Bump python-multipart from 0.0.12 to 0.0.20 in /docker/main

Bumps [python-multipart](https://github.com/Kludex/python-multipart) from 0.0.12 to 0.0.20.
- [Release notes](https://github.com/Kludex/python-multipart/releases)
- [Changelog](https://github.com/Kludex/python-multipart/blob/master/CHANGELOG.md)
- [Commits](https://github.com/Kludex/python-multipart/compare/0.0.12...0.0.20)

---
updated-dependencies:
- dependency-name: python-multipart
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

.cspell/frigate-dictionary.txt

@@ -108,8 +108,8 @@ imagestream
 imdecode
 imencode
 imread
+imutils
 imwrite
-inpoint
 interp
 iostat
 iotop
@@ -265,7 +265,6 @@ tensorrt
 tflite
 thresholded
 timelapse
-titlecase
 tmpfs
 tobytes
 toggleable

.github/DISCUSSION_TEMPLATE/camera-support.yml

@@ -73,7 +73,7 @@ body:
     attributes:
       label: Operating system
       options:
-        - Home Assistant OS
+        - HassOS
         - Debian
         - Other Linux
         - Proxmox
@@ -87,7 +87,7 @@ body:
     attributes:
       label: Install method
       options:
-        - Home Assistant Add-on
+        - HassOS Addon
         - Docker Compose
         - Docker CLI
         - Proxmox via Docker

.github/DISCUSSION_TEMPLATE/config-support.yml

@@ -59,7 +59,7 @@ body:
     attributes:
       label: Operating system
       options:
-        - Home Assistant OS
+        - HassOS
         - Debian
         - Other Linux
         - Proxmox
@@ -73,7 +73,7 @@ body:
     attributes:
       label: Install method
       options:
-        - Home Assistant Add-on
+        - HassOS Addon
         - Docker Compose
         - Docker CLI
         - Proxmox via Docker

.github/DISCUSSION_TEMPLATE/detector-support.yml

@@ -53,7 +53,7 @@ body:
     attributes:
       label: Install method
       options:
-        - Home Assistant Add-on
+        - HassOS Addon
         - Docker Compose
         - Docker CLI
         - Proxmox via Docker

.github/DISCUSSION_TEMPLATE/general-support.yml

@@ -73,7 +73,7 @@ body:
     attributes:
       label: Install method
       options:
-        - Home Assistant Add-on
+        - HassOS Addon
         - Docker Compose
         - Docker CLI
         - Proxmox via Docker

.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml

@@ -69,7 +69,7 @@ body:
     attributes:
       label: Install method
       options:
-        - Home Assistant Add-on
+        - HassOS Addon
         - Docker Compose
         - Docker CLI
         - Proxmox via Docker

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

@@ -6,7 +6,7 @@ body:
       value: |
         Use this form to submit a reproducible bug in Frigate or Frigate's UI.
 
-        Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
+        Before submitting your bug report, please [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
 
         **If you are unsure if your issue is actually a bug or not, please submit a support request first.**
 
@@ -14,7 +14,6 @@ body:
         [prs]: https://www.github.com/blakeblackshear/frigate/pulls
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
-        [ai]: https://docs.frigate.video
   - type: checkboxes
     attributes:
       label: Checklist
@@ -27,8 +26,6 @@ body:
         - label: I have tried a different browser to see if it is related to my browser.
           required: true
         - label: I have tried reproducing the issue in [incognito mode](https://www.computerworld.com/article/1719851/how-to-go-incognito-in-chrome-firefox-safari-and-edge.html) to rule out problems with any third party extensions or plugins I have installed.
-        - label: I have asked the AI at https://docs.frigate.video about my issue.
-          required: true
   - type: textarea
     id: description
     attributes:
@@ -100,7 +97,7 @@ body:
     attributes:
       label: Operating system
       options:
-        - Home Assistant OS
+        - HassOS
         - Debian
         - Other Linux
         - Proxmox
@@ -114,7 +111,7 @@ body:
     attributes:
       label: Install method
       options:
-        - Home Assistant Add-on
+        - HassOS Addon
         - Docker Compose
         - Docker CLI
     validations:

.github/pull_request_template.md

@@ -2,12 +2,12 @@
 <!--
   Thank you!
 
-  If you're introducing a new feature or significantly refactoring existing functionality,
-  we encourage you to start a discussion first. This helps ensure your idea aligns with
+  If you're introducing a new feature or significantly refactoring existing functionality, 
+  we encourage you to start a discussion first. This helps ensure your idea aligns with 
   Frigate's development goals.
 
   Describe what this pull request does and how it will benefit users of Frigate.
-  Please describe in detail any considerations, breaking changes, etc. that are
+  Please describe in detail any considerations, breaking changes, etc. that are 
   made in this pull request.
 -->
 
@@ -24,7 +24,7 @@
 ## Additional information
 
 - This PR fixes or closes issue: fixes #
-- This PR is related to issue:
+- This PR is related to issue: 
 
 ## Checklist
 
@@ -35,5 +35,4 @@
 - [ ] The code change is tested and works locally.
 - [ ] Local tests pass. **Your PR cannot be merged unless tests pass**
 - [ ] There is no commented out code in this PR.
-- [ ] UI changes including text have used i18n keys and have been added to the `en` locale.
 - [ ] The code has been formatted using Ruff (`ruff format frigate`)

.github/workflows/ci.yml

@@ -23,7 +23,7 @@ jobs:
     name: AMD64 Build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -41,13 +41,12 @@ jobs:
           target: frigate
           tags: ${{ steps.setup.outputs.image-name }}-amd64
           cache-from: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
-          cache-to: type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
   arm64_build:
     runs-on: ubuntu-22.04-arm
     name: ARM Build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -77,12 +76,42 @@ jobs:
             rpi.tags=${{ steps.setup.outputs.image-name }}-rpi
             *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-arm64,mode=max
+  jetson_jp5_build:
+    if: false
+    runs-on: ubuntu-22.04
+    name: Jetson Jetpack 5
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - name: Set up QEMU and Buildx
+        id: setup
+        uses: ./.github/actions/setup
+        with:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      - name: Build and push TensorRT (Jetson, Jetpack 5)
+        env:
+          ARCH: arm64
+          BASE_IMAGE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
+          SLIM_BASE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
+          TRT_BASE: nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime
+        uses: docker/bake-action@v6
+        with:
+          source: .
+          push: true
+          targets: tensorrt
+          files: docker/tensorrt/trt.hcl
+          set: |
+            tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt-jp5
+            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5
+            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-jp5,mode=max
   jetson_jp6_build:
     runs-on: ubuntu-22.04-arm
     name: Jetson Jetpack 6
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -113,7 +142,7 @@ jobs:
       - amd64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -132,8 +161,8 @@ jobs:
           files: docker/tensorrt/trt.hcl
           set: |
             tensorrt.tags=${{ steps.setup.outputs.image-name }}-tensorrt
-            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-tensorrt
-            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-tensorrt,mode=max
+            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64
+            *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
       - name: AMD/ROCm general build
         env:
           AMDGPU: gfx
@@ -147,7 +176,7 @@ jobs:
           set: |
             rocm.tags=${{ steps.setup.outputs.image-name }}-rocm
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-rocm,mode=max
-            *.cache-from=type=registry,ref=${{ steps.setup.outputs.cache-name }}-rocm
+            *.cache-from=type=gha
   arm64_extra_builds:
     runs-on: ubuntu-22.04-arm
     name: ARM Extra Build
@@ -155,7 +184,7 @@ jobs:
       - arm64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -173,31 +202,6 @@ jobs:
           set: |
             rk.tags=${{ steps.setup.outputs.image-name }}-rk
             *.cache-from=type=gha
-  synaptics_build:
-    runs-on: ubuntu-22.04-arm
-    name: Synaptics Build
-    needs:
-      - arm64_build
-    steps:
-      - name: Check out code
-        uses: actions/checkout@v5
-        with:
-          persist-credentials: false
-      - name: Set up QEMU and Buildx
-        id: setup
-        uses: ./.github/actions/setup
-        with:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      - name: Build and push Synaptics build
-        uses: docker/bake-action@v6
-        with:
-          source: .
-          push: true
-          targets: synaptics
-          files: docker/synaptics/synaptics.hcl
-          set: |
-            synaptics.tags=${{ steps.setup.outputs.image-name }}-synaptics
-            *.cache-from=type=gha
   # The majority of users running arm64 are rpi users, so the rpi
   # build should be the primary arm64 image
   assemble_default_build:
@@ -212,7 +216,7 @@ jobs:
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@184bdaa0721073962dff0199f1fb9940f07167d1
+        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
         with:
           registry: ghcr.io
           username: ${{ github.actor }}

.github/workflows/pull_request.yml

@@ -4,19 +4,43 @@ on:
   pull_request:
     paths-ignore:
       - "docs/**"
-      - ".github/*.yml"
-      - ".github/DISCUSSION_TEMPLATE/**"
-      - ".github/ISSUE_TEMPLATE/**"
+      - ".github/**"
 
 env:
   DEFAULT_PYTHON: 3.11
 
 jobs:
+  build_devcontainer:
+    runs-on: ubuntu-latest
+    name: Build Devcontainer
+    # The Dockerfile contains features that requires buildkit, and since the
+    # devcontainer cli uses docker-compose to build the image, the only way to
+    # ensure docker-compose uses buildkit is to explicitly enable it.
+    env:
+      DOCKER_BUILDKIT: "1"
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - uses: actions/setup-node@master
+        with:
+          node-version: 20.x
+      - name: Install devcontainer cli
+        run: npm install --global @devcontainers/cli
+      - name: Build devcontainer
+        run: devcontainer build --workspace-folder .
+      # It would be nice to also test the following commands, but for some
+      # reason they don't work even though in VS Code devcontainer works.
+      # - name: Start devcontainer
+      #   run: devcontainer up --workspace-folder .
+      # - name: Run devcontainer scripts
+      #   run: devcontainer run-user-commands --workspace-folder .
+
   web_lint:
     name: Web - Lint
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
         with:
           persist-credentials: false
       - uses: actions/setup-node@master
@@ -32,7 +56,7 @@ jobs:
     name: Web - Test
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
         with:
           persist-credentials: false
       - uses: actions/setup-node@master
@@ -52,7 +76,7 @@ jobs:
     name: Python Checks
     steps:
       - name: Check out the repository
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up Python ${{ env.DEFAULT_PYTHON }}
@@ -75,21 +99,16 @@ jobs:
     name: Python Tests
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
-      - uses: actions/setup-node@master
-        with:
-          node-version: 20.x
-      - name: Install devcontainer cli
-        run: npm install --global @devcontainers/cli
-      - name: Build devcontainer
-        env:
-          DOCKER_BUILDKIT: "1"
-        run: devcontainer build --workspace-folder .
-      - name: Start devcontainer
-        run: devcontainer up --workspace-folder .
-      - name: Run mypy in devcontainer
-        run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m mypy --config-file frigate/mypy.ini frigate"
-      - name: Run unit tests in devcontainer
-        run: devcontainer exec --workspace-folder . bash -lc "python3 -u -m unittest"
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Build
+        run: make
+      - name: Run mypy
+        run: docker run --rm --entrypoint=python3 frigate:latest -u -m mypy --config-file frigate/mypy.ini frigate
+      - name: Run tests
+        run: docker run --rm --entrypoint=python3 frigate:latest -u -m unittest

