- |
+| `qwen3-vl`        | Strong visual and situational understanding, higher vram requirement |
+| `Intern3.5VL`     | Relatively fast with good vision comprehension                       |
+| `gemma3`          | Strong frame-to-frame understanding, slower inference times          |
+| `qwen2.5-vl`      | Fast but capable model with good vision comprehension                |
 
 :::note
 
@@ -66,26 +54,26 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 
 :::
 
-#### Ollama Cloud models
-
-Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
-
 ### Configuration
 
 ```yaml
 genai:
   provider: ollama
   base_url: http://localhost:11434
-  model: qwen3-vl:4b
+  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
 ```
 
 ## 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.
+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).
+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`.
 
 ### Get API Key
 
@@ -102,32 +90,16 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
 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 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).
+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
 
@@ -148,41 +120,23 @@ 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
 
 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).
+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
 
-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
 
 ```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}"
 ```

docs/docs/configuration/genai/objects.md

@@ -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."

docs/docs/configuration/genai/review_summaries.md

@@ -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 +108,12 @@ 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).
+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.
 
 ### 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.
+Review reports can be requested via the [API](/integrations/api#review-summarization) 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.

docs/docs/configuration/hardware_acceleration_enrichments.md

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

docs/docs/configuration/hardware_acceleration_video.md

@@ -3,72 +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

docs/docs/configuration/index.md

@@ -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
@@ -167,16 +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
@@ -234,16 +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

docs/docs/configuration/license_plate_recognition.md

@@ -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 and are applied _before_ the `format` regex, if specified. 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.
@@ -375,38 +375,41 @@ 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.
 
      ```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.
 
      ```yaml
      logger:
        default: info
        logs:
-        # highlight-next-line
          frigate.data_processing.common.license_plate: debug
      ```
 
 3. Ensure your plates are being _detected_.
 
    If you are using a Frigate+ or `license_plate` detecting model:
+
    - Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
    - 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_.
+
    - 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 +432,6 @@ If you are using a model that natively detects `license_plate`, add an _object m
 
 If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
 
-### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
+### I see "Error running ... model" in my logs. How can I fix this?
 
 This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.

docs/docs/configuration/live.md

@@ -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:
     ...
@@ -222,28 +221,34 @@ Note that disabling a camera through the config file (`enabled: False`) removes
 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)
 
@@ -264,18 +269,21 @@ 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).
@@ -315,7 +323,9 @@ When your browser runs into problems playing back your camera streams, it will l
    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)
 

docs/docs/configuration/masks.md

@@ -3,8 +3,6 @@ id: masks
 title: Masks
 ---
 
-Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with `required_zones` is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
-
 ## Motion masks
 
 Motion masks are used to prevent unwanted types of motion from triggering detection. Try watching the Debug feed (Settings --> Debug) with `Motion Boxes` enabled to see what may be regularly detected as motion. For example, you want to mask out your timestamp, the sky, rooftops, etc. Keep in mind that this mask only prevents motion from being detected and does not prevent objects from being detected if object detection was started due to motion in unmasked areas. Motion is also used during object tracking to refine the object detection area in the next frame. _Over-masking will make it more difficult for objects to be tracked._
@@ -19,15 +17,6 @@ Object filter masks can be used to filter out stubborn false positives in fixed
 
 ![object mask](/img/bottom-center-mask.jpg)
 
-## Which tool do I need?
-
-| What you're trying to do                                                                                                       | Recommended tool                                                                                            | How it works                                                                                                                                                                                |
-| ------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Don't get alerts or recordings for activity in an area (e.g., the sidewalk in front of your house)                             | A [zone](zones.md) combined with `review.alerts.required_zones` (and/or `review.detections.required_zones`) | Frigate keeps detecting and tracking activity in the area, but a review item is only created once the bottom-center of an object's bounding box enters a required zone.                     |
-| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person)              | An **object filter mask** for that object type                                                              | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded.                                                      |
-| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask**                                                                                           | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
-| Stop tracking an object type altogether on this camera (e.g., you never care about cats)                                       | Remove the object from the camera's [`objects.track`](objects.md) list                                      | Frigate skips this object type entirely on this camera, regardless of where it appears.                                                                                                     |
-
 ## Using the mask creator
 
 To create a poly mask:
@@ -93,14 +82,3 @@ This is what `required_zones` are for. You should define a zone (remember this i
 > Maybe my specific situation just warrants this. I've just been having a hard time understanding the relevance of this information - it seems to be that it's exactly what would be expected when "masking out" an area of ANY image.
 
 That may be the case for you. Frigate will definitely work harder tracking people on the sidewalk to make sure it doesn't miss anyone who steps foot on your stoop. The trade off with the way you have it now is slower recognition of objects and potential misses. That may be acceptable based on your needs. Also, if your resolution is low enough on the detect stream, your regions may already be so big that they grab the entire object anyway.
-
-## Common mistakes
-
-**"I added a motion mask to ignore my driveway/sidewalk."**
-A motion mask doesn't hide an area from Frigate. Objects can still be detected and tracked inside a masked area. The mask only stops motion _in that area_ from triggering object detection. If you want activity on the sidewalk to never produce a review item, define a [zone](zones.md) over the area you DO care about (your stoop, your driveway) and add it to `review.alerts.required_zones`. Frigate will still see people on the sidewalk, but it won't create an alert until they cross into the zone.
-
-**"I added an object filter mask because I don't care about cars in my yard."**
-Object filter masks are for stubborn false positives at fixed locations, not for filtering whole areas or whole object types. If you only want alerts when a car enters the driveway, use a [zone](zones.md) with `required_zones`. If you don't care about a whole object type on this camera, remove it from [`objects.track`](objects.md).
-
-**"I masked everything except a thin strip on my stoop."**
-Heavy masking hurts tracking. Frigate uses motion near a tracked object's previous bounding box to decide where to look in the next frame; with most of the frame masked, an object walking from an unmasked area into a masked one effectively disappears and gets picked up as a "new" object when it reappears. For example: someone walks down your sidewalk, stops under a tree (masked area) to tie their shoe, then continues. Frigate sees that as two separate people and can create two separate review items. Because Frigate needs several consecutive frames above the confidence threshold to commit to a detection, each re-appearance can also delay or miss alerts. Use `required_zones` for "only alert me about this spot" and leave the surrounding area unmasked so tracking stays intact.

docs/docs/configuration/object_detectors.md

@@ -34,7 +34,7 @@ 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 />
 
@@ -65,7 +65,7 @@ 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
 
@@ -146,24 +146,20 @@ detectors:
 
 ### EdgeTPU Supported Models
 
-| Model                   | Notes                                       |
-| ----------------------- | ------------------------------------------- |
-| [Mobiledet](#mobiledet) | Default model                               |
-| [YOLOv9](#yolov9)       | More accurate but slower than default model |
+| Model                                 | Notes                                       |
+| ------------------------------------- | ------------------------------------------- |
+| [MobileNet v2](#ssdlite-mobilenet-v2) | Default model                               |
+| [YOLOv9](#yolo-v9)                    | More accurate but slower than default model |
 
-#### Mobiledet
+#### SSDLite MobileNet v2
 
 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
+A Tensorflow Lite is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an INT8 precision model.
 
-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.
+#### YOLO v9
 
-:::tip
-
-**Frigate+ Users:** Follow the [instructions](/integrations/plus#use-models) to set a model ID in your config file.
-
-:::
+[YOLOv9](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite) models that are compiled for Tensorflow Lite and properly quantized are supported, but not included by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`. Note that the model may require a custom label file (eg. [use this 17 label file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) for the model linked above.)
 
 <details>
   <summary>YOLOv9 Setup & Config</summary>
@@ -181,10 +177,10 @@ model:
   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
+  labelmap_path: /labelmap/labels-coco-17.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.
+Note that the labelmap uses a subset of the complete COCO label set that has only 17 objects.
 
 </details>
 
@@ -330,7 +326,7 @@ detectors:
 | [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
 | [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
 | [YOLOX](#yolox)                       | ✅  | ?   |                                                              |
-| [D-FINE / DEIMv2](#d-fine--deimv2)    | ❌  | ❌  |                                                              |
+| [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
 
 #### SSDLite MobileNet v2
 
@@ -464,13 +460,13 @@ model:
 
 </details>
 
-#### D-FINE / DEIMv2
+#### D-FINE
 
-[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
+[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
 
 :::warning
 
-Currently D-FINE / DEIMv2 models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
+Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
 
 :::
 
@@ -483,7 +479,7 @@ 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
@@ -499,31 +495,6 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
 
 </details>
 
-<details>
-  <summary>DEIMv2 Setup & Config</summary>
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
-  ov:
-    type: openvino
-    device: CPU
-
-model:
-  model_type: dfine
-  width: 640
-  height: 640
-  input_tensor: nchw
-  input_dtype: float
-  path: /config/model_cache/deimv2_hgnetv2_n.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-</details>
-
 ## Apple Silicon detector
 
 The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
@@ -597,13 +568,13 @@ $ 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).
@@ -628,12 +599,12 @@ $ 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.
@@ -673,7 +644,7 @@ The AMD GPU kernel is known problematic especially when converting models to mxr
 
 See [ONNX supported models](#supported-models) for supported models, there are some caveats:
 
-- D-FINE / DEIMv2 models are not supported
+- D-FINE models are not supported
 - YOLO-NAS models are known to not run well on integrated GPUs
 
 ## ONNX
@@ -685,9 +656,11 @@ 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**
@@ -718,7 +691,7 @@ detectors:
 | [RF-DETR](#rf-detr)           | ✅         | ❌      | Supports CUDA Graphs for optimal Nvidia performance |
 | [YOLO-NAS](#yolo-nas-1)       | ⚠️         | ⚠️      | Not supported by CUDA Graphs                        |
 | [YOLOX](#yolox-1)             | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
-| [D-FINE / DEIMv2](#d-fine--deimv2-1) | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
+| [D-FINE](#d-fine)             | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
 
 There is no default model provided, the following formats are supported:
 
@@ -847,9 +820,9 @@ model:
 
 </details>
 
-#### D-FINE / DEIMv2
+#### D-FINE
 
-[D-FINE](https://github.com/Peterande/D-FINE) and [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) are DETR based models that share the same ONNX input/output format. The ONNX exported models are supported, but not included by default. See the models section for downloading [D-FINE](#downloading-d-fine-model) or [DEIMv2](#downloading-deimv2-model) for use in Frigate.
+[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
 
 <details>
   <summary>D-FINE Setup & Config</summary>
@@ -873,28 +846,6 @@ model:
 
 </details>
 
-<details>
-  <summary>DEIMv2 Setup & Config</summary>
-
-After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
-
-```yaml
-detectors:
-  onnx:
-    type: onnx
-
-model:
-  model_type: dfine
-  width: 640
-  height: 640
-  input_tensor: nchw
-  input_dtype: float
-  path: /config/model_cache/deimv2_hgnetv2_n.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-</details>
-
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -994,7 +945,7 @@ MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the
 
 #### YOLO-NAS
 
-The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
+The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
 
 **Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
 
@@ -1028,7 +979,7 @@ model:
 
 #### YOLOv9
 
-The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
+The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
 
 ##### Configuration
 
@@ -1110,39 +1061,19 @@ model:
 
 #### Using a Custom Model
 
-To use your own custom model, first compile it into a [.dfp](https://developer.memryx.com/2p1/specs/files.html#dataflow-program) file, which is the format used by MemryX.
+To use your own model:
 
-#### Compile the Model
+1.  Package your compiled model into a `.zip` file.
 
-Custom models must be compiled using **MemryX SDK 2.1**.
+2.  The `.zip` must contain the compiled `.dfp` file.
 
-Before compiling your model, install the MemryX Neural Compiler tools from the
-[Install Tools](https://developer.memryx.com/2p1/get_started/install_tools.html) page on the **host**.
+3.  Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
 
-> **Note:** It is recommended to compile the model on the host machine, or on another separate machine, rather than inside the Frigate Docker container. Installing the compiler inside Docker may conflict with container packages. It is recommended to create a Python virtual environment and install the compiler there.
+4.  Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
 
-Once the SDK 2.1 environment is set up, follow the
-[MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) documentation to compile your model.
+5.  Update the `labelmap_path` to match your custom model's labels.
 
-Example:
-
-```bash
-mx_nc -m yolonas.onnx -c 4 --autocrop -v --dfp_fname yolonas.dfp
-```
-
-For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/2p1/tutorials/tutorials.html).
-
-#### Package the Compiled Model
-
-1. Package your compiled model into a `.zip` file.
-
-2. The `.zip` file must contain the compiled `.dfp` file.
-
-3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
-
-4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
-
-5. Update `labelmap_path` to match your custom model's labels.
+For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/tutorials/tutorials.html).
 
 ```yaml
 # The detector automatically selects the default model if nothing is provided in the config.
@@ -1579,60 +1510,17 @@ COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL
 EOF
 ```
 
-### Downloading DEIMv2 Model
-
-[DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
-
-- **HGNetv2** (smaller/faster): `atto`, `femto`, `pico`, `n`
-- **DINOv3** (larger/more accurate): `s`, `m`, `l`, `x`
-
-Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
-
-```sh
-docker build . --rm --build-arg BACKBONE=hgnetv2 --build-arg MODEL_SIZE=n --output . -f- <<'EOF'
-FROM python:3.11-slim AS build
-RUN apt-get update && apt-get install --no-install-recommends -y git libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /deimv2
-RUN git clone https://github.com/Intellindust-AI-Lab/DEIMv2.git .
-# Install CPU-only PyTorch first to avoid pulling CUDA variant
-RUN uv pip install --no-cache --system torch torchvision --index-url https://download.pytorch.org/whl/cpu
-RUN uv pip install --no-cache --system -r requirements.txt
-RUN uv pip install --no-cache --system onnx safetensors huggingface_hub
-RUN mkdir -p output
-ARG BACKBONE
-ARG MODEL_SIZE
-# Download from Hugging Face and convert safetensors to pth
-RUN python3 -c "\
-from huggingface_hub import hf_hub_download; \
-from safetensors.torch import load_file; \
-import torch; \
-backbone = '${BACKBONE}'.replace('hgnetv2','HGNetv2').replace('dinov3','DINOv3'); \
-size = '${MODEL_SIZE}'.upper(); \
-st = load_file(hf_hub_download('Intellindust/DEIMv2_' + backbone + '_' + size + '_COCO', 'model.safetensors')); \
-torch.save({'model': st}, 'output/deimv2.pth')"
-RUN sed -i "s/data = torch.rand(2/data = torch.rand(1/" tools/deployment/export_onnx.py
-# HuggingFace safetensors omits frozen constants that the model constructor initializes
-RUN sed -i "s/cfg.model.load_state_dict(state)/cfg.model.load_state_dict(state, strict=False)/" tools/deployment/export_onnx.py
-RUN python3 tools/deployment/export_onnx.py -c configs/deimv2/deimv2_${BACKBONE}_${MODEL_SIZE}_coco.yml -r output/deimv2.pth
-FROM scratch
-ARG BACKBONE
-ARG MODEL_SIZE
-COPY --from=build /deimv2/output/deimv2.onnx /deimv2_${BACKBONE}_${MODEL_SIZE}.onnx
-EOF
-```
-
-### Downloading RF-DETR Model
+### 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
@@ -1670,23 +1558,19 @@ 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

docs/docs/configuration/pwa.md

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

docs/docs/configuration/record.md

@@ -68,7 +68,7 @@ record:
 
 ## Will Frigate delete old recordings if my storage runs out?
 
-If there is less than an hour left of storage, the oldest hour of recordings will be deleted and a message will be printed in the Frigate logs. This emergency cleanup deletes the oldest recordings first regardless of retention settings to reclaim space as quickly as possible.
+As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 hours of recordings will be deleted.
 