.github/workflows/release.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v4
         with:
           persist-credentials: false
       - id: lowercaseRepo
@@ -18,7 +18,7 @@ jobs:
         with:
           string: ${{ github.repository }}
       - name: Log in to the Container registry
-        uses: docker/login-action@184bdaa0721073962dff0199f1fb9940f07167d1
+        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
         with:
           registry: ghcr.io
           username: ${{ github.actor }}
@@ -39,14 +39,14 @@ jobs:
           STABLE_TAG=${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; do
+          for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l 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; do
+            for variant in standard-arm64 tensorrt tensorrt-jp5 tensorrt-jp6 rk h8l 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

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.17.0
+VERSION = 0.16.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
@@ -20,12 +20,6 @@ local: version
 		--tag frigate:latest \
 		--load
 
-debug: version
-	docker buildx build --target=frigate --file docker/main/Dockerfile . \
-	    --build-arg DEBUG=true \
-		--tag frigate:latest \
-		--load
-
 amd64:
 	docker buildx build --target=frigate --file docker/main/Dockerfile . \
 		--tag $(IMAGE_REPO):$(VERSION)-$(COMMIT_HASH) \

README.md

@@ -4,15 +4,9 @@
 
 # Frigate - NVR With Realtime Object Detection for IP Cameras
 
-<a href="https://hosted.weblate.org/engage/frigate-nvr/">
-<img src="https://hosted.weblate.org/widget/frigate-nvr/language-badge.svg" alt="Translation status" />
-</a>
-
-\[English\] | [简体中文](https://github.com/blakeblackshear/frigate/blob/dev/README_CN.md)
-
 A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
-Use of a GPU or AI accelerator such as a [Google Coral](https://coral.ai/products/) or [Hailo](https://hailo.ai/) is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
+Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
 
 - Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
@@ -36,33 +30,21 @@ If you would like to make a donation to support development, please use [Github
 ## Screenshots
 
 ### Live dashboard
-
 <div>
 <img width="800" alt="Live dashboard" src="https://github.com/blakeblackshear/frigate/assets/569905/5e713cb9-9db5-41dc-947a-6937c3bc376e">
 </div>
 
 ### Streamlined review workflow
-
 <div>
 <img width="800" alt="Streamlined review workflow" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
 </div>
 
 ### Multi-camera scrubbing
-
 <div>
 <img width="800" alt="Multi-camera scrubbing" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
 </div>
 
 ### Built-in mask and zone editor
-
 <div>
 <img width="800" alt="Multi-camera scrubbing" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
-
-## Translations
-
-We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support language translations. Contributions are always welcome.
-
-<a href="https://hosted.weblate.org/engage/frigate-nvr/">
-<img src="https://hosted.weblate.org/widget/frigate-nvr/multi-auto.svg" alt="Translation status" />
-</a>

README_CN.md

@@ -1,70 +0,0 @@
-<p align="center">
-  <img align="center" alt="logo" src="docs/static/img/frigate.png">
-</p>
-
-# Frigate - 一个具有实时目标检测的本地NVR
-
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\] 
-
-<a href="https://hosted.weblate.org/engage/frigate-nvr/-/zh_Hans/">
-<img src="https://hosted.weblate.org/widget/frigate-nvr/-/zh_Hans/svg-badge.svg" alt="翻译状态" />
-</a>
-
-一个完整的本地网络视频录像机（NVR），专为[Home Assistant](https://www.home-assistant.io)设计，具备AI物体检测功能。使用OpenCV和TensorFlow在本地为IP摄像头执行实时物体检测。
-
-强烈推荐使用GPU或者AI加速器（例如[Google Coral加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)）。它们的性能甚至超过目前的顶级CPU，并且可以以极低的耗电实现更优的性能。
-- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与Home Assistant紧密集成
-- 设计上通过仅在必要时和必要地点寻找物体，最大限度地减少资源使用并最大化性能
-- 大量利用多进程处理，强调实时性而非处理每一帧
-- 使用非常低开销的运动检测来确定运行物体检测的位置
-- 使用TensorFlow进行物体检测，运行在单独的进程中以达到最大FPS
-- 通过MQTT进行通信，便于集成到其他系统中
-- 根据检测到的物体设置保留时间进行视频录制
-- 24/7全天候录制
-- 通过RTSP重新流传输以减少摄像头的连接数
-- 支持WebRTC和MSE，实现低延迟的实时观看
-
-## 社区中文翻译文档
-
-你可以在这里查看文档 https://docs.frigate-cn.video
-
-## 赞助
-
-如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
-
-## 截图
-
-### 实时监控面板
-<div>
-<img width="800" alt="实时监控面板" src="https://github.com/blakeblackshear/frigate/assets/569905/5e713cb9-9db5-41dc-947a-6937c3bc376e">
-</div>
-
-### 简单的核查工作流程
-<div>
-<img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
-</div>
-
-### 多摄像头可按时间轴查看
-<div>
-<img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
-</div>
-
-### 内置遮罩和区域编辑器
-<div>
-<img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
-</div>
-
-
-## 翻译
-我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
-
-
-## 非官方中文讨论社区
-欢迎加入中文讨论QQ群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
-
-Bilibili：https://space.bilibili.com/3546894915602564
-
-
-## 中文社区赞助商
-[![EdgeOne](https://edgeone.ai/media/34fe3a45-492d-4ea4-ae5d-ea1087ca7b4b.png)](https://edgeone.ai/zh?from=github)
-本项目 CDN 加速及安全防护由 Tencent EdgeOne 赞助

benchmark.py

@@ -4,13 +4,13 @@ from statistics import mean
 
 import numpy as np
 
+import frigate.util as util
 from frigate.config import DetectorTypeEnum
-from frigate.object_detection.base import (
+from frigate.object_detection import (
     ObjectDetectProcess,
     RemoteObjectDetector,
     load_labels,
 )
-from frigate.util.process import FrigateProcess
 
 my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
 labels = load_labels("/labelmap.txt")
@@ -91,7 +91,7 @@ edgetpu_process_2 = ObjectDetectProcess(
 )
 
 for x in range(0, 10):
-    camera_process = FrigateProcess(
+    camera_process = util.Process(
         target=start, args=(x, 300, detection_queue, events[str(x)])
     )
     camera_process.daemon = True

docker-compose.yml

@@ -1,8 +1,8 @@
+version: "3"
 services:
   devcontainer:
     container_name: frigate-devcontainer
-    # Check host system's actual render/video/plugdev group IDs with 'getent group render', 'getent group video', and 'getent group plugdev'
-    # Must add these exact IDs in container's group_add section or OpenVINO GPU acceleration will fail
+    # add groups from host for render, plugdev, video
     group_add:
       - "109" # render
       - "110" # render
@@ -24,8 +24,8 @@ services:
     #                     capabilities: [gpu]
     environment:
       YOLO_MODELS: ""
-    # devices:
-      # - /dev/bus/usb:/dev/bus/usb # Uncomment for Google Coral USB
+    devices:
+      - /dev/bus/usb:/dev/bus/usb
       # - /dev/dri:/dev/dri # for intel hwaccel, needs to be updated for your hardware
     volumes:
       - .:/workspace/frigate:cached
@@ -33,10 +33,9 @@ services:
       - /etc/localtime:/etc/localtime:ro
       - ./config:/config
       - ./debug:/media/frigate
-     # - /dev/bus/usb:/dev/bus/usb # Uncomment for Google Coral USB
+      - /dev/bus/usb:/dev/bus/usb
   mqtt:
     container_name: mqtt
-    image: eclipse-mosquitto:2.0
-    command: mosquitto -c /mosquitto-no-auth.conf # enable no-auth mode
+    image: eclipse-mosquitto:1.6
     ports:
-      - "1883:1883"
+      - "1883:1883"

docker/hailo8l/user_installation.sh

@@ -4,7 +4,7 @@
 sudo apt-get update
 sudo apt-get install -y build-essential cmake git wget
 
-hailo_version="4.21.0"
+hailo_version="4.20.0"
 arch=$(uname -m)
 
 if [[ $arch == "x86_64" ]]; then

docker/main/Dockerfile

@@ -55,7 +55,7 @@ RUN --mount=type=tmpfs,target=/tmp --mount=type=tmpfs,target=/var/cache/apt \
 FROM scratch AS go2rtc
 ARG TARGETARCH
 WORKDIR /rootfs/usr/local/go2rtc/bin
-ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.10/go2rtc_linux_${TARGETARCH}" go2rtc
+ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.2/go2rtc_linux_${TARGETARCH}" go2rtc
 
 FROM wget AS tempio
 ARG TARGETARCH
@@ -78,9 +78,8 @@ COPY docker/main/requirements-ov.txt /requirements-ov.txt
 RUN apt-get -qq update \
     && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
     && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip" \
-    && pip3 install -r /requirements-ov.txt
+    && pip install -r /requirements-ov.txt
 
 # Get OpenVino Model
 RUN --mount=type=bind,source=docker/main/build_ov_model.py,target=/build_ov_model.py \
@@ -148,12 +147,11 @@ RUN --mount=type=bind,source=docker/main/install_s6_overlay.sh,target=/deps/inst
 FROM base AS wheels
 ARG DEBIAN_FRONTEND
 ARG TARGETARCH
-ARG DEBUG=false
 
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
     && apt-get -qq install -y \
-    apt-transport-https wget unzip \
+    apt-transport-https wget \
     && apt-get -qq update \
     && apt-get -qq install -y \
     python3.11 \
@@ -174,12 +172,9 @@ RUN apt-get -qq update \
 RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip"
 
 COPY docker/main/requirements.txt /requirements.txt
-COPY docker/main/requirements-dev.txt /requirements-dev.txt
-
 RUN pip3 install -r /requirements.txt
 
 # Build pysqlite3 from source
@@ -187,10 +182,7 @@ COPY docker/main/build_pysqlite3.sh /build_pysqlite3.sh
 RUN /build_pysqlite3.sh
 
 COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
-RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt && \
-    if [ "$DEBUG" = "true" ]; then \
-        pip3 wheel --wheel-dir=/wheels -r /requirements-dev.txt; \
-    fi
+RUN pip3 wheel --wheel-dir=/wheels -r /requirements-wheels.txt
 
 # Install HailoRT & Wheels
 RUN --mount=type=bind,source=docker/main/install_hailort.sh,target=/deps/install_hailort.sh \
@@ -212,7 +204,6 @@ COPY docker/main/rootfs/ /
 # Frigate deps (ffmpeg, python, nginx, go2rtc, s6-overlay, etc)
 FROM slim-base AS deps
 ARG TARGETARCH
-ARG BASE_IMAGE
 
 ARG DEBIAN_FRONTEND
 # http://stackoverflow.com/questions/48162574/ddg#49462622
@@ -231,15 +222,9 @@ ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1
 # Set OpenCV ffmpeg loglevel to fatal: https://ffmpeg.org/doxygen/trunk/log_8h.html
 ENV OPENCV_FFMPEG_LOGLEVEL=8
 