 ## Configuring Recording Retention
 
@@ -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:

docs/docs/configuration/reference.md

@@ -16,8 +16,6 @@ mqtt:
   # Optional: Enable mqtt server (default: shown below)
   enabled: True
   # Required: host name
-  # NOTE: MQTT host can be specified with an environment variable or docker secrets that must begin with 'FRIGATE_'.
-  #       e.g. host: '{FRIGATE_MQTT_HOST}'
   host: mqtt.server.com
   # Optional: port (default: shown below)
   port: 1883
@@ -698,9 +696,6 @@ 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
@@ -908,8 +903,6 @@ 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 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

docs/docs/configuration/restream.md

@@ -34,7 +34,7 @@ To improve connection speed when using Birdseye via restream you can enable a sm
 
 The go2rtc restream can be secured with RTSP based username / password authentication. Ex:
 
-```yaml {2-4}
+```yaml
 go2rtc:
   rtsp:
     username: "admin"
@@ -147,7 +147,6 @@ For example:
 ```yaml
 go2rtc:
   streams:
-    # highlight-error-line
     my_camera: rtsp://username:$@foo%@192.168.1.100
 ```
 
@@ -156,7 +155,6 @@ becomes
 ```yaml
 go2rtc:
   streams:
-    # highlight-next-line
     my_camera: rtsp://username:$%40foo%25@192.168.1.100
 ```
 
@@ -187,40 +185,14 @@ 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/v1.9.10#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#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.
-
-:::
-
-NOTE: RTSP output will need to be passed with two curly braces `{{output}}`, whereas pipe output must be passed without curly braces.
+NOTE: The output will need to be passed with two curly braces `{{output}}`
 
 ```yaml
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
-    stream2: exec:rpicam-vid -t 0 --libav-format h264 -o -
 ```

docs/docs/configuration/review.md

@@ -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:

docs/docs/configuration/semantic_search.md

@@ -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.
 

docs/docs/configuration/snapshots.md

@@ -9,25 +9,4 @@ Snapshots are accessible in the UI in the Explore pane. This allows for quick su
 
 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 and uses it for both the snapshot and clean copy. As the object is tracked across frames, Frigate continuously evaluates whether the current frame is better than the previous best based on detection confidence, object size, and the presence of key attributes like faces or license plates. Frames where the object touches the edge of the frame are deprioritized. The snapshot is written to disk once tracking ends using whichever frame was determined to be the best.
-
-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`.
-
-## Clean Copy
-
-Frigate can produce up to two snapshot files per event, each used in different places:
-
-| Version | File | Annotations | Used by |
-| --- | --- | --- | --- |
-| **Regular snapshot** | `<camera>-<id>.jpg` | Respects your `timestamp`, `bounding_box`, `crop`, and `height` settings | API (`/api/events/<id>/snapshot.jpg`), MQTT (`<camera>/<label>/snapshot`), Explore pane in the UI |
-| **Clean copy** | `<camera>-<id>-clean.webp` | Always unannotated — no bounding box, no timestamp, no crop, full resolution | API (`/api/events/<id>/snapshot-clean.webp`), [Frigate+](/plus/first_model) submissions, "Download Clean Snapshot" in the UI |
-
-MQTT snapshots are configured separately under `cameras -> your_camera -> mqtt` and are unrelated to the clean copy.
-
-The clean copy is required for submitting events to [Frigate+](/plus/first_model) — if you plan to use Frigate+, keep `clean_copy` enabled regardless of your other snapshot settings.
-
-If you are not using Frigate+ and `timestamp`, `bounding_box`, and `crop` are all disabled, the regular snapshot is already effectively clean, so `clean_copy` provides no benefit and only uses additional disk space. You can safely set `clean_copy: False` in this case.
+Snapshots sent via MQTT are configured in the [config file](https://docs.frigate.video/configuration/) under `cameras -> your_camera -> mqtt`

docs/docs/configuration/tls.md

@@ -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:

docs/docs/configuration/zones.md

@@ -18,7 +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:
@@ -104,7 +104,6 @@ cameras:
   name_of_your_camera:
     zones:
       sidewalk:
-        # highlight-next-line
         loitering_time: 4 # unit is in seconds
         objects:
           - person
@@ -119,7 +118,6 @@ cameras:
   name_of_your_camera:
     zones:
       front_yard:
-        # highlight-next-line
         inertia: 3
         objects:
           - person
@@ -132,7 +130,6 @@ cameras:
   name_of_your_camera:
     zones:
       driveway_entrance:
-        # highlight-next-line
         inertia: 1
         objects:
           - car
@@ -195,6 +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)
 ```

docs/docs/development/contributing.md

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

docs/docs/frigate/camera_setup.md

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

docs/docs/frigate/hardware.md

@@ -20,13 +20,13 @@ 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 +38,9 @@ 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
 
@@ -55,10 +53,12 @@ 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.
@@ -86,7 +86,8 @@ 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 to provide efficient object detection.
+
   - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
   - Runs well with any size models including large
 
@@ -124,16 +125,10 @@ 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.
 
@@ -149,7 +144,9 @@ 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.
 
 :::
 
@@ -167,12 +164,12 @@ Inference speeds vary greatly depending on the CPU or GPU used, some known examp
 | 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.
 
@@ -182,15 +179,17 @@ 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

docs/docs/frigate/installation.md

@@ -3,11 +3,11 @@ id: installation
 title: Installation
 ---
 
-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.md#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.
 
 :::
 
@@ -56,7 +56,7 @@ services:
     volumes:
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # 1GB In-memory filesystem for recording segment storage
+      - type: tmpfs # Recommended: 1GB of memory
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -92,11 +92,7 @@ $ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576
 253MB
 ```
 
-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.
-
-## Extra Steps for Specific Hardware
-
-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.
+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
 
@@ -110,162 +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
 
@@ -297,7 +145,7 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
 
 #### Installation
 
-To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/install_hardware.html).
+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).
 
 Then follow these steps for installing the correct driver/runtime configuration:
 
@@ -306,12 +154,6 @@ Then follow these steps for installing the correct driver/runtime configuration:
 3. Run the script with `./user_installation.sh`
 4. **Restart your computer** to complete driver installation.
 
-:::warning
-
-For manual setup, use **MemryX SDK 2.1** only. Other SDK versions are not supported for this setup. See the [SDK 2.1 documentation](https://developer.memryx.com/2p1/index.html)
-
-:::
-
 #### Setup
 
 To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
@@ -460,7 +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
@@ -468,7 +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 # Recommended: 1GB of memory
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -508,15 +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
 
@@ -526,8 +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.
 
 :::
 
@@ -537,27 +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
 
@@ -695,43 +536,3 @@ docker run \
 ```
 
 Log into QNAP, open Container Station. Frigate docker container should be listed under 'Overview' and running. Visit Frigate Web UI by clicking Frigate docker, and then clicking the URL shown at the top of the detail page.
-
-## macOS - Apple Silicon
-
-:::warning
-
-macOS uses port 5000 for its Airplay Receiver service. If you want to expose port 5000 in Frigate for local app and API access the port will need to be mapped to another port on the host e.g. 5001
-
-Failure to remap port 5000 on the host will result in the WebUI and all API endpoints on port 5000 being unreachable, even if port 5000 is exposed correctly in Docker.
-
-:::
-
-Docker containers on macOS can be orchestrated by either [Docker Desktop](https://docs.docker.com/desktop/setup/install/mac-install/) or [OrbStack](https://orbstack.dev) (native swift app). The difference in inference speeds is negligable, however CPU, power consumption and container start times will be lower on OrbStack because it is a native Swift application.
-
-To allow Frigate to use the Apple Silicon Neural Engine / Processing Unit (NPU) the host must be running [Apple Silicon Detector](../configuration/object_detectors.md#apple-silicon-detector) on the host (outside Docker)
-
-#### Docker Compose example
-
-```yaml
-services:
-  frigate:
-    container_name: frigate
-    image: ghcr.io/blakeblackshear/frigate:stable-standard-arm64
-    restart: unless-stopped
-    shm_size: "512mb" # update for your cameras based on calculation above
-    volumes:
-      - /etc/localtime:/etc/localtime:ro
-      - /path/to/your/config:/config
-      - /path/to/your/recordings:/recordings
-    ports:
-      - "8971:8971"
-      # If exposing on macOS map to a diffent host port like 5001 or any orher port with no conflicts
-      # - "5001:5000" # Internal unauthenticated access. Expose carefully.
-      - "8554:8554" # RTSP feeds
-    extra_hosts:
-      # This is very important
-      # It allows frigate access to the NPU on Apple Silicon via Apple Silicon Detector
-      - "host.docker.internal:host-gateway" # Required to talk to the NPU detector
-    environment:
-      - FRIGATE_RTSP_PASSWORD: "password"
-```

docs/docs/frigate/planning_setup.md

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

docs/docs/frigate/updating.md

@@ -5,9 +5,9 @@ title: Updating
 
 # Updating Frigate
 
-The current stable version of Frigate is **0.17.2**. 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/v0.17.2).
+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., `0.17.2` 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:0.17.2
+           image: ghcr.io/blakeblackshear/frigate:0.16.2
        ```
      - Then pull the image:
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.17.2
+       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., `0.17.2`, `0.17.2-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:0.17.2
+       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

docs/docs/frigate/video_pipeline.md

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

docs/docs/guides/configuring_go2rtc.md

@@ -33,16 +33,19 @@ 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/v1.9.10#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.
@@ -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.
 

docs/docs/guides/getting_started.md

@@ -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
 
@@ -291,7 +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: ...

docs/docs/guides/ha_network_storage.md

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

docs/docs/integrations/home-assistant.md

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

docs/docs/integrations/mqtt.md

@@ -120,7 +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
 
@@ -134,14 +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
 }
@@ -149,13 +147,11 @@ 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",
@@ -284,7 +280,7 @@ 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:
 

docs/docs/integrations/plus.md

@@ -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>
 ```

docs/docs/integrations/third_party_extensions.md

@@ -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.

docs/docs/plus/first_model.md

@@ -24,8 +24,6 @@ You will receive an email notification when your Frigate+ model is ready.
 Models available in Frigate+ can be used with a special model path. No other information needs to be configured because it fetches the remaining config from Frigate+ automatically.
 
 ```yaml
-detectors: ...
-
 model:
   path: plus://<your_model_id>
 ```

docs/docs/plus/index.md

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

docs/docs/troubleshooting/cpu.md

@@ -1,73 +0,0 @@
----
-id: cpu
-title: High CPU Usage
----
-
-High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
-
-## 1. Hardware Acceleration for Video Decoding
-
-**Priority: Critical**
-
-Video decoding is one of the most CPU-intensive tasks in Frigate. While an AI accelerator handles object detection, it does not assist with decoding video streams. Hardware acceleration (hwaccel) offloads this work to your GPU or specialized video decode hardware, significantly reducing CPU usage and enabling you to support more cameras on the same hardware.
-
-### Key Concepts
-
-**Resolution & FPS Impact:** The decoding burden grows exponentially with resolution and frame rate. A 4K stream at 30 FPS requires roughly 4 times the processing power of a 1080p stream at the same frame rate, and doubling the frame rate doubles the decode workload. This is why hardware acceleration becomes critical when working with multiple high-resolution cameras.
-
-**Hardware Acceleration Benefits:** By using dedicated video decode hardware, you can:
-
-- Significantly reduce CPU usage per camera stream
-- Support 2-3x more cameras on the same hardware
-- Free up CPU resources for motion detection and other Frigate processes
-- Reduce system heat and power consumption
-
-### Configuration
-
-Frigate provides preset configurations for common hardware acceleration scenarios. Set up `hwaccel_args` based on your hardware in your [configuration](../configuration/reference) as described in the [getting started guide](../guides/getting_started).
-
-### Troubleshooting Hardware Acceleration
-
-If hardware acceleration isn't working:
-
-1. Check Frigate logs for FFmpeg errors related to hwaccel
-2. Verify the hardware device is accessible inside the container
-3. Ensure your camera streams use H.264 or H.265 codecs (most common)
-4. Try different presets if the automatic detection fails
-5. Check that your GPU drivers are properly installed on the host system
-
-## 2. Detector Selection and Configuration
-
-**Priority: Critical**
-
-Choosing the right detector for your hardware is the single most important factor for detection performance. The detector is responsible for running the AI model that identifies objects in video frames. Different detector types have vastly different performance characteristics and hardware requirements, as detailed in the [hardware documentation](../frigate/hardware).
-
-### Understanding Detector Performance
-
-Frigate uses motion detection as a first-line check before running expensive object detection, as explained in the [motion detection documentation](../configuration/motion_detection). When motion is detected, Frigate creates a "region" (the green boxes in the debug viewer) and sends it to the detector. The detector's inference speed determines how many detections per second your system can handle.
-
-**Calculating Detector Capacity:** Your detector has a finite capacity measured in detections per second. With an inference speed of 10ms, your detector can handle approximately 100 detections per second (1000ms / 10ms = 100).If your cameras collectively require more than this capacity, you'll experience delays, missed detections, or the system will fall behind.
-
-### Choosing the Right Detector
-
-Different detectors have vastly different performance characteristics, see the expected performance for object detectors in [the hardware docs](../frigate/hardware)
-
-### Multiple Detector Instances
-
-When a single detector cannot keep up with your camera count, some detector types (`openvino`, `onnx`) allow you to define multiple detector instances to share the workload. This is particularly useful with GPU-based detectors that have sufficient VRAM to run multiple inference processes.
-
-For detailed instructions on configuring multiple detectors, see the [Object Detectors documentation](../configuration/object_detectors).
-
-
-**When to add a second detector:**
-
-- Skipped FPS is consistently > 0 even during normal activity
-
-### Model Selection and Optimization
-
-The model you use significantly impacts detector performance. Frigate provides default models optimized for each detector type, but you can customize them as described in the [detector documentation](../configuration/object_detectors).
-
-**Model Size Trade-offs:**
-
-- Smaller models (320x320): Faster inference, Frigate is specifically optimized for a 320x320 size model.
-- Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.