-# Set NumPy to ignore getlimits warning
-ENV PYTHONWARNINGS="ignore:::numpy.core.getlimits"
-
 # Set HailoRT to disable logging
 ENV HAILORT_LOGGER_PATH=NONE
 
-# TensorFlow error only
-ENV TF_CPP_MIN_LOG_LEVEL=3
-
 ENV PATH="/usr/local/go2rtc/bin:/usr/local/tempio/bin:/usr/local/nginx/sbin:${PATH}"
 
 # Install dependencies
@@ -250,16 +235,11 @@ ENV DEFAULT_FFMPEG_VERSION="7.0"
 ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:5.0"
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip"
 
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
     pip3 install -U /deps/wheels/*.whl
 
-# Install MemryX runtime (requires libgomp (OpenMP) in the final docker image)
-RUN --mount=type=bind,source=docker/main/install_memryx.sh,target=/deps/install_memryx.sh \
-    bash -c "bash /deps/install_memryx.sh"
-
 COPY --from=deps-rootfs / /
 
 RUN ldconfig
@@ -277,12 +257,12 @@ ENTRYPOINT ["/init"]
 CMD []
 
 HEALTHCHECK --start-period=300s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
-    CMD test -f /dev/shm/.frigate-is-stopping && exit 0; curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1
+    CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1
 
 # Frigate deps with Node.js and NPM for devcontainer
 FROM deps AS devcontainer
 
-# Do not start the actual Frigate service on devcontainer as it will be started by VS Code
+# Do not start the actual Frigate service on devcontainer as it will be started by VSCode
 # But start a fake service for simulating the logs
 COPY docker/main/fake_frigate_run /etc/s6-overlay/s6-rc.d/frigate/run
 

docker/main/build_nginx.sh

@@ -2,7 +2,7 @@
 
 set -euxo pipefail
 
-NGINX_VERSION="1.27.4"
+NGINX_VERSION="1.25.3"
 VOD_MODULE_VERSION="1.31"
 SECURE_TOKEN_MODULE_VERSION="1.5"
 SET_MISC_MODULE_VERSION="v0.33"

docker/main/build_pysqlite3.sh

@@ -2,25 +2,18 @@
 
 set -euxo pipefail
 
-SQLITE3_VERSION="3.46.1"
+SQLITE3_VERSION="96c92aba00c8375bc32fafcdf12429c58bd8aabfcadab6683e35bbb9cdebf19e" # 3.46.0
 PYSQLITE3_VERSION="0.5.3"
 
-# Fetch the pre-built sqlite amalgamation instead of building from source
+# Fetch the source code for the latest release of Sqlite.
 if [[ ! -d "sqlite" ]]; then
-  mkdir sqlite
-  cd sqlite
-  
-  # Download the pre-built amalgamation from sqlite.org
-  # For SQLite 3.46.1, the amalgamation version is 3460100
-  SQLITE_AMALGAMATION_VERSION="3460100"
-  
-  wget https://www.sqlite.org/2024/sqlite-amalgamation-${SQLITE_AMALGAMATION_VERSION}.zip -O sqlite-amalgamation.zip
-  unzip sqlite-amalgamation.zip
-  mv sqlite-amalgamation-${SQLITE_AMALGAMATION_VERSION}/* .
-  rmdir sqlite-amalgamation-${SQLITE_AMALGAMATION_VERSION}
-  rm sqlite-amalgamation.zip
-  
+  wget https://www.sqlite.org/src/tarball/sqlite.tar.gz?r=${SQLITE3_VERSION} -O sqlite.tar.gz
+  tar xzf sqlite.tar.gz
+  cd sqlite/
+  LIBS="-lm" ./configure --disable-tcl --enable-tempstore=always
+  make sqlite3.c
   cd ../
+  rm sqlite.tar.gz
 fi
 
 # Grab the pysqlite3 source code.

docker/main/install_deps.sh

@@ -19,8 +19,7 @@ apt-get -qq install --no-install-recommends -y \
     nethogs \
     libgl1 \
     libglib2.0-0 \
-    libusb-1.0.0 \
-    libgomp1  # memryx detector
+    libusb-1.0.0
 
 update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
@@ -32,18 +31,6 @@ unset DEBIAN_FRONTEND
 yes | dpkg -i /tmp/libedgetpu1-max.deb && export DEBIAN_FRONTEND=noninteractive
 rm /tmp/libedgetpu1-max.deb
 
-# install mesa-teflon-delegate from bookworm-backports
-# Only available for arm64 at the moment
-if [[ "${TARGETARCH}" == "arm64" ]]; then
-    if [[ "${BASE_IMAGE}" == *"nvcr.io/nvidia/tensorrt"* ]]; then
-        echo "Info: Skipping apt-get commands because BASE_IMAGE includes 'nvcr.io/nvidia/tensorrt' for arm64."
-    else
-        echo "deb http://deb.debian.org/debian bookworm-backports main" | tee /etc/apt/sources.list.d/bookworm-backbacks.list
-        apt-get -qq update
-        apt-get -qq install --no-install-recommends --no-install-suggests -y mesa-teflon-delegate/bookworm-backports
-    fi
-fi
-
 # ffmpeg -> amd64
 if [[ "${TARGETARCH}" == "amd64" ]]; then
     mkdir -p /usr/lib/ffmpeg/5.0
@@ -70,16 +57,9 @@ fi
 
 # arch specific packages
 if [[ "${TARGETARCH}" == "amd64" ]]; then
-  # Install non-free version of i965 driver
-  sed -i -E "/^Components: main$/s/main/main contrib non-free non-free-firmware/" "/etc/apt/sources.list.d/debian.sources" \
-      && apt-get -qq update \
-      && apt-get install --no-install-recommends --no-install-suggests -y i965-va-driver-shaders \
-      && sed -i -E "/^Components: main contrib non-free non-free-firmware$/s/main contrib non-free non-free-firmware/main/" "/etc/apt/sources.list.d/debian.sources" \
-      && apt-get update
-
     # install amd / intel-i965 driver packages
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-gpu-tools onevpl-tools \
+        i965-va-driver intel-gpu-tools onevpl-tools \
         libva-drm2 \
         mesa-va-drivers radeontop
 
@@ -91,33 +71,11 @@ if [[ "${TARGETARCH}" == "amd64" ]]; then
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
     apt-get -qq update
     apt-get -qq install --no-install-recommends --no-install-suggests -y \
-        intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2
-
-    apt-get -qq install -y ocl-icd-libopencl1
+        intel-opencl-icd=24.35.30872.31-996~22.04 intel-level-zero-gpu=1.3.29735.27-914~22.04 intel-media-va-driver-non-free=24.3.3-996~22.04 \
+        libmfx1=23.2.2-880~22.04 libmfxgen1=24.2.4-914~22.04 libvpl2=1:2.13.0.0-996~22.04
 
     rm -f /usr/share/keyrings/intel-graphics.gpg
     rm -f /etc/apt/sources.list.d/intel-gpu-jammy.list
-
-    # install legacy and standard intel icd and level-zero-gpu
-    # see https://github.com/intel/compute-runtime/blob/master/LEGACY_PLATFORMS.md for more info
-    # needed core package
-    wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/libigdgmm12_22.5.5_amd64.deb
-    dpkg -i libigdgmm12_22.5.5_amd64.deb
-    rm libigdgmm12_22.5.5_amd64.deb
-
-    # legacy packages
-    wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-opencl-icd-legacy1_24.35.30872.36_amd64.deb
-    wget https://github.com/intel/compute-runtime/releases/download/24.35.30872.36/intel-level-zero-gpu-legacy1_1.5.30872.36_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-opencl_1.0.17537.24_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.17537.24/intel-igc-core_1.0.17537.24_amd64.deb
-    # standard packages
-    wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/intel-opencl-icd_24.52.32224.5_amd64.deb
-    wget https://github.com/intel/compute-runtime/releases/download/24.52.32224.5/intel-level-zero-gpu_1.6.32224.5_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-opencl-2_2.5.6+18417_amd64.deb
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.5.6/intel-igc-core-2_2.5.6+18417_amd64.deb
-
-    dpkg -i *.deb
-    rm *.deb
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then

docker/main/install_hailort.sh

@@ -2,7 +2,7 @@
 
 set -euxo pipefail
 
-hailo_version="4.21.0"
+hailo_version="4.20.0"
 
 if [[ "${TARGETARCH}" == "amd64" ]]; then
     arch="x86_64"
@@ -10,5 +10,5 @@ elif [[ "${TARGETARCH}" == "arm64" ]]; then
     arch="aarch64"
 fi
 
-wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-debian12-${TARGETARCH}.tar.gz" | tar -C / -xzf -
+wget -qO- "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${TARGETARCH}.tar.gz" | tar -C / -xzf -
 wget -P /wheels/ "https://github.com/frigate-nvr/hailort/releases/download/v${hailo_version}/hailort-${hailo_version}-cp311-cp311-linux_${arch}.whl"

docker/main/install_memryx.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-set -e
-
-# Download the MxAccl for Frigate github release
-wget https://github.com/memryx/mx_accl_frigate/archive/refs/heads/main.zip -O /tmp/mxaccl.zip
-unzip /tmp/mxaccl.zip -d /tmp
-mv /tmp/mx_accl_frigate-main /opt/mx_accl_frigate
-rm /tmp/mxaccl.zip
-
-# Install Python dependencies
-pip3 install -r /opt/mx_accl_frigate/freeze
-
-# Link the Python package dynamically
-SITE_PACKAGES=$(python3 -c "import site; print(site.getsitepackages()[0])")
-ln -s /opt/mx_accl_frigate/memryx "$SITE_PACKAGES/memryx"
-
-# Copy architecture-specific shared libraries
-ARCH=$(uname -m)
-if [[ "$ARCH" == "x86_64" ]]; then
-    cp /opt/mx_accl_frigate/memryx/x86/libmemx.so* /usr/lib/x86_64-linux-gnu/
-    cp /opt/mx_accl_frigate/memryx/x86/libmx_accl.so* /usr/lib/x86_64-linux-gnu/
-elif [[ "$ARCH" == "aarch64" ]]; then
-    cp /opt/mx_accl_frigate/memryx/arm/libmemx.so* /usr/lib/aarch64-linux-gnu/
-    cp /opt/mx_accl_frigate/memryx/arm/libmx_accl.so* /usr/lib/aarch64-linux-gnu/
-else
-    echo "Unsupported architecture: $ARCH"
-    exit 1
-fi
-
-# Refresh linker cache
-ldconfig

docker/main/install_s6_overlay.sh

@@ -2,7 +2,7 @@
 
 set -euxo pipefail
 
-s6_version="3.2.1.0"
+s6_version="3.1.5.0"
 
 if [[ "${TARGETARCH}" == "amd64" ]]; then
     s6_arch="x86_64"