docs/docs/troubleshooting/dummy-camera.md

@@ -1,60 +0,0 @@
----
-id: dummy-camera
-title: Analyzing Object Detection
----
-
-When investigating object detection or tracking problems, it can be helpful to replay an exported video as a temporary "dummy" camera. This lets you reproduce issues locally, iterate on configuration (detections, zones, enrichment settings), and capture logs and clips for analysis.
-
-## When to use
-
-- Replaying an exported clip to reproduce incorrect detections
-- Testing configuration changes (model settings, trackers, filters) against a known clip
-- Gathering deterministic logs and recordings for debugging or issue reports
-
-## 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` like this:
-
-```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 realtime and loop indefinitely, which is useful for long debugging sessions.
-- `-fflags +genpts` helps generate 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.
-
-## Variables to consider in object tracking
-
-- The exported video will not always line up exactly with how it originally ran through Frigate (or even with the last loop). Different frames may be used on replay, which can change detections and tracking.
-- Motion detection depends on the frames used; small frame shifts can change motion regions and therefore what gets passed to the detector.
-- Object detection is not deterministic: models and post-processing can yield different results across runs, so you may not get identical detections or track IDs every time.
-
-When debugging, treat the replay as a close approximation rather than a byte-for-byte replay. Capture multiple runs, enable recording if helpful, and examine logs and saved event clips to understand variability.
-
-## Troubleshooting
-
-- No video: verify the path is correct and accessible from the Frigate process/container.
-- FFmpeg errors: check the log output for ffmpeg-specific flags and adjust `input_args` accordingly for your file/container. You may also need to disable hardware acceleration (`hwaccel_args: ""`) for the dummy camera.
-- No detections: confirm the camera `roles` include `detect`, and model/detector configuration is enabled.

docs/docs/troubleshooting/edgetpu.md

@@ -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
 

docs/docs/troubleshooting/faqs.md

@@ -110,19 +110,3 @@ No. Frigate uses the TCP protocol to connect to your camera's RTSP URL. VLC auto
 TCP ensures that all data packets arrive in the correct order. This is crucial for video recording, decoding, and stream processing, which is why Frigate enforces a TCP connection. UDP is faster but less reliable, as it does not guarantee packet delivery or order, and VLC does not have the same requirements as Frigate.
 
 You can still configure Frigate to use UDP by using ffmpeg input args or the preset `preset-rtsp-udp`. See the [ffmpeg presets](/configuration/ffmpeg_presets) documentation.
-
-### Why does Frigate keep creating new events for my parked car?
-
-Stationary tracking is designed to _prevent_ this — a parked car should stay one tracked object and not generate new events. If you're getting repeated events for the same car, it's likely that Frigate is losing the tracked object and re-detecting it as a new one.
-
-Open one of the events in Explore → **Tracking Details**. If the detection scores are low (< 70% or so), the model isn't confident the parked car is a car. This is common with the free [COCO-trained](https://cocodataset.org/#explore) object detection models on steep/top-down angles, partially occluded cars, foliage, or low-light footage. When detections fall below `min_score` for too many frames the tracker loses the object, and the next confident frame creates a brand new one.
-
-What helps:
-
-- **Improve the view** — even a small angle change that gets more of the car visible could lift scores enough to stabilize tracking.
-- **Use a more accurate model** — switching from `mobiledet` to `yolov9`, or stepping up to a larger variant like `yolov9-s` over `yolov9-t`, can help (at the cost of inference time, and still on the COCO dataset). The biggest gains usually come from fine-tuning a model on images from your own cameras so it learns your specific scene. [Frigate+](https://frigate.video/plus) is a paid option that does this - models are trained on security-camera footage and can be fine-tuned on images you submit from your own setup.
-- **Don't set `detect -> stationary -> max_frames` for `car`** — it artificially ends tracking and forces re-detection as a new object. See [Stationary Objects](../configuration/stationary_objects.md).
-- **Restrict alerts to the areas you care about** with `required_zones` — see [Zones](../configuration/zones.md#restricting-alerts-and-detections-to-specific-zones). Make sure those zones use the default `loitering_time: 0` unless you specifically want the review item to stay open until the car leaves.
-- **Filter impossible locations** with [object filter masks](../configuration/masks.md#object-filter-masks) if cars are being detected on rooftops, treetops, etc.
-
-See [Object Filters](../configuration/object_filters.md) for more on tuning `min_score` and `threshold` — note that raising them too high will make this exact problem worse.

docs/docs/troubleshooting/gpu.md

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

docs/docs/troubleshooting/memory.md

@@ -1,6 +1,6 @@
 ---
 id: memory
-title: Memory Usage
+title: Memory Troubleshooting
 ---
 
 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.
@@ -9,20 +9,8 @@ Frigate includes built-in memory profiling using [memray](https://bloomberg.gith
 
 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>
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
 ```
 
 ### Module Names
@@ -40,7 +28,7 @@ Frigate processes are named using a module-based naming scheme. Common module na
 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
+export 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.
@@ -67,20 +55,11 @@ After a process exits normally, you'll find HTML reports in `/config/memray_repo
 
 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
+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
-```
+This will generate an HTML file that you can open in your browser.
 
 ## Understanding the Reports
 
@@ -131,4 +110,20 @@ The interactive HTML reports allow you to:
 - Check that memray is properly installed (included by default in Frigate)
 - Verify the process actually started and ran (check process logs)
 
+## Example Usage
+
+```bash
+# Enable profiling for review and capture modules
+export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
+
+# Start Frigate
+# ... let it run for a while ...
+
+# Check for reports
+ls -lh /config/memray_reports/
+
+# If a process crashed, manually generate report
+memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
+```
+
 For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).

docs/docs/troubleshooting/recordings.md

@@ -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?

docs/docusaurus.config.ts

@@ -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: [
@@ -181,7 +170,7 @@ const config: Config = {
           ],
         },
       ],
-      copyright: `Copyright © ${new Date().getFullYear()} Frigate, Inc.`,
+      copyright: `Copyright © ${new Date().getFullYear()} Frigate LLC`,
     },
   },
   plugins: [

docs/package-lock.json

@@ -18490,9 +18490,9 @@
       }
     },
     "node_modules/qs": {
-      "version": "6.14.1",
-      "resolved": "https://registry.npmjs.org/qs/-/qs-6.14.1.tgz",
-      "integrity": "sha512-4EK3+xJl8Ts67nLYNwqw/dsFVnCf+qR7RgXSK9jEEm9unao3njwMDdmsdvoKBKHzxd7tCYz5e5M+SnMjdtXGQQ==",
+      "version": "6.14.0",
+      "resolved": "https://registry.npmjs.org/qs/-/qs-6.14.0.tgz",
+      "integrity": "sha512-YWWTjgABSKcvs/nWBi9PycY/JiPJqOD4JA6o9Sej2AtvSGarXxKC3OQSk4pAarbdQlKAh5D4FCQkJNkW+GAn3w==",
       "license": "BSD-3-Clause",
       "dependencies": {
         "side-channel": "^1.1.0"

docs/sidebars.ts

@@ -129,27 +129,9 @@ 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",
+      "troubleshooting/memory",
     ],
     Development: [
       "development/contributing",

docs/src/css/custom.css

@@ -234,11 +234,3 @@
   content: "schema";
   color: var(--ifm-color-secondary-contrast-foreground);
 }
-
-.code-block-error-line {
-  background-color: #ff000020;
-  display: block;
-  margin: 0 calc(-1 * var(--ifm-pre-padding));
-  padding: 0 var(--ifm-pre-padding);
-  border-left: 3px solid #ff000080;
-}

docs/static/_headers

@@ -1,8 +0,0 @@
-https://:project.pages.dev/*
-  X-Robots-Tag: noindex
-
-https://:version.:project.pages.dev/*
-  X-Robots-Tag: noindex
-
-https://docs-dev.frigate.video/*
-  X-Robots-Tag: noindex

docs/static/frigate-api.yaml

@@ -14,38 +14,19 @@ paths:
     get:
       tags:
         - Auth
-      summary: Authenticate request
-      description: |-
-        Authenticates the current request based on proxy headers or JWT token.
-        This endpoint verifies authentication credentials and manages JWT token refresh.
-        On success, no JSON body is returned; authentication state is communicated via response headers and cookies.
+      summary: Auth
       operationId: auth_auth_get
       responses:
-        "202":
-          description: Authentication Accepted (no response body, different headers depending on auth method)
-          headers:
-            remote-user:
-              description: Authenticated username or "viewer" in proxy-only mode
-              schema:
-                type: string
-            remote-role:
-              description: Resolved role (e.g., admin, viewer, or custom)
-              schema:
-                type: string
-            Set-Cookie:
-              description: May include refreshed JWT cookie ("frigate-token") when applicable
-              schema:
-                type: string
-        "401":
-          description: Authentication Failed
+        "200":
+          description: Successful Response
+          content:
+            application/json:
+              schema: {}
   /profile:
     get:
       tags:
         - Auth
-      summary: Get user profile
-      description: |-
-        Returns the current authenticated user's profile including username, role, and allowed cameras.
-        This endpoint requires authentication and returns information about the user's permissions.
+      summary: Profile
       operationId: profile_profile_get
       responses:
         "200":
@@ -53,16 +34,11 @@ paths:
           content:
             application/json:
               schema: {}
-        "401":
-          description: Unauthorized
   /logout:
     get:
       tags:
         - Auth
-      summary: Logout user
-      description: |-
-        Logs out the current user by clearing the session cookie.
-        After logout, subsequent requests will require re-authentication.
+      summary: Logout
       operationId: logout_logout_get
       responses:
         "200":
@@ -70,22 +46,11 @@ paths:
           content:
             application/json:
               schema: {}
-        "303":
-          description: See Other (redirects to login page)
   /login:
     post:
       tags:
         - Auth
-      summary: Login with credentials
-      description: |-
-        Authenticates a user with username and password.
-        Returns a JWT token as a secure HTTP-only cookie that can be used for subsequent API requests.
-        The JWT token can also be retrieved from the response and used as a Bearer token in the Authorization header.
-
-        Example using Bearer token:
-        ```
-        curl -H "Authorization: Bearer <token_value>" https://frigate_ip:8971/api/profile
-        ```
+      summary: Login
       operationId: login_login_post
       requestBody:
         required: true
@@ -99,11 +64,6 @@ paths:
           content:
             application/json:
               schema: {}
-        "401":
-          description: Login Failed - Invalid credentials
-          content:
-            application/json:
-              schema: {}
         "422":
           description: Validation Error
           content:
@@ -114,10 +74,7 @@ paths:
     get:
       tags:
         - Auth
-      summary: Get all users
-      description: |-
-        Returns a list of all users with their usernames and roles.
-        Requires admin role. Each user object contains the username and assigned role.
+      summary: Get Users
       operationId: get_users_users_get
       responses:
         "200":
@@ -125,19 +82,10 @@ paths:
           content:
             application/json:
               schema: {}
-        "403":
-          description: Forbidden - Admin role required
     post:
       tags:
         - Auth
-      summary: Create new user
-      description: |-
-        Creates a new user with the specified username, password, and role.
-        Requires admin role. Password must meet strength requirements:
-        - Minimum 8 characters
-        - At least one uppercase letter
-        - At least one digit
-        - At least one special character (!@#$%^&*(),.?":{}\|<>)
+      summary: Create User
       operationId: create_user_users_post
       requestBody:
         required: true
@@ -151,13 +99,6 @@ paths:
           content:
             application/json:
               schema: {}
-        "400":
-          description: Bad Request - Invalid username or role
-          content:
-            application/json:
-              schema: {}
-        "403":
-          description: Forbidden - Admin role required
         "422":
           description: Validation Error
           content:
@@ -168,10 +109,7 @@ paths:
     delete:
       tags:
         - Auth
-      summary: Delete user
-      description: |-
-        Deletes a user by username. The built-in admin user cannot be deleted.
-        Requires admin role. Returns success message or error if user not found.
+      summary: Delete User
       operationId: delete_user_users__username__delete
       parameters:
         - name: username
@@ -180,15 +118,12 @@ paths:
           schema:
             type: string
             title: Username
-            description: The username of the user to delete
       responses:
         "200":
           description: Successful Response
           content:
             application/json:
               schema: {}
-        "403":
-          description: Forbidden - Cannot delete admin user or admin role required
         "422":
           description: Validation Error
           content:
@@ -199,17 +134,7 @@ paths:
     put:
       tags:
         - Auth
-      summary: Update user password
-      description: |-
-        Updates a user's password. Users can only change their own password unless they have admin role.
-        Requires the current password to verify identity for non-admin users.
-        Password must meet strength requirements:
-        - Minimum 8 characters
-        - At least one uppercase letter
-        - At least one digit
-        - At least one special character (!@#$%^&*(),.?":{}\|<>)
-
-        If user changes their own password, a new JWT cookie is automatically issued.
+      summary: Update Password
       operationId: update_password_users__username__password_put
       parameters:
         - name: username
@@ -218,7 +143,6 @@ paths:
           schema:
             type: string
             title: Username
-            description: The username of the user whose password to update
       requestBody:
         required: true
         content:
@@ -231,14 +155,6 @@ paths:
           content:
             application/json:
               schema: {}
-        "400":
-          description: Bad Request - Current password required or password doesn't meet requirements
-        "401":
-          description: Unauthorized - Current password is incorrect
-        "403":
-          description: Forbidden - Viewers can only update their own password
-        "404":
-          description: Not Found - User not found
         "422":
           description: Validation Error
           content:
@@ -249,10 +165,7 @@ paths:
     put:
       tags:
         - Auth
-      summary: Update user role
-      description: |-
-        Updates a user's role. The built-in admin user's role cannot be modified.
-        Requires admin role. Valid roles are defined in the configuration.
+      summary: Update Role
       operationId: update_role_users__username__role_put
       parameters:
         - name: username
@@ -261,7 +174,6 @@ paths:
           schema:
             type: string
             title: Username
-            description: The username of the user whose role to update
       requestBody:
         required: true
         content:
@@ -274,10 +186,6 @@ paths:
           content:
             application/json:
               schema: {}
-        "400":
-          description: Bad Request - Invalid role
-        "403":
-          description: Forbidden - Cannot modify admin user's role or admin role required
         "422":
           description: Validation Error
           content:
@@ -616,32 +524,6 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
-  /classification/attributes:
-    get:
-      tags:
-        - Classification
-      summary: Get custom classification attributes
-      description: |-
-        Returns custom classification attributes for a given object type.
-        Only includes models with classification_type set to 'attribute'.
-        By default returns a flat sorted list of all attribute labels.
-        If group_by_model is true, returns attributes grouped by model name.
-      operationId: get_custom_attributes_classification_attributes_get
-      parameters:
-        - name: object_type
-          in: query
-          schema:
-            type: string
-        - name: group_by_model
-          in: query
-          schema:
-            type: boolean
-            default: false
-      responses:
-        "200":
-          description: Successful Response
-        "422":
-          description: Validation Error
   /classification/{name}/dataset:
     get:
       tags:
@@ -2938,42 +2820,6 @@ paths:
             application/json:
               schema:
                 $ref: "#/components/schemas/HTTPValidationError"
-  /events/{event_id}/attributes:
-    post:
-      tags:
-        - Events
-      summary: Set custom classification attributes
-      description: |-
-        Sets an event's custom classification attributes for all attribute-type
-        models that apply to the event's object type.
-            Returns a success message or an error if the event is not found.
-      operationId: set_attributes_events__event_id__attributes_post
-      parameters:
-        - name: event_id
-          in: path
-          required: true
-          schema:
-            type: string
-            title: Event Id
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              $ref: "#/components/schemas/EventsAttributesBody"
-      responses:
-        "200":
-          description: Successful Response
-          content:
-            application/json:
-              schema:
-                $ref: "#/components/schemas/GenericResponse"
-        "422":
-          description: Validation Error
-          content:
-            application/json:
-              schema:
-                $ref: "#/components/schemas/HTTPValidationError"
   /events/{event_id}/description:
     post:
       tags:
@@ -5021,18 +4867,6 @@ components:
       required:
         - subLabel
       title: EventsSubLabelBody
-    EventsAttributesBody:
-      properties:
-        attributes:
-          type: object
-          title: Attributes
-          description: Object with model names as keys and attribute values
-          additionalProperties:
-            type: string
-      type: object
-      required:
-        - attributes
-      title: EventsAttributesBody
     ExportModel:
       properties:
         id:

docs/static/img/branding/LICENSE.md

@@ -1,12 +1,12 @@
 # COPYRIGHT AND TRADEMARK NOTICE
 
 The images, logos, and icons contained in this directory (the "Brand Assets") are
-proprietary to Frigate, Inc. and are NOT covered by the MIT License governing the
+proprietary to Frigate LLC and are NOT covered by the MIT License governing the
 rest of this repository.
 
 1. TRADEMARK STATUS
    The "Frigate" name and the accompanying logo are common law trademarks™ of
-   Frigate, Inc. Frigate, Inc. reserves all rights to these marks.
+   Frigate LLC. Frigate LLC reserves all rights to these marks.
 
 2. LIMITED PERMISSION FOR USE
    Permission is hereby granted to display these Brand Assets strictly for the
@@ -17,9 +17,9 @@ rest of this repository.
 3. RESTRICTIONS
    You may NOT:
    a. Use these Brand Assets to represent a derivative work (fork) as an official
-   product of Frigate, Inc.
+   product of Frigate LLC.
    b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
-   commercial affiliation with Frigate, Inc.
+   commercial affiliation with Frigate LLC.
    c. Modify or alter the Brand Assets.
 
 If you fork this repository with the intent to distribute a modified or competing
@@ -27,4 +27,4 @@ version of the software, you must replace these Brand Assets with your own
 original content.
 
 ALL RIGHTS RESERVED.
-Copyright (c) 2026 Frigate, Inc.
+Copyright (c) 2025 Frigate LLC.

frigate/api/app.py

@@ -23,12 +23,7 @@ from markupsafe import escape
 from peewee import SQL, fn, operator
 from pydantic import ValidationError
 
-from frigate.api.auth import (
-    allow_any_authenticated,
-    allow_public,
-    get_allowed_cameras_for_filter,
-    require_role,
-)
+from frigate.api.auth import allow_any_authenticated, allow_public, require_role
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import AppConfigSetBody
 from frigate.api.defs.tags import Tags
@@ -218,7 +213,7 @@ def config_raw_paths(request: Request):
     return JSONResponse(content=raw_paths)
 
 
-@router.get("/config/raw", dependencies=[Depends(require_role(["admin"]))])
+@router.get("/config/raw", dependencies=[Depends(allow_any_authenticated())])
 def config_raw():
     config_file = find_config_file()
 
@@ -692,19 +687,13 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
 @router.get(
     "/recognized_license_plates", dependencies=[Depends(allow_any_authenticated())]
 )
-def get_recognized_license_plates(
-    split_joined: Optional[int] = None,
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
-):
+def get_recognized_license_plates(split_joined: Optional[int] = None):
     try:
         query = (
             Event.select(
                 SQL("json_extract(data, '$.recognized_license_plate') AS plate")
             )
-            .where(
-                (SQL("json_extract(data, '$.recognized_license_plate') IS NOT NULL"))
-                & (Event.camera << allowed_cameras)
-            )
+            .where(SQL("json_extract(data, '$.recognized_license_plate') IS NOT NULL"))
             .distinct()
         )
         recognized_license_plates = [row[0] for row in query.tuples()]
@@ -732,12 +721,7 @@ def get_recognized_license_plates(
 
 
 @router.get("/timeline", dependencies=[Depends(allow_any_authenticated())])
-def timeline(
-    camera: str = "all",
-    limit: int = 100,
-    source_id: Optional[str] = None,
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
-):
+def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = None):
     clauses = []
 
     selected_columns = [
@@ -759,9 +743,6 @@ def timeline(
         else:
             clauses.append((Timeline.source_id.in_(source_ids)))
 
-    # Enforce per-camera access control
-    clauses.append((Timeline.camera << allowed_cameras))
-
     if len(clauses) == 0:
         clauses.append((True))
 