docker/main/requirements-dev.txt

@@ -1,4 +1 @@
 ruff
-
-# types
-types-peewee == 3.17.*

docker/main/requirements-wheels.txt

@@ -1,28 +1,25 @@
 aiofiles == 24.1.*
 click == 8.1.*
 # FastAPI
-aiohttp == 3.12.*
-starlette == 0.47.*
-starlette-context == 0.4.*
-fastapi[standard-no-fastapi-cloud-cli] == 0.116.*
-uvicorn == 0.35.*
+aiohttp == 3.11.3
+starlette == 0.41.2
+starlette-context == 0.3.6
+fastapi == 0.115.*
+uvicorn == 0.30.*
 slowapi == 0.1.*
-joserfc == 1.2.*
-cryptography == 44.0.*
-pathvalidate == 3.3.*
+imutils == 0.5.*
+joserfc == 1.0.*
+pathvalidate == 3.2.*
 markupsafe == 3.0.*
 python-multipart == 0.0.20
-# Classification Model Training
-tensorflow == 2.19.* ; platform_machine == 'aarch64'
-tensorflow-cpu == 2.19.* ; platform_machine == 'x86_64'
 # General
 mypy == 1.6.1
-onvif-zeep-async == 4.0.*
+onvif-zeep-async == 3.1.*
 paho-mqtt == 2.1.*
 pandas == 2.2.*
 peewee == 3.17.*
 peewee_migrate == 1.13.*
-psutil == 7.1.*
+psutil == 6.1.*
 pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
 pytz == 2025.*
@@ -31,24 +28,24 @@ ruamel.yaml == 0.18.*
 tzlocal == 5.2
 requests == 2.32.*
 types-requests == 2.32.*
-norfair == 2.3.*
+norfair == 2.2.*
 setproctitle == 1.3.*
 ws4py == 0.5.*
 unidecode == 1.3.*
-titlecase == 2.4.*
 # Image Manipulation
 numpy == 1.26.*
 opencv-python-headless == 4.11.0.*
 opencv-contrib-python == 4.11.0.*
-scipy == 1.16.*
+scipy == 1.14.*
 # OpenVino & ONNX
-openvino == 2025.3.*
-onnxruntime == 1.22.*
+openvino == 2024.4.*
+onnxruntime-openvino == 1.20.* ; platform_machine == 'x86_64'
+onnxruntime == 1.20.* ; platform_machine == 'aarch64'
 # Embeddings
 transformers == 4.45.*
 # Generative AI
 google-generativeai == 0.8.*
-ollama == 0.5.*
+ollama == 0.3.*
 openai == 1.65.*
 # push notifications
 py-vapid == 1.9.*
@@ -74,10 +71,3 @@ prometheus-client == 0.21.*
 # TFLite
 tflite_runtime @ https://github.com/frigate-nvr/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_x86_64.whl; platform_machine == 'x86_64'
 tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_aarch64.whl; platform_machine == 'aarch64'
-# audio transcription
-sherpa-onnx==1.12.*
-faster-whisper==1.1.*
-librosa==0.11.*
-soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*

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

@@ -10,7 +10,7 @@ echo "[INFO] Starting certsync..."
 
 lefile="/etc/letsencrypt/live/frigate/fullchain.pem"
 
-tls_enabled=`python3 /usr/local/nginx/get_listen_settings.py | jq -r .tls.enabled`
+tls_enabled=`python3 /usr/local/nginx/get_tls_settings.py | jq -r .enabled`
 
 while true
 do

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

@@ -4,16 +4,44 @@
 
 set -o errexit -o nounset -o pipefail
 
-# opt out of openvino telemetry
-if [ -e /usr/local/bin/opt_in_out ]; then
-  /usr/local/bin/opt_in_out --opt_out > /dev/null 2>&1
-fi
-
 # Logs should be sent to stdout so that s6 can collect them
 
 # Tell S6-Overlay not to restart this service
 s6-svc -O .
 
+function migrate_db_path() {
+    # Find config file in yaml or yml, but prefer yaml
+    local config_file="${CONFIG_FILE:-"/config/config.yml"}"
+    local config_file_yaml="${config_file//.yml/.yaml}"
+    if [[ -f "${config_file_yaml}" ]]; then
+        config_file="${config_file_yaml}"
+    elif [[ ! -f "${config_file}" ]]; then
+        # Frigate will create the config file on startup
+        return 0
+    fi
+    unset config_file_yaml
+
+    # Use yq to check if database.path is set
+    local user_db_path
+    user_db_path=$(yq eval '.database.path' "${config_file}")
+
+    if [[ "${user_db_path}" == "null" ]]; then
+        local previous_db_path="/media/frigate/frigate.db"
+        local new_db_dir="/config"
+        if [[ -f "${previous_db_path}" ]]; then
+            if mountpoint --quiet "${new_db_dir}"; then
+                # /config is a mount point, move the db
+                echo "[INFO] Moving db from '${previous_db_path}' to the '${new_db_dir}' dir..."
+                # Move all files that starts with frigate.db to the new directory
+                mv -vf "${previous_db_path}"* "${new_db_dir}"
+            else
+                echo "[ERROR] Trying to migrate the db path from '${previous_db_path}' to the '${new_db_dir}' dir, but '${new_db_dir}' is not a mountpoint, please mount the '${new_db_dir}' dir"
+                return 1
+            fi
+        fi
+    fi
+}
+
 function set_libva_version() {
     local ffmpeg_path
     ffmpeg_path=$(python3 /usr/local/ffmpeg/get_ffmpeg_path.py)
@@ -22,8 +50,8 @@ function set_libva_version() {
 }
 
 echo "[INFO] Preparing Frigate..."
+migrate_db_path
 set_libva_version
-
 echo "[INFO] Starting Frigate..."
 
 cd /opt/frigate || echo "[ERROR] Failed to change working directory to /opt/frigate"

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

@@ -50,38 +50,6 @@ function set_libva_version() {
     export LIBAVFORMAT_VERSION_MAJOR
 }
 
-function setup_homekit_config() {
-    local config_path="$1"
-
-    if [[ ! -f "${config_path}" ]]; then
-        echo "[INFO] Creating empty HomeKit config file..."
-        echo '{}' > "${config_path}"
-    fi
-
-    # Convert YAML to JSON for jq processing
-    local temp_json="/tmp/cache/homekit_config.json"
-    yq eval -o=json "${config_path}" > "${temp_json}" 2>/dev/null || {
-        echo "[WARNING] Failed to convert HomeKit config to JSON, skipping cleanup"
-        return 0
-    }
-
-    # Use jq to filter and keep only the homekit section
-    local cleaned_json="/tmp/cache/homekit_cleaned.json"
-    jq '
-        # Keep only the homekit section if it exists, otherwise empty object
-        if has("homekit") then {homekit: .homekit} else {homekit: {}} end
-    ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || echo '{"homekit": {}}' > "${cleaned_json}"
-
-    # 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}" "${cleaned_json}"
-}
-
 set_libva_version
 
 if [[ -f "/dev/shm/go2rtc.yaml" ]]; then
@@ -93,7 +61,7 @@ if [[ ! -f "/dev/shm/go2rtc.yaml" ]]; then
     echo "[INFO] Preparing new go2rtc config..."
 
     if [[ -n "${SUPERVISOR_TOKEN:-}" ]]; then
-        # Running as a Home Assistant Add-on, infer the IP address and port
+        # Running as a Home Assistant add-on, infer the IP address and port
         get_ip_and_port_from_supervisor
     fi
 
@@ -102,10 +70,6 @@ else
     echo "[WARNING] Unable to remove existing go2rtc config. Changes made to your frigate config file may not be recognized. Please remove the /dev/shm/go2rtc.yaml from your docker host manually."
 fi
 
-# HomeKit configuration persistence setup
-readonly homekit_config_path="/config/go2rtc_homekit.yml"
-setup_homekit_config "${homekit_config_path}"
-
 readonly config_path="/config"
 
 if [[ -x "${config_path}/go2rtc" ]]; then
@@ -118,7 +82,5 @@ fi
 echo "[INFO] Starting go2rtc..."
 
 # Replace the bash process with the go2rtc process, redirecting stderr to stdout
-# Use HomeKit config as the primary config so writebacks go there
-# The main config from Frigate will be loaded as a secondary config
 exec 2>&1
-exec "${binary_path}" -config="${homekit_config_path}" -config=/dev/shm/go2rtc.yaml
+exec "${binary_path}" -config=/dev/shm/go2rtc.yaml

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

@@ -79,13 +79,8 @@ if [ ! \( -f "$letsencrypt_path/privkey.pem" -a -f "$letsencrypt_path/fullchain.
         -keyout "$letsencrypt_path/privkey.pem" -out "$letsencrypt_path/fullchain.pem" 2>/dev/null
 fi
 
-# build templates for optional FRIGATE_BASE_PATH environment variable
-python3 /usr/local/nginx/get_base_path.py | \
-    tempio -template /usr/local/nginx/templates/base_path.gotmpl \
-       -out /usr/local/nginx/conf/base_path.conf
-
 # build templates for optional TLS support
-python3 /usr/local/nginx/get_listen_settings.py | \
+python3 /usr/local/nginx/get_tls_settings.py | \
     tempio  -template /usr/local/nginx/templates/listen.gotmpl \
             -out /usr/local/nginx/conf/listen.conf
 