@@ -777,10 +758,7 @@ def timeline(
 
 
 @router.get("/timeline/hourly", dependencies=[Depends(allow_any_authenticated())])
-def hourly_timeline(
-    params: AppTimelineHourlyQueryParameters = Depends(),
-    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
-):
+def hourly_timeline(params: AppTimelineHourlyQueryParameters = Depends()):
     """Get hourly summary for timeline."""
     cameras = params.cameras
     labels = params.labels
@@ -798,9 +776,6 @@ def hourly_timeline(
         camera_list = cameras.split(",")
         clauses.append((Timeline.camera << camera_list))
 
-    # Enforce per-camera access control
-    clauses.append((Timeline.camera << allowed_cameras))
-
     if labels != "all":
         label_list = labels.split(",")
         clauses.append((Timeline.data["label"] << label_list))

frigate/api/auth.py

@@ -67,6 +67,7 @@ def require_admin_by_default():
         "/stats",
         "/stats/history",
         "/config",
+        "/config/raw",
         "/vainfo",
         "/nvinfo",
         "/labels",
@@ -142,6 +143,17 @@ def require_admin_by_default():
     return admin_checker
 
 
+def _is_authenticated(request: Request) -> bool:
+    """
+    Helper to determine if a request is from an authenticated user.
+
+    Returns True if the request has a valid authenticated user (not anonymous).
+    Port 5000 internal requests are considered anonymous despite having admin role.
+    """
+    username = request.headers.get("remote-user")
+    return username is not None and username != "anonymous"
+
+
 def allow_public():
     """
     Override dependency to allow unauthenticated access to an endpoint.
@@ -161,24 +173,27 @@ def allow_public():
 
 def allow_any_authenticated():
     """
-    Override dependency to allow any request that passed through the /auth endpoint.
+    Override dependency to allow any authenticated user (bypass admin requirement).
 
     Allows:
-    - Port 5000 internal requests (remote-user: "anonymous", remote-role: "admin")
-    - Authenticated users with JWT tokens (remote-user: username)
-    - Unauthenticated requests when auth is disabled (remote-user: "viewer")
+    - Port 5000 internal requests (have admin role despite anonymous user)
+    - Any authenticated user with a real username (not "anonymous")
 
     Rejects:
-    - Requests with no remote-user header (did not pass through /auth endpoint)
+    - Port 8971 requests with anonymous user (auth disabled, no proxy auth)
 
     Example:
         @router.get("/authenticated-endpoint", dependencies=[Depends(allow_any_authenticated())])
     """
 
     async def auth_checker(request: Request):
-        # Ensure a remote-user has been set by the /auth endpoint
-        username = request.headers.get("remote-user")
-        if username is None:
+        # Port 5000 requests have admin role and should be allowed
+        role = request.headers.get("remote-role")
+        if role == "admin":
+            return
+
+        # Otherwise require a real authenticated user (not anonymous)
+        if not _is_authenticated(request):
             raise HTTPException(status_code=401, detail="Authentication required")
         return
 
@@ -349,15 +364,21 @@ def validate_password_strength(password: str) -> tuple[bool, Optional[str]]:
     Validate password strength.
 
     Returns a tuple of (is_valid, error_message).
-
-    Longer passwords are harder to crack than shorter complex ones.
-    https://pages.nist.gov/800-63-3/sp800-63b.html
     """
     if not password:
         return False, "Password cannot be empty"
 
-    if len(password) < 12:
-        return False, "Password must be at least 12 characters long"
+    if len(password) < 8:
+        return False, "Password must be at least 8 characters long"
+
+    if not any(c.isupper() for c in password):
+        return False, "Password must contain at least one uppercase letter"
+
+    if not any(c.isdigit() for c in password):
+        return False, "Password must contain at least one digit"
+
+    if not any(c in '!@#$%^&*(),.?":{}|<>' for c in password):
+        return False, "Password must contain at least one special character"
 
     return True, None
 
@@ -438,11 +459,10 @@ def resolve_role(
     Determine the effective role for a request based on proxy headers and configuration.
 
     Order of resolution:
-            1. If a role header is defined in proxy_config.header_map.role:
-                 - If a role_map is configured, treat the header as group claims
-                     (split by proxy_config.separator) and map to roles.
-                     Admin matches short-circuit to admin.
-                 - If no role_map is configured, treat the header as role names directly.
+      1. If a role header is defined in proxy_config.header_map.role:
+         - If a role_map is configured, treat the header as group claims
+           (split by proxy_config.separator) and map to roles.
+         - If no role_map is configured, treat the header as role names directly.
       2. If no valid role is found, return proxy_config.default_role if it's valid in config_roles, else 'viewer'.
 
     Args:
@@ -492,12 +512,6 @@ def resolve_role(
         }
         logger.debug("Matched roles from role_map: %s", matched_roles)
 
-        # If admin matches, prioritize it to avoid accidental downgrade when
-        # users belong to both admin and lower-privilege groups.
-        if "admin" in matched_roles and "admin" in config_roles:
-            logger.debug("Resolved role (with role_map) to 'admin'.")
-            return "admin"
-
         if matched_roles:
             resolved = next(
                 (r for r in config_roles if r in matched_roles), validated_default
@@ -535,37 +549,7 @@ def resolve_role(
 
 
 # Endpoints
-@router.get(
-    "/auth",
-    dependencies=[Depends(allow_public())],
-    summary="Authenticate request",
-    description=(
-        "Authenticates the current request based on proxy headers or JWT token. "
-        "This endpoint verifies authentication credentials and manages JWT token refresh. "
-        "On success, no JSON body is returned; authentication state is communicated via response headers and cookies."
-    ),
-    status_code=202,
-    responses={
-        202: {
-            "description": "Authentication Accepted (no response body)",
-            "headers": {
-                "remote-user": {
-                    "description": 'Authenticated username or "viewer" in proxy-only mode',
-                    "schema": {"type": "string"},
-                },
-                "remote-role": {
-                    "description": "Resolved role (e.g., admin, viewer, or custom)",
-                    "schema": {"type": "string"},
-                },
-                "Set-Cookie": {
-                    "description": "May include refreshed JWT cookie when applicable",
-                    "schema": {"type": "string"},
-                },
-            },
-        },
-        401: {"description": "Authentication Failed"},
-    },
-)
+@router.get("/auth", dependencies=[Depends(allow_public())])
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
@@ -592,12 +576,12 @@ def auth(request: Request):
     # if auth is disabled, just apply the proxy header map and return success
     if not auth_config.enabled:
         # pass the user header value from the upstream proxy if a mapping is specified
-        # or use viewer if none are specified
+        # or use anonymous if none are specified
         user_header = proxy_config.header_map.user
         success_response.headers["remote-user"] = (
-            request.headers.get(user_header, default="viewer")
+            request.headers.get(user_header, default="anonymous")
             if user_header
-            else "viewer"
+            else "anonymous"
         )
 
         # parse header and resolve a valid role
@@ -705,14 +689,9 @@ def auth(request: Request):
         return fail_response
 
 
-@router.get(
-    "/profile",
-    dependencies=[Depends(allow_any_authenticated())],
-    summary="Get user profile",
-    description="Returns the current authenticated user's profile including username, role, and allowed cameras. This endpoint requires authentication and returns information about the user's permissions.",
-)
+@router.get("/profile", dependencies=[Depends(allow_any_authenticated())])
 def profile(request: Request):
-    username = request.headers.get("remote-user", "viewer")
+    username = request.headers.get("remote-user", "anonymous")
     role = request.headers.get("remote-role", "viewer")
 
     all_camera_names = set(request.app.frigate_config.cameras.keys())
@@ -724,12 +703,7 @@ def profile(request: Request):
     )
 
 
-@router.get(
-    "/logout",
-    dependencies=[Depends(allow_public())],
-    summary="Logout user",
-    description="Logs out the current user by clearing the session cookie. After logout, subsequent requests will require re-authentication.",
-)
+@router.get("/logout", dependencies=[Depends(allow_public())])
 def logout(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     response = RedirectResponse("/login", status_code=303)
@@ -740,12 +714,7 @@ def logout(request: Request):
 limiter = Limiter(key_func=get_remote_addr)
 
 
-@router.post(
-    "/login",
-    dependencies=[Depends(allow_public())],
-    summary="Login with credentials",
-    description='Authenticates a user with username and password. Returns a JWT token as a secure HTTP-only cookie that can be used for subsequent API requests. The JWT token can also be retrieved from the response and used as a Bearer token in the Authorization header.\n\nExample using Bearer token:\n```\ncurl -H "Authorization: Bearer <token_value>" https://frigate_ip:8971/api/profile\n```',
-)
+@router.post("/login", dependencies=[Depends(allow_public())])
 @limiter.limit(limit_value=rateLimiter.get_limit)
 def login(request: Request, body: AppPostLoginBody):
     JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
@@ -783,12 +752,7 @@ def login(request: Request, body: AppPostLoginBody):
     return JSONResponse(content={"message": "Login failed"}, status_code=401)
 
 
-@router.get(
-    "/users",
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Get all users",
-    description="Returns a list of all users with their usernames and roles. Requires admin role. Each user object contains the username and assigned role.",
-)
+@router.get("/users", dependencies=[Depends(require_role(["admin"]))])
 def get_users():
     exports = (
         User.select(User.username, User.role).order_by(User.username).dicts().iterator()
@@ -796,12 +760,7 @@ def get_users():
     return JSONResponse([e for e in exports])
 
 
-@router.post(
-    "/users",
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Create new user",
-    description="Creates a new user with the specified username, password, and role. Requires admin role. Password must be at least 12 characters long.",
-)
+@router.post("/users", dependencies=[Depends(require_role(["admin"]))])
 def create_user(
     request: Request,
     body: AppPostUsersBody,
@@ -817,15 +776,6 @@ def create_user(
             content={"message": f"Role must be one of: {', '.join(config_roles)}"},
             status_code=400,
         )
-
-    # Validate password strength
-    is_valid, error_message = validate_password_strength(body.password)
-    if not is_valid:
-        return JSONResponse(
-            content={"message": error_message},
-            status_code=400,
-        )
-
     role = body.role or "viewer"
     password_hash = hash_password(body.password, iterations=HASH_ITERATIONS)
     User.insert(
@@ -836,16 +786,10 @@ def create_user(
             User.notification_tokens: [],
         }
     ).execute()
-    request.app.config_publisher.publisher.publish("config/auth", None)
     return JSONResponse(content={"username": body.username})
 
 
-@router.delete(
-    "/users/{username}",
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete user",
-    description="Deletes a user by username. The built-in admin user cannot be deleted. Requires admin role. Returns success message or error if user not found.",
-)
+@router.delete("/users/{username}", dependencies=[Depends(require_role(["admin"]))])
 def delete_user(request: Request, username: str):
     # Prevent deletion of the built-in admin user
     if username == "admin":
@@ -854,15 +798,11 @@ def delete_user(request: Request, username: str):
         )
 
     User.delete_by_id(username)
-    request.app.config_publisher.publisher.publish("config/auth", None)
     return JSONResponse(content={"success": True})
 
 
 @router.put(
-    "/users/{username}/password",
-    dependencies=[Depends(allow_any_authenticated())],
-    summary="Update user password",
-    description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must be at least 12 characters long. If user changes their own password, a new JWT cookie is automatically issued.",
+    "/users/{username}/password", dependencies=[Depends(allow_any_authenticated())]
 )
 async def update_password(
     request: Request,
@@ -890,9 +830,13 @@ async def update_password(
     except DoesNotExist:
         return JSONResponse(content={"message": "User not found"}, status_code=404)
 
-    # Require old_password when non-admin user is changing any password
-    # Admin users changing passwords do NOT need to provide the current password
-    if current_role != "admin":
+    # Require old_password when:
+    # 1. Non-admin user is changing another user's password (admin only action)
+    # 2. Any user is changing their own password
+    is_changing_own_password = current_username == username
+    is_non_admin = current_role != "admin"
+
+    if is_changing_own_password or is_non_admin:
         if not body.old_password:
             return JSONResponse(
                 content={"message": "Current password is required"},
@@ -943,8 +887,6 @@ async def update_password(
 @router.put(
     "/users/{username}/role",
     dependencies=[Depends(require_role(["admin"]))],
-    summary="Update user role",
-    description="Updates a user's role. The built-in admin user's role cannot be modified. Requires admin role. Valid roles are defined in the configuration.",
 )
 async def update_role(
     request: Request,
@@ -974,7 +916,6 @@ async def update_role(
         )
 
     User.set_by_id(username, {User.role: body.role})
-    request.app.config_publisher.publisher.publish("config/auth", None)
     return JSONResponse(content={"success": True})
 
 
@@ -988,16 +929,7 @@ async def require_camera_access(
 
     current_user = await get_current_user(request)
     if isinstance(current_user, JSONResponse):
-        detail = "Authentication required"
-        try:
-            error_payload = json.loads(current_user.body)
-            detail = (
-                error_payload.get("message") or error_payload.get("detail") or detail
-            )
-        except Exception:
-            pass
-
-        raise HTTPException(status_code=current_user.status_code, detail=detail)
+        return current_user
 
     role = current_user["role"]
     all_camera_names = set(request.app.frigate_config.cameras.keys())
@@ -1015,61 +947,6 @@ async def require_camera_access(
         )
 
 
-def _get_stream_owner_cameras(request: Request, stream_name: str) -> set[str]:
-    owner_cameras: set[str] = set()
-
-    for camera_name, camera in request.app.frigate_config.cameras.items():
-        if stream_name == camera_name:
-            owner_cameras.add(camera_name)
-            continue
-
-        if stream_name in camera.live.streams.values():
-            owner_cameras.add(camera_name)
-
-    return owner_cameras
-
-
-async def require_go2rtc_stream_access(
-    stream_name: Optional[str] = None,
-    request: Request = None,
-):
-    """Dependency to enforce go2rtc stream access based on owning camera access."""
-    if stream_name is None:
-        return
-
-    current_user = await get_current_user(request)
-    if isinstance(current_user, JSONResponse):
-        detail = "Authentication required"
-        try:
-            error_payload = json.loads(current_user.body)
-            detail = (
-                error_payload.get("message") or error_payload.get("detail") or detail
-            )
-        except Exception:
-            pass
-
-        raise HTTPException(status_code=current_user.status_code, detail=detail)
-
-    role = current_user["role"]
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-    roles_dict = request.app.frigate_config.auth.roles
-    allowed_cameras = User.get_allowed_cameras(role, roles_dict, all_camera_names)
-
-    # Admin or full access bypasses
-    if role == "admin" or not roles_dict.get(role):
-        return
-
-    owner_cameras = _get_stream_owner_cameras(request, stream_name)
-
-    if owner_cameras & set(allowed_cameras):
-        return
-
-    raise HTTPException(
-        status_code=403,
-        detail=f"Access denied to camera '{stream_name}'. Allowed: {allowed_cameras}",
-    )
-
-
 async def get_allowed_cameras_for_filter(request: Request):
     """Dependency to get allowed_cameras for filtering lists."""
     current_user = await get_current_user(request)

frigate/api/camera.py

@@ -17,14 +17,14 @@ from zeep.transports import AsyncTransport
 
 from frigate.api.auth import (
     allow_any_authenticated,
-    require_go2rtc_stream_access,
+    require_camera_access,
     require_role,
 )
 from frigate.api.defs.tags import Tags
 from frigate.config.config import FrigateConfig
 from frigate.util.builtin import clean_camera_user_pass
 from frigate.util.image import run_ffmpeg_snapshot
-from frigate.util.services import ffprobe_stream, is_restricted_go2rtc_source
+from frigate.util.services import ffprobe_stream
 
 logger = logging.getLogger(__name__)
 
@@ -71,27 +71,14 @@ def go2rtc_streams():
 
 
 @router.get(
-    "/go2rtc/streams/{stream_name}",
-    dependencies=[Depends(require_go2rtc_stream_access)],
+    "/go2rtc/streams/{camera_name}", dependencies=[Depends(require_camera_access)]
 )
-def go2rtc_camera_stream(request: Request, stream_name: str):
+def go2rtc_camera_stream(request: Request, camera_name: str):
     r = requests.get(
-        "http://127.0.0.1:1984/api/streams",
-        params={
-            "src": stream_name,
-            "video": "all",
-            "audio": "all",
-            "microphone": "",
-        },
+        f"http://127.0.0.1:1984/api/streams?src={camera_name}&video=all&audio=all&microphone"
     )
     if not r.ok:
-        camera_config = request.app.frigate_config.cameras.get(stream_name)
-
-        if camera_config is None:
-            for camera_name, camera in request.app.frigate_config.cameras.items():
-                if stream_name in camera.live.streams.values():
-                    camera_config = request.app.frigate_config.cameras.get(camera_name)
-                    break
+        camera_config = request.app.frigate_config.cameras.get(camera_name)
 
         if camera_config and camera_config.enabled:
             logger.error("Failed to fetch streams from go2rtc")
@@ -111,19 +98,6 @@ def go2rtc_camera_stream(request: Request, stream_name: str):
 )
 def go2rtc_add_stream(request: Request, stream_name: str, src: str = ""):
     """Add or update a go2rtc stream configuration."""
-    if src and is_restricted_go2rtc_source(src):
-        logger.warning(
-            "Rejected go2rtc stream '%s' with restricted source type (echo/expr/exec)",
-            stream_name,
-        )
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "Restricted stream source type",
-            },
-            status_code=400,
-        )
-
     try:
         params = {"name": stream_name}
         if src:
@@ -874,10 +848,9 @@ async def onvif_probe(
                             try:
                                 if isinstance(uri, str) and uri.startswith("rtsp://"):
                                     if username and password and "@" not in uri:
-                                        # Inject raw credentials and add only the
-                                        # authenticated version. The credentials will be encoded
-                                        # later by ffprobe_stream or the config system.
-                                        cred = f"{username}:{password}@"
+                                        # Inject URL-encoded credentials and add only the
+                                        # authenticated version.
+                                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
                                         injected = uri.replace(
                                             "rtsp://", f"rtsp://{cred}", 1
                                         )
@@ -930,8 +903,12 @@ async def onvif_probe(
                     "/cam/realmonitor?channel=1&subtype=0",
                     "/11",
                 ]
-                # Use raw credentials for pattern fallback URIs when provided
-                auth_str = f"{username}:{password}@" if username and password else ""
+                # Use URL-encoded credentials for pattern fallback URIs when provided
+                auth_str = (
+                    f"{quote_plus(username)}:{quote_plus(password)}@"
+                    if username and password
+                    else ""
+                )
                 rtsp_port = 554
                 for path in common_paths:
                     uri = f"rtsp://{auth_str}{host}:{rtsp_port}{path}"
@@ -953,7 +930,7 @@ async def onvif_probe(
                         and uri.startswith("rtsp://")
                         and "@" not in uri
                     ):
-                        cred = f"{username}:{password}@"
+                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
                         cred_uri = uri.replace("rtsp://", f"rtsp://{cred}", 1)
                         if cred_uri not in to_test:
                             to_test.append(cred_uri)

frigate/api/classification.py

@@ -31,7 +31,6 @@ from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
 from frigate.config.camera import DetectConfig
-from frigate.config.classification import ObjectClassificationType
 from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event
@@ -40,7 +39,6 @@ from frigate.util.classification import (
     collect_state_classification_examples,
     get_dataset_image_count,
     read_training_metadata,
-    write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
 
@@ -73,7 +71,7 @@ def get_faces():
         face_dict[name] = []
 
         for file in filter(
-            lambda f: f.lower().endswith((".webp", ".png", ".jpg", ".jpeg")),
+            lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
             os.listdir(face_dir),
         ):
             face_dict[name].append(file)
@@ -582,7 +580,7 @@ def get_classification_dataset(name: str):
         dataset_dict[category_name] = []
 
         for file in filter(
-            lambda f: f.lower().endswith((".webp", ".png", ".jpg", ".jpeg")),
+            lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
             os.listdir(category_dir),
         ):
             dataset_dict[category_name].append(file)
@@ -624,59 +622,6 @@ def get_classification_dataset(name: str):
     )
 
 
-@router.get(
-    "/classification/attributes",
-    summary="Get custom classification attributes",
-    description="""Returns custom classification attributes for a given object type.
-    Only includes models with classification_type set to 'attribute'.
-    By default returns a flat sorted list of all attribute labels.
-    If group_by_model is true, returns attributes grouped by model name.""",
-)
-def get_custom_attributes(
-    request: Request, object_type: str = None, group_by_model: bool = False
-):
-    models_with_attributes = {}
-
-    for (
-        model_key,
-        model_config,
-    ) in request.app.frigate_config.classification.custom.items():
-        if (
-            not model_config.enabled
-            or not model_config.object_config
-            or model_config.object_config.classification_type
-            != ObjectClassificationType.attribute
-        ):
-            continue
-
-        model_objects = getattr(model_config.object_config, "objects", []) or []
-        if object_type is not None and object_type not in model_objects:
-            continue
-
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        if not os.path.exists(dataset_dir):
-            continue
-
-        attributes = []
-        for category_name in os.listdir(dataset_dir):
-            category_dir = os.path.join(dataset_dir, category_name)
-            if os.path.isdir(category_dir) and category_name != "none":
-                attributes.append(category_name)
-
-        if attributes:
-            model_name = model_config.name or model_key
-            models_with_attributes[model_name] = sorted(attributes)
-
-    if group_by_model:
-        return JSONResponse(content=models_with_attributes)
-    else:
-        # Flatten to a unique sorted list
-        all_attributes = set()
-        for attributes in models_with_attributes.values():
-            all_attributes.update(attributes)
-        return JSONResponse(content=sorted(list(all_attributes)))
-
-
 @router.get(
     "/classification/{name}/train",
     summary="Get classification train images",
@@ -693,7 +638,7 @@ def get_classification_images(name: str):
         status_code=200,
         content=list(
             filter(
-                lambda f: f.lower().endswith((".webp", ".png", ".jpg", ".jpeg")),
+                lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
                 os.listdir(train_dir),
             )
         ),
@@ -759,28 +704,15 @@ def delete_classification_dataset_images(
         CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
     )
 
-    deleted_count = 0
     for id in list_of_ids:
         file_path = os.path.join(folder, sanitize_filename(id))
 
         if os.path.isfile(file_path):
             os.unlink(file_path)
-            deleted_count += 1
 
     if os.path.exists(folder) and not os.listdir(folder) and category.lower() != "none":
         os.rmdir(folder)
 
-    # Update training metadata to reflect deleted images
-    # This ensures the dataset is marked as changed after deletion
-    # (even if the total count happens to be the same after adding and deleting)
-    if deleted_count > 0:
-        sanitized_name = sanitize_filename(name)
-        metadata = read_training_metadata(sanitized_name)
-        if metadata:
-            last_count = metadata.get("last_training_image_count", 0)
-            updated_count = max(0, last_count - deleted_count)
-            write_training_metadata(sanitized_name, updated_count)
-
     return JSONResponse(
         content=({"success": True, "message": "Successfully deleted images."}),
         status_code=200,
@@ -856,12 +788,6 @@ def rename_classification_category(
 
     try:
         os.rename(old_folder, new_folder)
-
-        # Mark dataset as ready to train by resetting training metadata
-        # This ensures the dataset is marked as changed after renaming
-        sanitized_name = sanitize_filename(name)
-        write_training_metadata(sanitized_name, 0)
-
         return JSONResponse(
             content=(
                 {

frigate/api/defs/query/events_query_parameters.py

@@ -12,7 +12,6 @@ class EventsQueryParams(BaseModel):
     labels: Optional[str] = "all"
     sub_label: Optional[str] = "all"
     sub_labels: Optional[str] = "all"
-    attributes: Optional[str] = "all"
     zone: Optional[str] = "all"
     zones: Optional[str] = "all"
     limit: Optional[int] = 100
@@ -59,8 +58,6 @@ class EventsSearchQueryParams(BaseModel):
     limit: Optional[int] = 50
     cameras: Optional[str] = "all"
     labels: Optional[str] = "all"
-    sub_labels: Optional[str] = "all"
-    attributes: Optional[str] = "all"
     zones: Optional[str] = "all"
     after: Optional[float] = None
     before: Optional[float] = None

frigate/api/defs/query/review_query_parameters.py

@@ -10,7 +10,7 @@ class ReviewQueryParams(BaseModel):
     cameras: str = "all"
     labels: str = "all"
     zones: str = "all"
-    reviewed: Union[int, SkipJsonSchema[None]] = None
+    reviewed: int = 0
     limit: Union[int, SkipJsonSchema[None]] = None
     severity: Union[SeverityEnum, SkipJsonSchema[None]] = None
     before: Union[float, SkipJsonSchema[None]] = None

frigate/api/defs/request/events_body.py

@@ -24,13 +24,6 @@ class EventsLPRBody(BaseModel):
     )
 
 
-class EventsAttributesBody(BaseModel):
-    attributes: List[str] = Field(
-        title="Selected classification attributes for the event",
-        default_factory=list,
-    )
-
-
 class EventsDescriptionBody(BaseModel):
     description: Union[str, None] = Field(title="The description of the event")
 

frigate/api/defs/request/export_recordings_body.py

@@ -1,10 +1,9 @@
-from typing import Optional, Union
+from typing import Union
 
 from pydantic import BaseModel, Field
 from pydantic.json_schema import SkipJsonSchema
 
 from frigate.record.export import (
-    ChaptersEnum,
     PlaybackFactorEnum,
     PlaybackSourceEnum,
 )
@@ -17,13 +16,5 @@ class ExportRecordingsBody(BaseModel):
     source: PlaybackSourceEnum = Field(
         default=PlaybackSourceEnum.recordings, title="Playback source"
     )
-    name: Optional[str] = Field(title="Friendly name", default=None, max_length=256)
+    name: str = Field(title="Friendly name", default=None, max_length=256)
     image_path: Union[str, SkipJsonSchema[None]] = None
-    chapters: Optional[ChaptersEnum] = Field(
-        default=None,
-        title="Chapter mode",
-        description=(
-            "Optional chapter metadata to embed in the export. When omitted, "
-            "no chapter track is added."
-        ),
-    )

frigate/api/event.py

@@ -37,7 +37,6 @@ from frigate.api.defs.query.regenerate_query_parameters import (
     RegenerateQueryParameters,
 )
 from frigate.api.defs.request.events_body import (
-    EventsAttributesBody,
     EventsCreateBody,
     EventsDeleteBody,
     EventsDescriptionBody,
@@ -56,7 +55,6 @@ from frigate.api.defs.response.event_response import (
 from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.comms.event_metadata_updater import EventMetadataTypeEnum
-from frigate.config.classification import ObjectClassificationType
 from frigate.const import CLIPS_DIR, TRIGGER_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
@@ -69,25 +67,6 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.events])
 
 
-def _build_attribute_filter_clause(attributes: str):
-    filtered_attributes = [
-        attr.strip() for attr in attributes.split(",") if attr.strip()
-    ]
-    attribute_clauses = []
-
-    for attr in filtered_attributes:
-        attribute_clauses.append(Event.data.cast("text") % f'*:"{attr}"*')
-
-        escaped_attr = json.dumps(attr, ensure_ascii=True)[1:-1]
-        if escaped_attr != attr:
-            attribute_clauses.append(Event.data.cast("text") % f'*:"{escaped_attr}"*')
-
-    if not attribute_clauses:
-        return None
-
-    return reduce(operator.or_, attribute_clauses)
-
-
 @router.get(
     "/events",
     response_model=list[EventResponse],
@@ -120,8 +99,6 @@ def events(
     if sub_labels == "all" and sub_label != "all":
         sub_labels = sub_label
 
-    attributes = unquote(params.attributes)
-
     zone = params.zone
     zones = params.zones
 
@@ -210,12 +187,6 @@ def events(
         sub_label_clause = reduce(operator.or_, sub_label_clauses)
         clauses.append((sub_label_clause))
 
-    if attributes != "all":
-        # Custom classification results are stored as data[model_name] = result_value
-        attribute_clause = _build_attribute_filter_clause(attributes)
-        if attribute_clause is not None:
-            clauses.append(attribute_clause)
-
     if recognized_license_plate != "all":
         filtered_recognized_license_plates = recognized_license_plate.split(",")
 
@@ -521,8 +492,6 @@ def events_search(
     # Filters
     cameras = params.cameras
     labels = params.labels
-    sub_labels = params.sub_labels
-    attributes = unquote(params.attributes)
     zones = params.zones
     after = params.after
     before = params.before
@@ -597,34 +566,6 @@ def events_search(
     if labels != "all":
         event_filters.append((Event.label << labels.split(",")))
 
-    if sub_labels != "all":
-        # use matching so joined sub labels are included
-        # for example a sub label 'bob' would get events
-        # with sub labels 'bob' and 'bob, john'
-        sub_label_clauses = []
-        filtered_sub_labels = sub_labels.split(",")
-
-        if "None" in filtered_sub_labels:
-            filtered_sub_labels.remove("None")
-            sub_label_clauses.append((Event.sub_label.is_null()))
-
-        for label in filtered_sub_labels:
-            sub_label_clauses.append(
-                (Event.sub_label.cast("text") == label)
-            )  # include exact matches
-
-            # include this label when part of a list
-            sub_label_clauses.append((Event.sub_label.cast("text") % f"*{label},*"))
-            sub_label_clauses.append((Event.sub_label.cast("text") % f"*, {label}*"))
-
-        event_filters.append((reduce(operator.or_, sub_label_clauses)))
-
-    if attributes != "all":
-        # Custom classification results are stored as data[model_name] = result_value
-        attribute_clause = _build_attribute_filter_clause(attributes)
-        if attribute_clause is not None:
-            event_filters.append(attribute_clause)
-
     if zones != "all":
         zone_clauses = []
         filtered_zones = zones.split(",")
@@ -1410,107 +1351,6 @@ async def set_plate(
     )
 
 
-@router.post(
-    "/events/{event_id}/attributes",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Set custom classification attributes",
-    description=(
-        "Sets an event's custom classification attributes for all attribute-type "
-        "models that apply to the event's object type."
-    ),
-)
-async def set_attributes(
-    request: Request,
-    event_id: str,
-    body: EventsAttributesBody,
-):
-    try:
-        event: Event = Event.get(Event.id == event_id)
-        await require_camera_access(event.camera, request=request)
-    except DoesNotExist:
-        return JSONResponse(
-            content=({"success": False, "message": f"Event {event_id} not found."}),
-            status_code=404,
-        )
-
-    object_type = event.label
-    selected_attributes = set(body.attributes or [])
-    applied_updates: list[dict[str, str | float | None]] = []
-
-    for (
-        model_key,
-        model_config,
-    ) in request.app.frigate_config.classification.custom.items():
-        # Only apply to enabled attribute classifiers that target this object type
-        if (
-            not model_config.enabled
-            or not model_config.object_config
-            or model_config.object_config.classification_type
-            != ObjectClassificationType.attribute
-            or object_type not in (model_config.object_config.objects or [])
-        ):
-            continue
-
-        # Get available labels from dataset directory
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        available_labels = set()
-
-        if os.path.exists(dataset_dir):
-            for category_name in os.listdir(dataset_dir):
-                category_dir = os.path.join(dataset_dir, category_name)
-                if os.path.isdir(category_dir):
-                    available_labels.add(category_name)
-
-        if not available_labels:
-            logger.warning(
-                "No dataset found for custom attribute model %s at %s",
-                model_key,
-                dataset_dir,
-            )
-            continue
-
-        # Find all selected attributes that apply to this model
-        model_name = model_config.name or model_key
-        matching_attrs = selected_attributes & available_labels
-
-        if matching_attrs:
-            # Publish updates for each selected attribute
-            for attr in matching_attrs:
-                request.app.event_metadata_updater.publish(