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

@@ -1,146 +0,0 @@
-#!/command/with-contenv bash
-# shellcheck shell=bash
-# Do preparation tasks before starting the main services
-
-set -o errexit -o nounset -o pipefail
-
-function migrate_addon_config_dir() {
-    local home_assistant_config_dir="/homeassistant"
-
-    if ! mountpoint --quiet "${home_assistant_config_dir}"; then
-        # Not running as a Home Assistant Add-on
-        return 0
-    fi
-
-    local config_dir="/config"
-    local new_config_file="${config_dir}/config.yml"
-    local new_config_file_yaml="${new_config_file//.yml/.yaml}"
-    if [[ -f "${new_config_file_yaml}" || -f "${new_config_file}" ]]; then
-        # Already migrated
-        return 0
-    fi
-
-    local old_config_file="${home_assistant_config_dir}/frigate.yml"
-    local old_config_file_yaml="${old_config_file//.yml/.yaml}"
-    if [[ -f "${old_config_file}" ]]; then
-        :
-    elif [[ -f "${old_config_file_yaml}" ]]; then
-        old_config_file="${old_config_file_yaml}"
-        new_config_file="${new_config_file_yaml}"
-    else
-        # Nothing to migrate
-        return 0
-    fi
-    unset old_config_file_yaml new_config_file_yaml
-
-    echo "[INFO] Starting migration from Home Assistant config dir to Add-on config dir..." >&2
-
-    local db_path
-    db_path=$(yq -r '.database.path' "${old_config_file}")
-    if [[ "${db_path}" == "null" ]]; then
-        db_path="${config_dir}/frigate.db"
-    fi
-    if [[ "${db_path}" == "${config_dir}/"* ]]; then
-        # replace /config/ prefix with /homeassistant/
-        local old_db_path="${home_assistant_config_dir}/${db_path:8}"
-
-        if [[ -f "${old_db_path}" ]]; then
-            local new_db_dir
-            new_db_dir="$(dirname "${db_path}")"
-            echo "[INFO] Migrating database from '${old_db_path}' to '${new_db_dir}' dir..." >&2
-            mkdir -vp "${new_db_dir}"
-            mv -vf "${old_db_path}" "${new_db_dir}"
-            local db_file
-            for db_file in "${old_db_path}"-shm "${old_db_path}"-wal; do
-                if [[ -f "${db_file}" ]]; then
-                    mv -vf "${db_file}" "${new_db_dir}"
-                fi
-            done
-            unset db_file
-        fi
-    fi
-
-    local config_entry
-    for config_entry in .model.path .model.labelmap_path .ffmpeg.path .mqtt.tls_ca_certs .mqtt.tls_client_cert .mqtt.tls_client_key; do
-        local config_entry_path
-        config_entry_path=$(yq -r "${config_entry}" "${old_config_file}")
-        if [[ "${config_entry_path}" == "${config_dir}/"* ]]; then
-            # replace /config/ prefix with /homeassistant/
-            local old_config_entry_path="${home_assistant_config_dir}/${config_entry_path:8}"
-
-            if [[ -f "${old_config_entry_path}" ]]; then
-                local new_config_entry_entry
-                new_config_entry_entry="$(dirname "${config_entry_path}")"
-                echo "[INFO] Migrating ${config_entry} from '${old_config_entry_path}' to '${config_entry_path}'..." >&2
-                mkdir -vp "${new_config_entry_entry}"
-                mv -vf "${old_config_entry_path}" "${config_entry_path}"
-            fi
-        fi
-    done
-
-    local old_model_cache_path="${home_assistant_config_dir}/model_cache"
-    if [[ -d "${old_model_cache_path}" ]]; then
-        echo "[INFO] Migrating '${old_model_cache_path}' to '${config_dir}'..." >&2
-        mv -f "${old_model_cache_path}" "${config_dir}"
-    fi
-
-    echo "[INFO] Migrating other files from '${home_assistant_config_dir}' to '${config_dir}'..." >&2
-    local file
-    for file in .exports .jwt_secret .timeline .vacuum go2rtc; do
-        file="${home_assistant_config_dir}/${file}"
-        if [[ -f "${file}" ]]; then
-            mv -vf "${file}" "${config_dir}"
-        fi
-    done
-
-    echo "[INFO] Migrating config file from '${old_config_file}' to '${new_config_file}'..." >&2
-    mv -vf "${old_config_file}" "${new_config_file}"
-
-    echo "[INFO] Migration from Home Assistant config dir to Add-on config dir completed." >&2
-}
-
-function migrate_db_from_media_to_config() {
-    # Find config file in yml or yaml, but prefer yml
-    local config_file="${CONFIG_FILE:-"/config/config.yml"}"
-    local config_file_yaml="${config_file//.yml/.yaml}"
-    if [[ -f "${config_file}" ]]; then
-        :
-    elif [[ -f "${config_file_yaml}" ]]; then
-        config_file="${config_file_yaml}"
-    else
-        # Frigate will create the config file on startup
-        return 0
-    fi
-    unset config_file_yaml
-
-    local user_db_path
-    user_db_path=$(yq -r '.database.path' "${config_file}")
-    if [[ "${user_db_path}" == "null" ]]; then
-        local old_db_path="/media/frigate/frigate.db"
-        local new_db_dir="/config"
-        if [[ -f "${old_db_path}" ]]; then
-            echo "[INFO] Migrating database from '${old_db_path}' to '${new_db_dir}' dir..." >&2
-            if mountpoint --quiet "${new_db_dir}"; then
-                # /config is a mount point, move the db
-                mv -vf "${old_db_path}" "${new_db_dir}"
-                local db_file
-                for db_file in "${old_db_path}"-shm "${old_db_path}"-wal; do
-                    if [[ -f "${db_file}" ]]; then
-                        mv -vf "${db_file}" "${new_db_dir}"
-                    fi
-                done
-                unset db_file
-            else
-                echo "[ERROR] Trying to migrate the database path from '${old_db_path}' to '${new_db_dir}' dir, but '${new_db_dir}' is not a mountpoint, please mount the '${new_db_dir}' dir" >&2
-                return 1
-            fi
-        fi
-    fi
-}
-
-# remove leftover from last run, not normally needed, but just in case
-# used by the docker healthcheck
-rm -f /dev/shm/.frigate-is-stopping
-
-migrate_addon_config_dir
-migrate_db_from_media_to_config

docker/main/rootfs/etc/s6-overlay/s6-rc.d/prepare/type

@@ -1 +0,0 @@
-oneshot

docker/main/rootfs/etc/s6-overlay/s6-rc.d/prepare/up

@@ -1 +0,0 @@
-/etc/s6-overlay/s6-rc.d/prepare/run

docker/main/rootfs/usr/local/ffmpeg/get_ffmpeg_path.py

@@ -1,6 +1,6 @@
 import json
+import os
 import sys
-from typing import Any
 
 from ruamel.yaml import YAML
 
@@ -9,24 +9,28 @@ from frigate.const import (
     DEFAULT_FFMPEG_VERSION,
     INCLUDED_FFMPEG_VERSIONS,
 )
-from frigate.util.config import find_config_file
 
 sys.path.remove("/opt/frigate")
 
 yaml = YAML()
 
-config_file = find_config_file()
+config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
+
+# Check if we can use .yaml instead of .yml
+config_file_yaml = config_file.replace(".yml", ".yaml")
+if os.path.isfile(config_file_yaml):
+    config_file = config_file_yaml
 
 try:
     with open(config_file) as f:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, Any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.load(raw_config)
     elif config_file.endswith(".json"):
-        config: dict[str, Any] = json.loads(raw_config)
+        config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:
-    config: dict[str, Any] = {}
+    config: dict[str, any] = {}
 
 path = config.get("ffmpeg", {}).get("path", "default")
 if path == "default":

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

@@ -4,7 +4,6 @@ import json
 import os
 import sys
 from pathlib import Path
-from typing import Any
 
 from ruamel.yaml import YAML
 
@@ -16,7 +15,6 @@ from frigate.const import (
     LIBAVFORMAT_VERSION_MAJOR,
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
-from frigate.util.config import find_config_file
 
 sys.path.remove("/opt/frigate")
 
@@ -31,20 +29,25 @@ if os.path.isdir("/run/secrets"):
                 Path(os.path.join("/run/secrets", secret_file)).read_text().strip()
             )
 
-config_file = find_config_file()
+config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
+
+# Check if we can use .yaml instead of .yml
+config_file_yaml = config_file.replace(".yml", ".yaml")
+if os.path.isfile(config_file_yaml):
+    config_file = config_file_yaml
 
 try:
     with open(config_file) as f:
         raw_config = f.read()
 
     if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, Any] = yaml.load(raw_config)
+        config: dict[str, any] = yaml.load(raw_config)
     elif config_file.endswith(".json"):
-        config: dict[str, Any] = json.loads(raw_config)
+        config: dict[str, any] = json.loads(raw_config)
 except FileNotFoundError:
-    config: dict[str, Any] = {}
+    config: dict[str, any] = {}
 
-go2rtc_config: dict[str, Any] = config.get("go2rtc", {})
+go2rtc_config: dict[str, any] = config.get("go2rtc", {})
 
 # Need to enable CORS for go2rtc so the frigate integration / card work automatically
 if go2rtc_config.get("api") is None:
@@ -54,7 +57,7 @@ elif go2rtc_config["api"].get("origin") is None:
 
 # Need to set default location for HA config
 if go2rtc_config.get("hass") is None:
-    go2rtc_config["hass"] = {"config": "/homeassistant"}
+    go2rtc_config["hass"] = {"config": "/config"}
 
 # we want to ensure that logs are easy to read
 if go2rtc_config.get("log") is None:
@@ -66,6 +69,10 @@ elif go2rtc_config["log"].get("format") is None:
 if go2rtc_config.get("webrtc") is None:
     go2rtc_config["webrtc"] = {}
 
+# go2rtc should listen on 8555 tcp & udp by default
+if go2rtc_config["webrtc"].get("listen") is None:
+    go2rtc_config["webrtc"]["listen"] = ":8555"
+
 if go2rtc_config["webrtc"].get("candidates") is None:
     default_candidates = []
     # use internal candidate if it was discovered when running through the add-on
@@ -77,15 +84,33 @@ if go2rtc_config["webrtc"].get("candidates") is None:
 
     go2rtc_config["webrtc"]["candidates"] = default_candidates
 
-if go2rtc_config.get("rtsp", {}).get("username") is not None:
-    go2rtc_config["rtsp"]["username"] = go2rtc_config["rtsp"]["username"].format(
-        **FRIGATE_ENV_VARS
-    )
+# This prevents WebRTC from attempting to establish a connection to the internal
+# docker IPs which are not accessible from outside the container itself and just
+# wastes time during negotiation. Note that this is only necessary because
+# Frigate container doesn't run in host network mode.
+if go2rtc_config["webrtc"].get("filter") is None:
+    go2rtc_config["webrtc"]["filter"] = {"candidates": []}
+elif go2rtc_config["webrtc"]["filter"].get("candidates") is None:
+    go2rtc_config["webrtc"]["filter"]["candidates"] = []
 
-if go2rtc_config.get("rtsp", {}).get("password") is not None:
-    go2rtc_config["rtsp"]["password"] = go2rtc_config["rtsp"]["password"].format(
-        **FRIGATE_ENV_VARS
-    )
+# sets default RTSP response to be equivalent to ?video=h264,h265&audio=aac
+# this means user does not need to specify audio codec when using restream
+# as source for frigate and the integration supports HLS playback
+if go2rtc_config.get("rtsp") is None:
+    go2rtc_config["rtsp"] = {"default_query": "mp4"}
+else:
+    if go2rtc_config["rtsp"].get("default_query") is None:
+        go2rtc_config["rtsp"]["default_query"] = "mp4"
+
+    if go2rtc_config["rtsp"].get("username") is not None:
+        go2rtc_config["rtsp"]["username"] = go2rtc_config["rtsp"]["username"].format(
+            **FRIGATE_ENV_VARS
+        )
+
+    if go2rtc_config["rtsp"].get("password") is not None:
+        go2rtc_config["rtsp"]["password"] = go2rtc_config["rtsp"]["password"].format(
+            **FRIGATE_ENV_VARS
+        )
 
 # ensure ffmpeg path is set correctly
 path = config.get("ffmpeg", {}).get("path", "default")
@@ -103,7 +128,7 @@ elif go2rtc_config["ffmpeg"].get("bin") is None:
 
 # need to replace ffmpeg command when using ffmpeg4
 if LIBAVFORMAT_VERSION_MAJOR < 59:
-    rtsp_args = "-fflags nobuffer -flags low_delay -stimeout 10000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
+    rtsp_args = "-fflags nobuffer -flags low_delay -stimeout 5000000 -user_agent go2rtc/ffmpeg -rtsp_transport tcp -i {input}"
     if go2rtc_config.get("ffmpeg") is None:
         go2rtc_config["ffmpeg"] = {"rtsp": rtsp_args}
     elif go2rtc_config["ffmpeg"].get("rtsp") is None:
@@ -135,7 +160,7 @@ for name in go2rtc_config.get("streams", {}):
 
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
-    birdseye: dict[str, Any] = config.get("birdseye")
+    birdseye: dict[str, any] = config.get("birdseye")
 
     input = f"-f rawvideo -pix_fmt yuv420p -video_size {birdseye.get('width', 1280)}x{birdseye.get('height', 720)} -r 10 -i {BIRDSEYE_PIPE}"
     ffmpeg_cmd = f"exec:{parse_preset_hardware_acceleration_encode(ffmpeg_path, config.get('ffmpeg', {}).get('hwaccel_args', ''), input, '-rtsp_transport tcp -f rtsp {output}')}"

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

@@ -17,9 +17,7 @@ http {
 
     log_format main '$remote_addr - $remote_user [$time_local] "$request" '
                         '$status $body_bytes_sent "$http_referer" '
-                        '"$http_user_agent" "$http_x_forwarded_for" '
-                        'request_time="$request_time" upstream_response_time="$upstream_response_time"';
-
+                        '"$http_user_agent" "$http_x_forwarded_for"';
 
     access_log /dev/stdout main;
 
@@ -32,7 +30,7 @@ http {
 
     gzip on;
     gzip_comp_level 6;
-    gzip_types text/plain text/css application/json application/x-javascript application/javascript text/javascript image/svg+xml image/x-icon image/bmp;
+    gzip_types text/plain text/css application/json application/x-javascript application/javascript text/javascript image/svg+xml image/x-icon image/bmp image/png image/gif image/jpeg image/jpg;
     gzip_proxied no-cache no-store private expired auth;
     gzip_vary on;
 
@@ -84,7 +82,7 @@ http {
         aio on;
 
         # file upload size
-        client_max_body_size 20M;
+        client_max_body_size 10M;
 
         # https://github.com/kaltura/nginx-vod-module#vod_open_file_thread_pool
         vod_open_file_thread_pool default;
@@ -98,7 +96,6 @@ http {
         gzip_types application/vnd.apple.mpegurl;
 
         include auth_location.conf;
-        include base_path.conf;
 
         location /vod/ {
             include auth_request.conf;
@@ -302,29 +299,11 @@ http {
                 add_header Cache-Control "public";
             }
 
-            location /locales/ {
-                access_log off;
-                add_header Cache-Control "public";
-            }
-
-            location ~ ^/.*-([A-Za-z0-9]+)\.webmanifest$ {
-                access_log off;
-                expires 1y;
-                add_header Cache-Control "public";
-                default_type application/json;
-                proxy_set_header Accept-Encoding "";
-                sub_filter_once off;
-                sub_filter_types application/json;
-                sub_filter '"start_url": "/BASE_PATH/"' '"start_url" : "$http_x_ingress_path/"';
-                sub_filter '"src": "/BASE_PATH/' '"src": "$http_x_ingress_path/';
-            }
-
             sub_filter 'href="/BASE_PATH/' 'href="$http_x_ingress_path/';
             sub_filter 'url(/BASE_PATH/' 'url($http_x_ingress_path/';
             sub_filter '"/BASE_PATH/dist/' '"$http_x_ingress_path/dist/';
             sub_filter '"/BASE_PATH/js/' '"$http_x_ingress_path/js/';
             sub_filter '"/BASE_PATH/assets/' '"$http_x_ingress_path/assets/';
-            sub_filter '"/BASE_PATH/locales/' '"$http_x_ingress_path/locales/';
             sub_filter '"/BASE_PATH/monacoeditorwork/' '"$http_x_ingress_path/assets/';
             sub_filter 'return"/BASE_PATH/"' 'return window.baseUrl';
             sub_filter '<body>' '<body><script>window.baseUrl="$http_x_ingress_path/";</script>';

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

@@ -1,11 +0,0 @@
-"""Prints the base path as json to stdout."""
-
-import json
-import os
-from typing import Any
-
-base_path = os.environ.get("FRIGATE_BASE_PATH", "")
-
-result: dict[str, Any] = {"base_path": base_path}
-
-print(json.dumps(result))

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

@@ -1,35 +0,0 @@
-"""Prints the tls config as json to stdout."""
-
-import json
-import sys
-from typing import Any
-
-from ruamel.yaml import YAML
-
-sys.path.insert(0, "/opt/frigate")
-from frigate.util.config import find_config_file
-
-sys.path.remove("/opt/frigate")
-
-yaml = YAML()
-
-config_file = find_config_file()
-
-try:
-    with open(config_file) as f:
-        raw_config = f.read()
-
-    if config_file.endswith((".yaml", ".yml")):
-        config: dict[str, Any] = yaml.load(raw_config)
-    elif config_file.endswith(".json"):
-        config: dict[str, Any] = json.loads(raw_config)
-except FileNotFoundError:
-    config: dict[str, Any] = {}
-
-tls_config: dict[str, any] = config.get("tls", {"enabled": True})
-networking_config = config.get("networking", {})
-ipv6_config = networking_config.get("ipv6", {"enabled": False})
-
-output = {"tls": tls_config, "ipv6": ipv6_config}
-
-print(json.dumps(output))

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

@@ -0,0 +1,30 @@
+"""Prints the tls config as json to stdout."""
+
+import json
+import os
+
+from ruamel.yaml import YAML
+
+yaml = YAML()
+
+config_file = os.environ.get("CONFIG_FILE", "/config/config.yml")
+
+# Check if we can use .yaml instead of .yml
+config_file_yaml = config_file.replace(".yml", ".yaml")
+if os.path.isfile(config_file_yaml):
+    config_file = config_file_yaml
+
+try:
+    with open(config_file) as f:
+        raw_config = f.read()
+
+    if config_file.endswith((".yaml", ".yml")):
+        config: dict[str, any] = yaml.load(raw_config)
+    elif config_file.endswith(".json"):
+        config: dict[str, any] = json.loads(raw_config)
+except FileNotFoundError:
+    config: dict[str, any] = {}
+
+tls_config: dict[str, any] = config.get("tls", {"enabled": True})
+
+print(json.dumps(tls_config))

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

@@ -1,19 +0,0 @@
-{{ if .base_path }}
-location = {{ .base_path }} {
-    return 302 {{ .base_path }}/;
-}
-
-location ^~ {{ .base_path }}/ {
-    # remove base_url from the path before passing upstream
-    rewrite ^{{ .base_path }}/(.*) /$1 break;
-
-    proxy_pass $scheme://127.0.0.1:8971;
-    proxy_http_version 1.1;
-    proxy_set_header Upgrade $http_upgrade;
-    proxy_set_header Connection "upgrade";
-    proxy_set_header Host $host;
-    proxy_set_header X-Ingress-Path {{ .base_path }};
-
-    access_log off;
-}
-{{ end }}

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

@@ -1,45 +1,33 @@
-
-# Internal (IPv4 always; IPv6 optional)
+# intended for internal traffic, not protected by auth
 listen 5000;
-{{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:5000;{{ end }}{{ end }}
-
 
+{{ if not .enabled }}
 # intended for external traffic, protected by auth
-{{ if .tls }}
-    {{ if .tls.enabled }}
-        # external HTTPS (IPv4 always; IPv6 optional)
-        listen 8971 ssl;
-        {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971 ssl;{{ end }}{{ end }}
-
-        ssl_certificate /etc/letsencrypt/live/frigate/fullchain.pem;
-        ssl_certificate_key /etc/letsencrypt/live/frigate/privkey.pem;
-
-        # generated 2024-06-01, Mozilla Guideline v5.7, nginx 1.25.3, OpenSSL 1.1.1w, modern configuration, no OCSP
-        # https://ssl-config.mozilla.org/#server=nginx&version=1.25.3&config=modern&openssl=1.1.1w&ocsp=false&guideline=5.7
-        ssl_session_timeout 1d;
-        ssl_session_cache shared:MozSSL:10m;  # about 40000 sessions
-        ssl_session_tickets off;
-
-        # modern configuration
-        ssl_protocols TLSv1.3;
-        ssl_prefer_server_ciphers off;
-
-        # HSTS (ngx_http_headers_module is required) (63072000 seconds)
-        add_header Strict-Transport-Security "max-age=63072000" always;
-
-        # ACME challenge location
-        location /.well-known/acme-challenge/ {
-                default_type "text/plain";
-                root /etc/letsencrypt/www;
-        }
-    {{ else }}
-        # external HTTP (IPv4 always; IPv6 optional)
-        listen 8971;
-        {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971;{{ end }}{{ end }}
-    {{ end }}
+listen 8971;
 {{ else }}
-    # (No tls section) default to HTTP (IPv4 always; IPv6 optional)
-    listen 8971;
-    {{ if .ipv6 }}{{ if .ipv6.enabled }}listen [::]:8971;{{ end }}{{ end }}
+# intended for external traffic, protected by auth
+listen 8971 ssl;
+
+ssl_certificate /etc/letsencrypt/live/frigate/fullchain.pem;
+ssl_certificate_key /etc/letsencrypt/live/frigate/privkey.pem;
+
+# generated 2024-06-01, Mozilla Guideline v5.7, nginx 1.25.3, OpenSSL 1.1.1w, modern configuration, no OCSP
+# https://ssl-config.mozilla.org/#server=nginx&version=1.25.3&config=modern&openssl=1.1.1w&ocsp=false&guideline=5.7
+ssl_session_timeout 1d;
+ssl_session_cache shared:MozSSL:10m;  # about 40000 sessions
+ssl_session_tickets off;
+
+# modern configuration
+ssl_protocols TLSv1.3;
+ssl_prefer_server_ciphers off;
+
+# HSTS (ngx_http_headers_module is required) (63072000 seconds)
+add_header Strict-Transport-Security "max-age=63072000" always;
+
+# ACME challenge location
+location /.well-known/acme-challenge/ {
+    default_type "text/plain";
+    root /etc/letsencrypt/www;
+}
 {{ end }}
 

docker/memryx/user_installation.sh

@@ -1,47 +0,0 @@
-#!/bin/bash
-set -e  # Exit immediately if any command fails
-set -o pipefail
-
-echo "Starting MemryX driver and runtime installation..."
-
-# Detect architecture
-arch=$(uname -m)
-
-# Purge existing packages and repo
-echo "Removing old MemryX installations..."
-# Remove any holds on MemryX packages (if they exist)
-sudo apt-mark unhold memx-* mxa-manager || true
-sudo apt purge -y memx-* mxa-manager || true
-sudo rm -f /etc/apt/sources.list.d/memryx.list /etc/apt/trusted.gpg.d/memryx.asc
-
-# Install kernel headers
-echo "Installing kernel headers for: $(uname -r)"
-sudo apt update
-sudo apt install -y dkms linux-headers-$(uname -r)
-
-# Add MemryX key and repo
-echo "Adding MemryX GPG key and repository..."
-wget -qO- https://developer.memryx.com/deb/memryx.asc | sudo tee /etc/apt/trusted.gpg.d/memryx.asc >/dev/null
-echo 'deb https://developer.memryx.com/deb stable main' | sudo tee /etc/apt/sources.list.d/memryx.list >/dev/null
-
-# Update and install memx-drivers
-echo "Installing memx-drivers..."
-sudo apt update
-sudo apt install -y memx-drivers
-
-# ARM-specific board setup
-if [[ "$arch" == "aarch64" || "$arch" == "arm64" ]]; then
-    echo "Running ARM board setup..."
-    sudo mx_arm_setup
-fi
-
-echo -e "\n\n\033[1;31mYOU MUST RESTART YOUR COMPUTER NOW\033[0m\n\n"
-
-# Install other runtime packages
-packages=("memx-accl" "mxa-manager")
-for pkg in "${packages[@]}"; do
-    echo "Installing $pkg..."
-    sudo apt install -y "$pkg"
-done
-
-echo "MemryX installation complete!"