-                    (event_id, model_name, attr, 1.0),
-                    EventMetadataTypeEnum.attribute.value,
-                )
-                applied_updates.append(
-                    {"model": model_name, "label": attr, "score": 1.0}
-                )
-        else:
-            # Clear this model's attribute
-            request.app.event_metadata_updater.publish(
-                (event_id, model_name, None, None),
-                EventMetadataTypeEnum.attribute.value,
-            )
-            applied_updates.append({"model": model_name, "label": None, "score": None})
-
-    if len(applied_updates) == 0:
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "No matching attributes found for this object type.",
-            },
-            status_code=400,
-        )
-
-    return JSONResponse(
-        content={
-            "success": True,
-            "message": f"Updated {len(applied_updates)} attribute(s)",
-            "applied": applied_updates,
-        },
-        status_code=200,
-    )
-
-
 @router.post(
     "/events/{event_id}/description",
     response_model=GenericResponse,

frigate/api/export.py

@@ -91,26 +91,8 @@ def export_recording(
     playback_factor = body.playback
     playback_source = body.source
     friendly_name = body.name
-
-    # sanitize_filepath normalizes "\" to "/" but leaves ".." intact, so a path
-    # like "clips\..\..\etc/passwd" passes the CLIPS_DIR prefix check yet still
-    # escapes the directory once resolved. A valid snapshot path never uses "..".
-    if body.image_path and ".." in body.image_path:
-        return JSONResponse(
-            content=({"success": False, "message": "Invalid image path"}),
-            status_code=400,
-        )
-
     existing_image = sanitize_filepath(body.image_path) if body.image_path else None
 
-    # a chapters value in the request body overrides the camera's export config
-    camera_config = request.app.frigate_config.cameras[camera_name]
-    chapters = (
-        body.chapters
-        if body.chapters is not None
-        else camera_config.record.export.chapters
-    )
-
     # Ensure that existing_image is a valid path
     if existing_image and not existing_image.startswith(CLIPS_DIR):
         return JSONResponse(
@@ -179,7 +161,6 @@ def export_recording(
             if playback_source in PlaybackSourceEnum.__members__.values()
             else PlaybackSourceEnum.recordings
         ),
-        chapters=chapters,
     )
     exporter.start()
     return JSONResponse(

frigate/api/media.py

@@ -50,28 +50,13 @@ from frigate.models import Event, Previews, Recordings, Regions, ReviewSegment
 from frigate.track.object_processing import TrackedObjectProcessor
 from frigate.util.file import get_event_thumbnail_bytes
 from frigate.util.image import get_image_from_recording
-from frigate.util.media import get_keyframe_before
 from frigate.util.time import get_dst_transitions
 
 logger = logging.getLogger(__name__)
 
-
 router = APIRouter(tags=[Tags.media])
 
 
-def _resolve_cache_age(max_cache_age: int) -> int:
-    """Return max_cache_age as an int.
-
-    When a media handler is invoked directly by another handler instead of
-    through its route, FastAPI doesn't resolve the Query() default and
-    max_cache_age arrives as the Query object; fall back to its int default.
-    """
-    if isinstance(max_cache_age, int):
-        return max_cache_age
-
-    return max_cache_age.default
-
-
 @router.get("/{camera_name}", dependencies=[Depends(require_camera_access)])
 async def mjpeg_feed(
     request: Request,
@@ -393,9 +378,7 @@ async def submit_recording_snapshot_to_plus(
             )
 
         nd = cv2.imdecode(np.frombuffer(image_data, dtype=np.int8), cv2.IMREAD_COLOR)
-        await asyncio.to_thread(
-            request.app.frigate_config.plus_api.upload_image, nd, camera_name
-        )
+        request.app.frigate_config.plus_api.upload_image(nd, camera_name)
 
         return JSONResponse(
             content={
@@ -917,33 +900,6 @@ async def vod_ts(
         if recording.end_time > end_ts:
             duration -= int((recording.end_time - end_ts) * 1000)
 
-        # nginx-vod-module pushes clipFrom forward to the next keyframe,
-        # which can leave too few frames and produce an empty/unplayable
-        # segment. Snap clipFrom back to the preceding keyframe so the
-        # segment always starts with a decodable frame.
-        if "clipFrom" in clip:
-            keyframe_ms = get_keyframe_before(recording.path, clip["clipFrom"])
-            if keyframe_ms is not None:
-                gained = clip["clipFrom"] - keyframe_ms
-                clip["clipFrom"] = keyframe_ms
-                duration += gained
-                logger.debug(
-                    "VOD: snapped clipFrom to keyframe at %sms for %s, duration now %sms",
-                    keyframe_ms,
-                    recording.path,
-                    duration,
-                )
-            else:
-                # could not read keyframes, remove clipFrom to use full recording
-                logger.debug(
-                    "VOD: no keyframe info for %s, removing clipFrom to use full recording",
-                    recording.path,
-                )
-                del clip["clipFrom"]
-                duration = int(recording.duration * 1000)
-                if recording.end_time > end_ts:
-                    duration -= int((recording.end_time - end_ts) * 1000)
-
         if duration < min_duration_ms:
             # skip if the clip has no valid duration (too short to contain frames)
             logger.debug(
@@ -1157,6 +1113,7 @@ async def event_snapshot(
 
 @router.get(
     "/events/{event_id}/thumbnail.{extension}",
+    dependencies=[Depends(require_camera_access)],
 )
 async def event_thumbnail(
     request: Request,
@@ -1200,12 +1157,11 @@ async def event_thumbnail(
             status_code=404,
         )
 
-    img_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
-    img = cv2.imdecode(img_as_np, flags=1)
-
     # android notifications prefer a 2:1 ratio
     if format == "android":
-        img = cv2.copyMakeBorder(
+        img_as_np = np.frombuffer(thumbnail_bytes, dtype=np.uint8)
+        img = cv2.imdecode(img_as_np, flags=1)
+        thumbnail = cv2.copyMakeBorder(
             img,
             0,
             0,
@@ -1215,20 +1171,20 @@ async def event_thumbnail(
             (0, 0, 0),
         )
 
-    quality_params = None
-    if extension in (Extension.jpg, Extension.jpeg):
-        quality_params = [int(cv2.IMWRITE_JPEG_QUALITY), 70]
-    elif extension == Extension.webp:
-        quality_params = [int(cv2.IMWRITE_WEBP_QUALITY), 60]
+        quality_params = None
+        if extension in (Extension.jpg, Extension.jpeg):
+            quality_params = [int(cv2.IMWRITE_JPEG_QUALITY), 70]
+        elif extension == Extension.webp:
+            quality_params = [int(cv2.IMWRITE_WEBP_QUALITY), 60]
 
-    _, encoded = cv2.imencode(f".{extension.value}", img, quality_params)
-    thumbnail_bytes = encoded.tobytes()
+        _, img = cv2.imencode(f".{extension.value}", thumbnail, quality_params)
+        thumbnail_bytes = img.tobytes()
 
     return Response(
         thumbnail_bytes,
         media_type=extension.get_mime_type(),
         headers={
-            "Cache-Control": f"private, max-age={_resolve_cache_age(max_cache_age)}"
+            "Cache-Control": f"private, max-age={max_cache_age}"
             if event_complete
             else "no-store",
         },
@@ -1358,12 +1314,12 @@ def grid_snapshot(
 
 @router.get(
     "/events/{event_id}/snapshot-clean.webp",
+    dependencies=[Depends(require_camera_access)],
 )
-async def event_snapshot_clean(request: Request, event_id: str, download: bool = False):
+def event_snapshot_clean(request: Request, event_id: str, download: bool = False):
     webp_bytes = None
     try:
         event = Event.get(Event.id == event_id)
-        await require_camera_access(event.camera, request=request)
         snapshot_config = request.app.frigate_config.cameras[event.camera].snapshots
         if not (snapshot_config.enabled and event.has_snapshot):
             return JSONResponse(
@@ -1484,7 +1440,7 @@ async def event_snapshot_clean(request: Request, event_id: str, download: bool =
 
 
 @router.get(
-    "/events/{event_id}/clip.mp4",
+    "/events/{event_id}/clip.mp4", dependencies=[Depends(require_camera_access)]
 )
 async def event_clip(
     request: Request,
@@ -1498,8 +1454,6 @@ async def event_clip(
             content={"success": False, "message": "Event not found"}, status_code=404
         )
 
-    await require_camera_access(event.camera, request=request)
-
     if not event.has_clip:
         return JSONResponse(
             content={"success": False, "message": "Clip not available"}, status_code=404
@@ -1516,9 +1470,9 @@ async def event_clip(
 
 
 @router.get(
-    "/events/{event_id}/preview.gif",
+    "/events/{event_id}/preview.gif", dependencies=[Depends(require_camera_access)]
 )
-async def event_preview(request: Request, event_id: str):
+def event_preview(request: Request, event_id: str):
     try:
         event: Event = Event.get(Event.id == event_id)
     except DoesNotExist:
@@ -1526,20 +1480,18 @@ async def event_preview(request: Request, event_id: str):
             content={"success": False, "message": "Event not found"}, status_code=404
         )
 
-    await require_camera_access(event.camera, request=request)
-
     start_ts = event.start_time
     end_ts = start_ts + (
         min(event.end_time - event.start_time, 20) if event.end_time else 20
     )
-    return await preview_gif(request, event.camera, start_ts, end_ts)
+    return preview_gif(request, event.camera, start_ts, end_ts)
 
 
 @router.get(
     "/{camera_name}/start/{start_ts}/end/{end_ts}/preview.gif",
     dependencies=[Depends(require_camera_access)],
 )
-async def preview_gif(
+def preview_gif(
     request: Request,
     camera_name: str,
     start_ts: float,
@@ -1550,25 +1502,25 @@ async def preview_gif(
 ):
     if datetime.fromtimestamp(start_ts) < datetime.now().replace(minute=0, second=0):
         # has preview mp4
-        try:
-            preview: Previews = (
-                Previews.select(
-                    Previews.camera,
-                    Previews.path,
-                    Previews.duration,
-                    Previews.start_time,
-                    Previews.end_time,
-                )
-                .where(
-                    Previews.start_time.between(start_ts, end_ts)
-                    | Previews.end_time.between(start_ts, end_ts)
-                    | ((start_ts > Previews.start_time) & (end_ts < Previews.end_time))
-                )
-                .where(Previews.camera == camera_name)
-                .limit(1)
-                .get()
+        preview: Previews = (
+            Previews.select(
+                Previews.camera,
+                Previews.path,
+                Previews.duration,
+                Previews.start_time,
+                Previews.end_time,
             )
-        except DoesNotExist:
+            .where(
+                Previews.start_time.between(start_ts, end_ts)
+                | Previews.end_time.between(start_ts, end_ts)
+                | ((start_ts > Previews.start_time) & (end_ts < Previews.end_time))
+            )
+            .where(Previews.camera == camera_name)
+            .limit(1)
+            .get()
+        )
+
+        if not preview:
             return JSONResponse(
                 content={"success": False, "message": "Preview not found"},
                 status_code=404,
@@ -1602,8 +1554,7 @@ async def preview_gif(
             "-",
         ]
 
-        process = await asyncio.to_thread(
-            sp.run,
+        process = sp.run(
             ffmpeg_cmd,
             capture_output=True,
         )
@@ -1670,8 +1621,7 @@ async def preview_gif(
             "-",
         ]
 
-        process = await asyncio.to_thread(
-            sp.run,
+        process = sp.run(
             ffmpeg_cmd,
             input=str.encode("\n".join(selected_previews)),
             capture_output=True,
@@ -1690,7 +1640,7 @@ async def preview_gif(
         gif_bytes,
         media_type="image/gif",
         headers={
-            "Cache-Control": f"private, max-age={_resolve_cache_age(max_cache_age)}",
+            "Cache-Control": f"private, max-age={max_cache_age}",
             "Content-Type": "image/gif",
         },
     )
@@ -1700,7 +1650,7 @@ async def preview_gif(
     "/{camera_name}/start/{start_ts}/end/{end_ts}/preview.mp4",
     dependencies=[Depends(require_camera_access)],
 )
-async def preview_mp4(
+def preview_mp4(
     request: Request,
     camera_name: str,
     start_ts: float,
@@ -1780,8 +1730,7 @@ async def preview_mp4(
             path,
         ]
 
-        process = await asyncio.to_thread(
-            sp.run,
+        process = sp.run(
             ffmpeg_cmd,
             capture_output=True,
         )
@@ -1845,8 +1794,7 @@ async def preview_mp4(
             path,
         ]
 
-        process = await asyncio.to_thread(
-            sp.run,
+        process = sp.run(
             ffmpeg_cmd,
             input=str.encode("\n".join(selected_previews)),
             capture_output=True,
@@ -1861,7 +1809,7 @@ async def preview_mp4(
 
     headers = {
         "Content-Description": "File Transfer",
-        "Cache-Control": f"private, max-age={_resolve_cache_age(max_cache_age)}",
+        "Cache-Control": f"private, max-age={max_cache_age}",
         "Content-Type": "video/mp4",
         "Content-Length": str(os.path.getsize(path)),
         # nginx: https://nginx.org/en/docs/http/ngx_http_proxy_module.html#proxy_ignore_headers
@@ -1876,8 +1824,8 @@ async def preview_mp4(
     )
 
 
-@router.get("/review/{event_id}/preview")
-async def review_preview(
+@router.get("/review/{event_id}/preview", dependencies=[Depends(require_camera_access)])
+def review_preview(
     request: Request,
     event_id: str,
     format: str = Query(default="gif", enum=["gif", "mp4"]),
@@ -1890,8 +1838,6 @@ async def review_preview(
             status_code=404,
         )
 
-    await require_camera_access(review.camera, request=request)
-
     padding = 8
     start_ts = review.start_time - padding
     end_ts = (
@@ -1899,20 +1845,18 @@ async def review_preview(
     )
 
     if format == "gif":
-        return await preview_gif(request, review.camera, start_ts, end_ts)
+        return preview_gif(request, review.camera, start_ts, end_ts)
     else:
-        return await preview_mp4(request, review.camera, start_ts, end_ts)
+        return preview_mp4(request, review.camera, start_ts, end_ts)
 
 
 @router.get(
-    "/preview/{file_name}/thumbnail.jpg",
-    dependencies=[Depends(allow_any_authenticated())],
+    "/preview/{file_name}/thumbnail.jpg", dependencies=[Depends(require_camera_access)]
 )
 @router.get(
-    "/preview/{file_name}/thumbnail.webp",
-    dependencies=[Depends(allow_any_authenticated())],
+    "/preview/{file_name}/thumbnail.webp", dependencies=[Depends(require_camera_access)]
 )
-async def preview_thumbnail(request: Request, file_name: str):
+def preview_thumbnail(file_name: str):
     """Get a thumbnail from the cached preview frames."""
     if len(file_name) > 1000:
         return JSONResponse(
@@ -1922,17 +1866,6 @@ async def preview_thumbnail(request: Request, file_name: str):
             status_code=403,
         )
 
-    # Extract camera name from preview filename (format: preview_{camera}-{timestamp}.ext)
-    if not file_name.startswith("preview_"):
-        return JSONResponse(
-            content={"success": False, "message": "Invalid preview filename"},
-            status_code=400,
-        )
-    # Use rsplit to handle camera names containing dashes (e.g. front-door)
-    name_part = file_name[len("preview_") :].rsplit(".", 1)[0]  # strip extension
-    camera_name = name_part.rsplit("-", 1)[0]  # split off timestamp
-    await require_camera_access(camera_name, request=request)
-
     safe_file_name_current = sanitize_filename(file_name)
     preview_dir = os.path.join(CACHE_DIR, "preview_frames")
 
@@ -2002,7 +1935,7 @@ async def label_clip(request: Request, camera_name: str, label: str):
     try:
         event = event_query.get()
 
-        return await event_clip(request, event.id, 0)
+        return await event_clip(request, event.id)
     except DoesNotExist:
         return JSONResponse(
             content={"success": False, "message": "Event not found"}, status_code=404

frigate/api/review.py

@@ -144,8 +144,6 @@ async def review(
             (UserReviewStatus.has_been_reviewed == False)
             | (UserReviewStatus.has_been_reviewed.is_null())
         )
-    elif reviewed == 1:
-        review_query = review_query.where(UserReviewStatus.has_been_reviewed == True)
 
     # Apply ordering and limit
     review_query = (

frigate/app.py

@@ -100,10 +100,6 @@ class FrigateApp:
             )
             if (
                 config.semantic_search.enabled
-                or any(
-                    c.objects.genai.enabled or c.review.genai.enabled
-                    for c in config.cameras.values()
-                )
                 or config.lpr.enabled
                 or config.face_recognition.enabled
                 or len(config.classification.custom) > 0

frigate/comms/mqtt.py

@@ -225,8 +225,7 @@ class MqttClient(Communicator):
             "birdseye_mode",
             "review_alerts",
             "review_detections",
-            "object_descriptions",
-            "review_descriptions",
+            "genai",
         ]
 
         for name in self.config.cameras.keys():

frigate/comms/webpush.py

@@ -17,12 +17,11 @@ from titlecase import titlecase
 from frigate.comms.base_communicator import Communicator
 from frigate.comms.config_updater import ConfigSubscriber
 from frigate.config import FrigateConfig
-from frigate.config.auth import AuthConfig
 from frigate.config.camera.updater import (
     CameraConfigUpdateEnum,
     CameraConfigUpdateSubscriber,
 )
-from frigate.const import BASE_DIR, CONFIG_DIR