docker/rockchip/Dockerfile

@@ -11,10 +11,8 @@ COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
 COPY docker/rockchip/requirements-wheels-rk.txt /requirements-wheels-rk.txt
 RUN sed -i "/https:\/\//d" /requirements-wheels.txt
 RUN sed -i "/onnxruntime/d" /requirements-wheels.txt
-RUN sed -i '/\[.*\]/d' /requirements-wheels.txt \
-    && pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
+RUN pip3 wheel --wheel-dir=/rk-wheels -c /requirements-wheels.txt -r /requirements-wheels-rk.txt
 RUN rm -rf /rk-wheels/opencv_python-*
-RUN rm -rf /rk-wheels/torch-*
 
 FROM deps AS rk-frigate
 ARG TARGETARCH
@@ -28,11 +26,9 @@ COPY --from=rootfs / /
 COPY docker/rockchip/COCO /COCO
 COPY docker/rockchip/conv2rknn.py /opt/conv2rknn.py
 
-ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/librknnrt.so /usr/lib/
+ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.0/librknnrt.so /usr/lib/
 
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-11/ffmpeg /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-11/ffprobe /usr/lib/ffmpeg/6.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/7.1-1/ffmpeg /usr/lib/ffmpeg/7.0/bin/
-ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/7.1-1/ffprobe /usr/lib/ffmpeg/7.0/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffmpeg /usr/lib/ffmpeg/6.0/bin/
+ADD --chmod=111 https://github.com/MarcA711/Rockchip-FFmpeg-Builds/releases/download/6.1-7/ffprobe /usr/lib/ffmpeg/6.0/bin/
 ENV DEFAULT_FFMPEG_VERSION="6.0"
 ENV INCLUDED_FFMPEG_VERSIONS="${DEFAULT_FFMPEG_VERSION}:${INCLUDED_FFMPEG_VERSIONS}"

docker/rockchip/conv2rknn.py

@@ -14,7 +14,7 @@ try:
     with open("/config/conv2rknn.yaml", "r") as config_file:
         configuration = yaml.safe_load(config_file)
 except FileNotFoundError:
-    raise Exception("Please place a config file at /config/conv2rknn.yaml")
+    raise Exception("Please place a config.yaml file in /config/conv2rknn.yaml")
 
 if configuration["config"] != None:
     rknn_config = configuration["config"]

docker/rockchip/requirements-wheels-rk.txt

@@ -1,2 +1,2 @@
-rknn-toolkit2 == 2.3.2
-rknn-toolkit-lite2 == 2.3.2
+rknn-toolkit2 == 2.3.0
+rknn-toolkit-lite2 == 2.3.0

docker/rocm/Dockerfile

@@ -2,7 +2,7 @@
 
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
-ARG ROCM=1
+ARG ROCM=6.3.3
 ARG AMDGPU=gfx900
 ARG HSA_OVERRIDE_GFX_VERSION
 ARG HSA_OVERRIDE
@@ -13,16 +13,16 @@ FROM wget AS rocm
 ARG ROCM
 ARG AMDGPU
 
-RUN apt update -qq && \
+RUN apt update && \
     apt install -y wget gpg && \
-    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/7.0.1/ubuntu/jammy/amdgpu-install_7.0.1.70001-1_all.deb && \
+    wget -O rocm.deb https://repo.radeon.com/amdgpu-install/$ROCM/ubuntu/jammy/amdgpu-install_6.3.60303-1_all.deb && \
     apt install -y ./rocm.deb && \
     apt update && \
-    apt install -qq -y rocm
+    apt install -y rocm
 
 RUN mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib
 RUN cd /opt/rocm-$ROCM/lib && \
-    cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocsolver*.so* librocfft*.so* librocprofiler*.so* libroctx*.so*  librocroller.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/ && \
+    cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocfft*.so* librocprofiler*.so* libroctx*.so* /opt/rocm-dist/opt/rocm-$ROCM/lib/ && \
     mkdir -p /opt/rocm-dist/opt/rocm-$ROCM/lib/migraphx/lib && \
     cp -dpr migraphx/lib/* /opt/rocm-dist/opt/rocm-$ROCM/lib/migraphx/lib
 RUN cd /opt/rocm-dist/opt/ && ln -s rocm-$ROCM rocm
@@ -33,21 +33,17 @@ RUN echo /opt/rocm/lib|tee /opt/rocm-dist/etc/ld.so.conf.d/rocm.conf
 #######################################################################
 FROM deps AS deps-prelim
 
-COPY docker/rocm/debian-backports.sources /etc/apt/sources.list.d/debian-backports.sources
-RUN apt-get update && \
-    apt-get install -y libnuma1 && \
-    apt-get install -qq -y -t bookworm-backports mesa-va-drivers mesa-vulkan-drivers
+RUN apt-get update && apt-get install -y libnuma1
 
 WORKDIR /opt/frigate
 COPY --from=rootfs / /
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip" --break-system-packages
 RUN python3 -m pip config set global.break-system-packages true
 
 COPY docker/rocm/requirements-wheels-rocm.txt /requirements.txt
-RUN pip3 uninstall -y onnxruntime \
+RUN pip3 uninstall -y onnxruntime-openvino \
     && pip3 install -r /requirements.txt
 
 #######################################################################
@@ -64,10 +60,8 @@ COPY --from=rocm /opt/rocm-dist/ /
 
 #######################################################################
 FROM deps-prelim AS rocm-prelim-hsa-override0
-ENV MIGRAPHX_DISABLE_MIOPEN_FUSION=1
-ENV MIGRAPHX_DISABLE_SCHEDULE_PASS=1
-ENV MIGRAPHX_DISABLE_REDUCE_FUSION=1
-ENV MIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS=1
+ENV HSA_ENABLE_SDMA=0
+ENV MIGRAPHX_ENABLE_NHWC=1
 
 COPY --from=rocm-dist / /
 

docker/rocm/debian-backports.sources

@@ -1,6 +0,0 @@
-Types: deb
-URIs: http://deb.debian.org/debian
-Suites: bookworm-backports
-Components: main
-Enabled: yes
-Signed-By: /usr/share/keyrings/debian-archive-keyring.gpg

docker/rocm/requirements-wheels-rocm.txt

@@ -1 +1 @@
-onnxruntime-migraphx @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v7.0.1/onnxruntime_migraphx-1.23.0-cp311-cp311-linux_x86_64.whl
+onnxruntime-rocm @ https://github.com/NickM-27/frigate-onnxruntime-rocm/releases/download/v6.3.3/onnxruntime_rocm-1.20.1-cp311-cp311-linux_x86_64.whl

docker/rocm/rocm.hcl

@@ -2,7 +2,7 @@ variable "AMDGPU" {
   default = "gfx900"
 }
 variable "ROCM" {
-  default = "7.0.1"
+  default = "6.3.3"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""

docker/synaptics/Dockerfile

@@ -1,28 +0,0 @@
-# syntax=docker/dockerfile:1.6
-
-# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
-ARG DEBIAN_FRONTEND=noninteractive
-
-# Globally set pip break-system-packages option to avoid having to specify it every time
-ARG PIP_BREAK_SYSTEM_PACKAGES=1
-
-FROM wheels AS synap1680-wheels
-ARG TARGETARCH
-
-# Install dependencies
-RUN wget -qO- "https://github.com/GaryHuang-ASUS/synaptics_astra_sdk/releases/download/v1.5.0/Synaptics-SL1680-v1.5.0-rt.tar" | tar -C / -xzf -
-RUN wget -P /wheels/ "https://github.com/synaptics-synap/synap-python/releases/download/v0.0.4-preview/synap_python-0.0.4-cp311-cp311-manylinux_2_35_aarch64.whl"
-
-FROM deps AS synap1680-deps
-ARG TARGETARCH
-ARG PIP_BREAK_SYSTEM_PACKAGES
-
-RUN --mount=type=bind,from=synap1680-wheels,source=/wheels,target=/deps/synap-wheels \
-pip3 install --no-deps -U /deps/synap-wheels/*.whl
-
-WORKDIR /opt/frigate/
-COPY --from=rootfs / /
-
-COPY --from=synap1680-wheels /rootfs/usr/local/lib/*.so /usr/lib
-
-ADD https://raw.githubusercontent.com/synaptics-astra/synap-release/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80/model.synap /synaptics/mobilenet.synap

docker/synaptics/synaptics.hcl

@@ -1,27 +0,0 @@
-target wheels {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64"]
-  target = "wheels"
-}
-
-target deps {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64"]
-  target = "deps"
-}
-
-target rootfs {
-  dockerfile = "docker/main/Dockerfile"
-  platforms = ["linux/arm64"]
-  target = "rootfs"
-}
-
-target synaptics {
-  dockerfile = "docker/synaptics/Dockerfile"
-  contexts = {
-    wheels = "target:wheels",
-    deps = "target:deps",
-    rootfs = "target:rootfs"
-  }
-  platforms = ["linux/arm64"]
-}

docker/synaptics/synaptics.mk

@@ -1,15 +0,0 @@
-BOARDS += synaptics
-
-local-synaptics: version
-	docker buildx bake --file=docker/synaptics/synaptics.hcl synaptics \
-		--set synaptics.tags=frigate:latest-synaptics \
-		--load
-
-build-synaptics: version
-	docker buildx bake --file=docker/synaptics/synaptics.hcl synaptics \
-		--set synaptics.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-synaptics
-
-push-synaptics: build-synaptics
-	docker buildx bake --file=docker/synaptics/synaptics.hcl synaptics \
-		--set synaptics.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-synaptics \
-		--push

docker/tensorrt/Dockerfile.amd64

@@ -6,32 +6,24 @@ ARG DEBIAN_FRONTEND=noninteractive
 # Globally set pip break-system-packages option to avoid having to specify it every time
 ARG PIP_BREAK_SYSTEM_PACKAGES=1
 
-FROM wheels AS trt-wheels
+FROM tensorrt-base AS frigate-tensorrt
 ARG PIP_BREAK_SYSTEM_PACKAGES
+ENV TRT_VER=8.6.1
 
 # Install TensorRT wheels
 COPY docker/tensorrt/requirements-amd64.txt /requirements-tensorrt.txt
-COPY docker/main/requirements-wheels.txt /requirements-wheels.txt
-
-# remove dependencies from the requirements that have type constraints
-RUN  sed -i '/\[.*\]/d' /requirements-wheels.txt \
-    && pip3 wheel --wheel-dir=/trt-wheels -c /requirements-wheels.txt -r /requirements-tensorrt.txt
-
-FROM deps AS frigate-tensorrt
-ARG PIP_BREAK_SYSTEM_PACKAGES
-
-RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 uninstall -y onnxruntime tensorflow-cpu \
-    && pip3 install -U /deps/trt-wheels/*.whl
-
-COPY --from=rootfs / /
-COPY docker/tensorrt/detector/rootfs/etc/ld.so.conf.d /etc/ld.so.conf.d
-RUN ldconfig
+RUN pip3 install -U -r /requirements-tensorrt.txt && ldconfig
 