+from frigate.const import CONFIG_DIR
 from frigate.models import User
 
 logger = logging.getLogger(__name__)
@@ -59,7 +58,6 @@ class WebPushClient(Communicator):
             for c in self.config.cameras.values()
         }
         self.last_notification_time: float = 0
-        self.user_cameras: dict[str, set[str]] = {}
         self.notification_queue: queue.Queue[PushNotification] = queue.Queue()
         self.notification_thread = threading.Thread(
             target=self._process_notifications, daemon=True
@@ -80,12 +78,13 @@ class WebPushClient(Communicator):
             for sub in user["notification_tokens"]:
                 self.web_pushers[user["username"]].append(WebPusher(sub))
 
-        # notification and auth config updater
-        self.global_config_subscriber = ConfigSubscriber("config/")
+        # notification config updater
+        self.global_config_subscriber = ConfigSubscriber(
+            "config/notifications", exact=True
+        )
         self.config_subscriber = CameraConfigUpdateSubscriber(
             self.config, self.config.cameras, [CameraConfigUpdateEnum.notifications]
         )
-        self._refresh_user_cameras()
 
     def subscribe(self, receiver: Callable) -> None:
         """Wrapper for allowing dispatcher to subscribe."""
@@ -165,19 +164,13 @@ class WebPushClient(Communicator):
 
     def publish(self, topic: str, payload: Any, retain: bool = False) -> None:
         """Wrapper for publishing when client is in valid state."""
-        # check for updated global config (notifications, auth)
-        while True:
-            config_topic, config_payload = (
-                self.global_config_subscriber.check_for_update()
-            )
-            if config_topic is None:
-                break
-            if config_topic == "config/notifications" and config_payload:
-                self.config.notifications = config_payload
-            elif config_topic == "config/auth":
-                if isinstance(config_payload, AuthConfig):
-                    self.config.auth = config_payload
-                self._refresh_user_cameras()
+        # check for updated notification config
+        _, updated_notification_config = (
+            self.global_config_subscriber.check_for_update()
+        )
+
+        if updated_notification_config:
+            self.config.notifications = updated_notification_config
 
         updates = self.config_subscriber.check_for_updates()
 
@@ -298,31 +291,6 @@ class WebPushClient(Communicator):
             except Exception as e:
                 logger.error(f"Error processing notification: {str(e)}")
 
-    def _refresh_user_cameras(self) -> None:
-        """Rebuild the user-to-cameras access cache from the database."""
-        all_camera_names = set(self.config.cameras.keys())
-        roles_dict = self.config.auth.roles
-        updated: dict[str, set[str]] = {}
-        for user in User.select(User.username, User.role).dicts().iterator():
-            allowed = User.get_allowed_cameras(
-                user["role"], roles_dict, all_camera_names
-            )
-            updated[user["username"]] = set(allowed)
-            logger.debug(
-                "User %s has access to cameras: %s",
-                user["username"],
-                ", ".join(allowed),
-            )
-        self.user_cameras = updated
-
-    def _user_has_camera_access(self, username: str, camera: str) -> bool:
-        """Check if a user has access to a specific camera based on cached roles."""
-        allowed = self.user_cameras.get(username)
-        if allowed is None:
-            logger.debug(f"No camera access information found for user {username}")
-            return False
-        return camera in allowed
-
     def _within_cooldown(self, camera: str) -> bool:
         now = datetime.datetime.now().timestamp()
         if now - self.last_notification_time < self.config.notifications.cooldown:
@@ -403,7 +371,7 @@ class WebPushClient(Communicator):
 
         sorted_objects.update(payload["after"]["data"]["sub_labels"])
 
-        image = f"{payload['after']['thumb_path'].replace(BASE_DIR, '')}"
+        image = f"{payload['after']['thumb_path'].replace('/media/frigate', '')}"
         ended = state == "end" or state == "genai"
 
         if state == "genai" and payload["after"]["data"]["metadata"]:
@@ -420,7 +388,7 @@ class WebPushClient(Communicator):
             else:
                 title = base_title
 
-            message = payload["after"]["data"]["metadata"]["shortSummary"]
+            message = payload["after"]["data"]["metadata"]["scene"]
         else:
             zone_names = payload["after"]["data"]["zones"]
             formatted_zone_names = []
@@ -450,14 +418,6 @@ class WebPushClient(Communicator):
         logger.debug(f"Sending push notification for {camera}, review ID {reviewId}")
 
         for user in self.web_pushers:
-            if not self._user_has_camera_access(user, camera):
-                logger.debug(
-                    "Skipping notification for user %s - no access to camera %s",
-                    user,
-                    camera,
-                )
-                continue
-
             self.send_push_notification(
                 user=user,
                 payload=payload,
@@ -505,14 +465,6 @@ class WebPushClient(Communicator):
         )
 
         for user in self.web_pushers:
-            if not self._user_has_camera_access(user, camera):
-                logger.debug(
-                    "Skipping notification for user %s - no access to camera %s",
-                    user,
-                    camera,
-                )
-                continue
-
             self.send_push_notification(
                 user=user,
                 payload=payload,

frigate/comms/ws.py

@@ -17,117 +17,9 @@ from ws4py.websocket import WebSocket as WebSocket_
 
 from frigate.comms.base_communicator import Communicator
 from frigate.config import FrigateConfig
-from frigate.const import (
-    CLEAR_ONGOING_REVIEW_SEGMENTS,
-    EXPIRE_AUDIO_ACTIVITY,
-    INSERT_MANY_RECORDINGS,
-    INSERT_PREVIEW,
-    NOTIFICATION_TEST,
-    REQUEST_REGION_GRID,
-    UPDATE_AUDIO_ACTIVITY,
-    UPDATE_AUDIO_TRANSCRIPTION_STATE,
-    UPDATE_BIRDSEYE_LAYOUT,
-    UPDATE_CAMERA_ACTIVITY,
-    UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
-    UPDATE_EVENT_DESCRIPTION,
-    UPDATE_MODEL_STATE,
-    UPDATE_REVIEW_DESCRIPTION,
-    UPSERT_REVIEW_SEGMENT,
-)
-from frigate.models import User
 
 logger = logging.getLogger(__name__)
 
-# Internal IPC topics — NEVER allowed from WebSocket, regardless of role
-_WS_BLOCKED_TOPICS = frozenset(
-    {
-        INSERT_MANY_RECORDINGS,
-        INSERT_PREVIEW,
-        REQUEST_REGION_GRID,
-        UPSERT_REVIEW_SEGMENT,
-        CLEAR_ONGOING_REVIEW_SEGMENTS,
-        UPDATE_CAMERA_ACTIVITY,
-        UPDATE_AUDIO_ACTIVITY,
-        EXPIRE_AUDIO_ACTIVITY,
-        UPDATE_EVENT_DESCRIPTION,
-        UPDATE_REVIEW_DESCRIPTION,
-        UPDATE_MODEL_STATE,
-        UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
-        UPDATE_BIRDSEYE_LAYOUT,
-        UPDATE_AUDIO_TRANSCRIPTION_STATE,
-        NOTIFICATION_TEST,
-    }
-)
-
-# Read-only topics any authenticated user (including viewer) can send
-_WS_VIEWER_TOPICS = frozenset(
-    {
-        "onConnect",
-        "modelState",
-        "audioTranscriptionState",
-        "birdseyeLayout",
-        "embeddingsReindexProgress",
-    }
-)
-
-# Camera-scoped command topics a camera-authorized (non-admin) user may send.
-_WS_CAMERA_COMMAND_TOPICS = frozenset({"ptz"})
-
-
-def _check_ws_authorization(
-    topic: str,
-    role_header: str | None,
-    separator: str,
-    roles_config: dict[str, list[str]] | None = None,
-    camera_names: set[str] | None = None,
-) -> bool:
-    """Check if a WebSocket message is authorized.
-
-    Args:
-        topic: The message topic.
-        role_header: The HTTP_REMOTE_ROLE header value, or None.
-        separator: The role separator character from proxy config.
-        roles_config: The auth.roles mapping (role -> allowed cameras), used to
-            authorize camera-scoped commands for non-admin users.
-        camera_names: All configured camera names, used to resolve a role's
-            allowed cameras.
-
-    Returns:
-        True if authorized, False if blocked.
-    """
-    # Block IPC-only topics unconditionally
-    if topic in _WS_BLOCKED_TOPICS:
-        return False
-
-    # No role header: default to viewer (fail-closed)
-    roles = [r.strip() for r in role_header.split(separator)] if role_header else []
-
-    # Admin can send anything
-    if "admin" in roles:
-        return True
-
-    # Read-only topics any authenticated user can send
-    if topic in _WS_VIEWER_TOPICS:
-        return True
-
-    # Camera-scoped command like "<camera>/ptz": allow when the user's role(s)
-    # grant access to that camera.
-    parts = topic.split("/")
-    if (
-        roles_config is not None
-        and len(parts) == 2
-        and parts[1] in _WS_CAMERA_COMMAND_TOPICS
-    ):
-        allowed: set[str] = set()
-        # No role header maps to the default viewer role (e.g. proxy-only setups)
-        for role in roles or ["viewer"]:
-            allowed.update(
-                User.get_allowed_cameras(role, roles_config, camera_names or set())
-            )
-        return parts[0] in allowed
-
-    return False
-
 
 class WebSocket(WebSocket_):  # type: ignore[misc]
     def unhandled_error(self, error: Any) -> None:
@@ -157,9 +49,6 @@ class WebSocketClient(Communicator):
 
         class _WebSocketHandler(WebSocket):
             receiver = self._dispatcher
-            role_separator = self.config.proxy.separator or ","
-            roles_config = self.config.auth.roles
-            camera_names = set(self.config.cameras.keys())
 
             def received_message(self, message: WebSocket.received_message) -> None:  # type: ignore[name-defined]
                 try:
@@ -174,29 +63,11 @@ class WebSocketClient(Communicator):
                     )
                     return
 
-                topic = json_message["topic"]
-
-                # Authorization check (skip when environ is None — direct internal connection)
-                role_header = (
-                    self.environ.get("HTTP_REMOTE_ROLE") if self.environ else None
+                logger.debug(
+                    f"Publishing mqtt message from websockets at {json_message['topic']}."
                 )
-                if self.environ is not None and not _check_ws_authorization(
-                    topic,
-                    role_header,
-                    self.role_separator,
-                    self.roles_config,
-                    self.camera_names,
-                ):
-                    logger.warning(
-                        "Blocked unauthorized WebSocket message: topic=%s, role=%s",
-                        topic,
-                        role_header,
-                    )
-                    return
-
-                logger.debug(f"Publishing mqtt message from websockets at {topic}.")
                 self.receiver(
-                    topic,
+                    json_message["topic"],
                     json_message["payload"],
                 )
 

frigate/config/camera/genai.py

@@ -26,6 +26,3 @@ class GenAIConfig(FrigateBaseModel):
     provider_options: dict[str, Any] = Field(
         default={}, title="GenAI Provider extra options."
     )
-    runtime_options: dict[str, Any] = Field(
-        default={}, title="Options to pass during inference calls."
-    )

frigate/config/camera/onvif.py

@@ -72,7 +72,7 @@ class PtzAutotrackConfig(FrigateBaseModel):
 
 
 class OnvifConfig(FrigateBaseModel):
-    host: EnvString = Field(default="", title="Onvif Host")
+    host: str = Field(default="", title="Onvif Host")
     port: int = Field(default=8000, title="Onvif Port")
     user: Optional[EnvString] = Field(default=None, title="Onvif Username")
     password: Optional[EnvString] = Field(default=None, title="Onvif Password")

frigate/config/camera/record.py

@@ -9,7 +9,6 @@ from frigate.review.types import SeverityEnum
 from ..base import FrigateBaseModel
 
 __all__ = [
-    "ChaptersEnum",
     "RecordConfig",
     "RecordExportConfig",
     "RecordPreviewConfig",
@@ -67,19 +66,10 @@ class RecordPreviewConfig(FrigateBaseModel):
     )
 
 
-class ChaptersEnum(str, Enum):
-    none = "none"
-    recording_segments = "recording_segments"
-
-
 class RecordExportConfig(FrigateBaseModel):
     timelapse_args: str = Field(
         default=DEFAULT_TIME_LAPSE_FFMPEG_ARGS, title="Timelapse Args"
     )
-    chapters: ChaptersEnum = Field(
-        default=ChaptersEnum.none,
-        title="Chapter metadata to embed in exported recordings",
-    )
 
 
 class RecordConfig(FrigateBaseModel):

frigate/config/camera/review.py

@@ -108,13 +108,12 @@ class GenAIReviewConfig(FrigateBaseModel):
         default="""### Normal Activity Indicators (Level 0)
 - Known/verified people in any zone at any time
 - People with pets in residential areas
-- Routine residential vehicle access during daytime/evening (6 AM - 10 PM): entering, exiting, loading/unloading items — normal commute and travel patterns
 - 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)
-- **Checking or probing vehicle/building access**: trying handles without entering, peering through windows, examining multiple vehicles, or possessing break-in tools — 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
@@ -134,8 +133,8 @@ 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 probing access (trying handles without entering, checking multiple vehicles), taking items, climbing → Level 1
-   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service, routine vehicle access) → Level 0
+   - 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.""",

frigate/config/config.py

@@ -28,7 +28,6 @@ from frigate.util.builtin import (
     get_ffmpeg_arg_list,
 )
 from frigate.util.config import (
-    CURRENT_CONFIG_VERSION,
     StreamInfoRetriever,
     convert_area_to_pixels,
     find_config_file,
@@ -77,12 +76,11 @@ logger = logging.getLogger(__name__)
 
 yaml = YAML()
 
-DEFAULT_CONFIG = f"""
+DEFAULT_CONFIG = """
 mqtt:
   enabled: False
 
-cameras: {{}}  # No cameras defined, UI wizard should be used
-version: {CURRENT_CONFIG_VERSION}
+cameras: {}  # No cameras defined, UI wizard should be used
 """
 
 DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
@@ -662,13 +660,6 @@ class FrigateConfig(FrigateBaseModel):
             # generate zone contours
             if len(camera_config.zones) > 0:
                 for zone in camera_config.zones.values():
-                    if zone.filters:
-                        for object_name, filter_config in zone.filters.items():
-                            zone.filters[object_name] = RuntimeFilterConfig(
-                                frame_shape=camera_config.frame_shape,
-                                **filter_config.model_dump(exclude_unset=True),
-                            )
-
                     zone.generate_contour(camera_config.frame_shape)
 
             # Set live view stream if none is set
@@ -762,7 +753,8 @@ class FrigateConfig(FrigateBaseModel):
             if new_config and f.tell() == 0:
                 f.write(DEFAULT_CONFIG)
                 logger.info(
-                    "Created default config file, see the getting started docs for configuration: https://docs.frigate.video/guides/getting_started"
+                    "Created default config file, see the getting started docs \
+                    for configuration https://docs.frigate.video/guides/getting_started"
                 )
 
             f.seek(0)

frigate/config/env.py

@@ -24,10 +24,8 @@ EnvString = Annotated[str, AfterValidator(validate_env_string)]
 
 def validate_env_vars(v: dict[str, str], info: ValidationInfo) -> dict[str, str]:
     if isinstance(info.context, dict) and info.context.get("install", False):
-        for k, val in v.items():
-            os.environ[k] = val
-            if k.startswith("FRIGATE_"):
-                FRIGATE_ENV_VARS[k] = val
+        for k, v in v.items():
+            os.environ[k] = v
 
     return v
 

frigate/config/mqtt.py

@@ -13,7 +13,7 @@ __all__ = ["MqttConfig"]
 
 class MqttConfig(FrigateBaseModel):
     enabled: bool = Field(default=True, title="Enable MQTT Communication.")
-    host: EnvString = Field(default="", title="MQTT Host")
+    host: str = Field(default="", title="MQTT Host")
     port: int = Field(default=1883, title="MQTT Port")
     topic_prefix: str = Field(default="frigate", title="MQTT Topic Prefix")
     client_id: str = Field(default="frigate", title="MQTT Client ID")

frigate/const.py

@@ -77,9 +77,6 @@ FFMPEG_HWACCEL_RKMPP = "preset-rkmpp"
 FFMPEG_HWACCEL_AMF = "preset-amd-amf"
 FFMPEG_HVC1_ARGS = ["-tag:v", "hvc1"]
 
-# RKNN constants
-SUPPORTED_RK_SOCS = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
-
 # Regex constants
 
 REGEX_CAMERA_NAME = r"^[a-zA-Z0-9_-]+$"

frigate/data_processing/common/license_plate/mixin.py

@@ -374,9 +374,6 @@ class LicensePlateProcessingMixin:
                             combined_plate = re.sub(
                                 pattern, replacement, combined_plate
                             )
-                            logger.debug(
-                                f"{camera}: Processing replace rule: '{pattern}' -> '{replacement}', result: '{combined_plate}'"
-                            )
                     except re.error as e:
                         logger.warning(
                             f"{camera}: Invalid regex in replace_rules '{pattern}': {e}"
@@ -384,7 +381,7 @@ class LicensePlateProcessingMixin:
 
             if combined_plate != original_combined:
                 logger.debug(
-                    f"{camera}: All rules applied: '{original_combined}' -> '{combined_plate}'"
+                    f"{camera}: Rules applied: '{original_combined}' -> '{combined_plate}'"
                 )
 
             # Compute the combined area for qualifying boxes