 WORKDIR /opt/frigate/
+COPY --from=rootfs / /
 
 # Dev Container w/ TRT
 FROM devcontainer AS devcontainer-trt
 
+COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
+COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
+COPY --from=trt-deps /usr/local/cuda-12.1 /usr/local/cuda
+COPY docker/tensorrt/detector/rootfs/ /
+COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     pip3 install -U /deps/trt-wheels/*.whl

docker/tensorrt/Dockerfile.arm64

@@ -1,69 +1,17 @@
-# syntax=docker/dockerfile:1.6
+# syntax=docker/dockerfile:1.4
 
 # https://askubuntu.com/questions/972516/debian-frontend-environment-variable
 ARG DEBIAN_FRONTEND=noninteractive
+
 ARG BASE_IMAGE
-ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3
-
-# Build TensorRT-specific library
-FROM ${TRT_BASE} AS trt-deps
-
-ARG TARGETARCH
-ARG COMPUTE_LEVEL
-
-RUN apt-get update \
-    && apt-get install -y git build-essential cuda-nvcc-* cuda-nvtx-* libnvinfer-dev libnvinfer-plugin-dev libnvparsers-dev libnvonnxparsers-dev \
-    && rm -rf /var/lib/apt/lists/*
-RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
-    /tensorrt_libyolo.sh
-
-# COPY required individual CUDA deps
-RUN mkdir -p /usr/local/cuda-deps
-RUN if [ "$TARGETARCH" = "amd64" ]; then \
-    cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libcurand.so.* /usr/local/cuda-deps/ && \
-    cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libnvrtc.so.* /usr/local/cuda-deps/ && \
-    cd /usr/local/cuda-deps/ && \
-    for lib in libnvrtc.so.*; do \
-    if [[ "$lib" =~ libnvrtc.so\.([0-9]+\.[0-9]+\.[0-9]+) ]]; then \
-    version="${BASH_REMATCH[1]}"; \
-    ln -sf "libnvrtc.so.$version" libnvrtc.so; \
-    fi; \
-    done && \
-    for lib in libcurand.so.*; do \
-    if [[ "$lib" =~ libcurand.so\.([0-9]+\.[0-9]+\.[0-9]+\.[0-9]+) ]]; then \
-    version="${BASH_REMATCH[1]}"; \
-    ln -sf "libcurand.so.$version" libcurand.so; \
-    fi; \
-    done; \
-    fi
-
-# Frigate w/ TensorRT Support as separate image
-FROM deps AS tensorrt-base
-
-#Disable S6 Global timeout
-ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
-
-# COPY TensorRT Model Generation Deps
-COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
-COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
-
-# COPY Individual CUDA deps folder
-COPY --from=trt-deps /usr/local/cuda-deps /usr/local/cuda
-
-COPY docker/tensorrt/detector/rootfs/ /
-ENV YOLO_MODELS=""
-
-HEALTHCHECK --start-period=600s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
-    CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1
-
 FROM ${BASE_IMAGE} AS build-wheels
 ARG DEBIAN_FRONTEND
 
 # Add deadsnakes PPA for python3.11
 RUN apt-get -qq update && \
-    apt-get -qq install -y --no-install-recommends \
-    software-properties-common \
-    && add-apt-repository ppa:deadsnakes/ppa
+        apt-get -qq install -y --no-install-recommends \
+        software-properties-common \
+        && add-apt-repository ppa:deadsnakes/ppa
 
 # Use a separate container to build wheels to prevent build dependencies in final image
 RUN apt-get -qq update \
@@ -76,7 +24,6 @@ RUN apt-get -qq update \
 RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
 RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip"
 
 FROM build-wheels AS trt-wheels
@@ -99,11 +46,12 @@ RUN --mount=type=bind,source=docker/tensorrt/detector/build_python_tensorrt.sh,t
     && TENSORRT_VER=$(cat /etc/TENSORRT_VER) /deps/build_python_tensorrt.sh
 
 COPY docker/tensorrt/requirements-arm64.txt /requirements-tensorrt.txt
-
-RUN pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt
-
 # See https://elinux.org/Jetson_Zoo#ONNX_Runtime
-ADD https://nvidia.box.com/shared/static/9yvw05k6u343qfnkhdv2x6xhygze0aq1.whl /trt-wheels/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
+ADD https://nvidia.box.com/shared/static/9yvw05k6u343qfnkhdv2x6xhygze0aq1.whl /tmp/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
+
+RUN pip3 uninstall -y onnxruntime-openvino \
+    && pip3 wheel --wheel-dir=/trt-wheels -r /requirements-tensorrt.txt \
+    && pip3 install --no-deps /tmp/onnxruntime_gpu-1.19.0-cp311-cp311-linux_aarch64.whl
 
 FROM build-wheels AS trt-model-wheels
 ARG DEBIAN_FRONTEND
@@ -144,12 +92,11 @@ RUN mkdir -p /etc/ld.so.conf.d && echo /usr/lib/ffmpeg/jetson/lib/ > /etc/ld.so.
 COPY --from=trt-wheels /etc/TENSORRT_VER /etc/TENSORRT_VER
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
     --mount=type=bind,from=trt-model-wheels,source=/trt-model-wheels,target=/deps/trt-model-wheels \
-    pip3 uninstall -y onnxruntime \
-    && pip3 install -U /deps/trt-wheels/*.whl /deps/trt-model-wheels/*.whl \
+    pip3 install -U /deps/trt-wheels/*.whl /deps/trt-model-wheels/*.whl \
     && ldconfig
 
 WORKDIR /opt/frigate/
 COPY --from=rootfs / /
 
 # Fixes "Error importing detector runtime: /usr/lib/aarch64-linux-gnu/libstdc++.so.6: cannot allocate memory in static TLS block"
-ENV LD_PRELOAD /usr/lib/aarch64-linux-gnu/libstdc++.so.6
+ENV LD_PRELOAD /usr/lib/aarch64-linux-gnu/libstdc++.so.6

docker/tensorrt/Dockerfile.base

@@ -0,0 +1,44 @@
+# syntax=docker/dockerfile:1.6
+
+# https://askubuntu.com/questions/972516/debian-frontend-environment-variable
+ARG DEBIAN_FRONTEND=noninteractive
+
+ARG TRT_BASE=nvcr.io/nvidia/tensorrt:23.12-py3
+
+# Build TensorRT-specific library
+FROM ${TRT_BASE} AS trt-deps
+
+ARG TARGETARCH
+ARG COMPUTE_LEVEL
+
+RUN apt-get update \
+    && apt-get install -y git build-essential cuda-nvcc-* cuda-nvtx-* libnvinfer-dev libnvinfer-plugin-dev libnvparsers-dev libnvonnxparsers-dev \
+    && rm -rf /var/lib/apt/lists/*
+RUN --mount=type=bind,source=docker/tensorrt/detector/tensorrt_libyolo.sh,target=/tensorrt_libyolo.sh \
+    /tensorrt_libyolo.sh
+
+# COPY required individual CUDA deps
+RUN mkdir -p /usr/local/cuda-deps
+RUN if [ "$TARGETARCH" = "amd64" ]; then \
+      cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libcurand.so.* /usr/local/cuda-deps/ && \
+      cp /usr/local/cuda-12.3/targets/x86_64-linux/lib/libnvrtc.so.* /usr/local/cuda-deps/ ; \
+    fi
+
+# Frigate w/ TensorRT Support as separate image
+FROM deps AS tensorrt-base
+
+#Disable S6 Global timeout
+ENV S6_CMD_WAIT_FOR_SERVICES_MAXTIME=0
+
+# COPY TensorRT Model Generation Deps
+COPY --from=trt-deps /usr/local/lib/libyolo_layer.so /usr/local/lib/libyolo_layer.so
+COPY --from=trt-deps /usr/local/src/tensorrt_demos /usr/local/src/tensorrt_demos
+
+# COPY Individual CUDA deps folder
+COPY --from=trt-deps /usr/local/cuda-deps /usr/local/cuda
+
+COPY docker/tensorrt/detector/rootfs/ /
+ENV YOLO_MODELS=""
+
+HEALTHCHECK --start-period=600s --start-interval=5s --interval=15s --timeout=5s --retries=3 \
+    CMD curl --fail --silent --show-error http://127.0.0.1:5000/api/version || exit 1

docker/tensorrt/detector/rootfs/etc/ld.so.conf.d/cuda_tensorrt.conf

@@ -1,6 +1,8 @@
+/usr/local/lib
+/usr/local/cuda
 /usr/local/lib/python3.11/dist-packages/nvidia/cudnn/lib
 /usr/local/lib/python3.11/dist-packages/nvidia/cuda_runtime/lib
 /usr/local/lib/python3.11/dist-packages/nvidia/cublas/lib
-/usr/local/lib/python3.11/dist-packages/nvidia/cufft/lib
-/usr/local/lib/python3.11/dist-packages/nvidia/curand/lib/
-/usr/local/lib/python3.11/dist-packages/nvidia/cuda_nvrtc/lib/
+/usr/local/lib/python3.11/dist-packages/nvidia/cuda_nvrtc/lib
+/usr/local/lib/python3.11/dist-packages/tensorrt
+/usr/local/lib/python3.11/dist-packages/nvidia/cufft/lib

docker/tensorrt/requirements-amd64.txt

@@ -1,19 +1,17 @@
 # NVidia TensorRT Support (amd64 only)
 --extra-index-url 'https://pypi.nvidia.com'
-cython==3.0.*; platform_machine == 'x86_64'
-nvidia_cuda_cupti_cu12==12.5.82; platform_machine == 'x86_64'
-nvidia-cublas-cu12==12.5.3.*; platform_machine == 'x86_64'
-nvidia-cudnn-cu12==9.3.0.*; platform_machine == 'x86_64'
-nvidia-cufft-cu12==11.2.3.*; platform_machine == 'x86_64'
-nvidia-curand-cu12==10.3.6.*; platform_machine == 'x86_64'
-nvidia_cuda_nvcc_cu12==12.5.82; platform_machine == 'x86_64'
-nvidia-cuda-nvrtc-cu12==12.5.82; platform_machine == 'x86_64'
-nvidia_cuda_runtime_cu12==12.5.82; platform_machine == 'x86_64'
-nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
-nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
-nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
-nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
-tensorflow==2.19.*; platform_machine == 'x86_64'
+numpy < 1.24; platform_machine == 'x86_64'
+tensorrt == 8.6.1; platform_machine == 'x86_64'
+tensorrt_bindings == 8.6.1; platform_machine == 'x86_64'
+cuda-python == 11.8.*; platform_machine == 'x86_64'
+cython == 3.0.*; platform_machine == 'x86_64'
+nvidia-cuda-runtime-cu12 == 12.1.*; platform_machine == 'x86_64'
+nvidia-cuda-runtime-cu11 == 11.8.*; platform_machine == 'x86_64'
+nvidia-cublas-cu11 == 11.11.3.6; platform_machine == 'x86_64'
+nvidia-cudnn-cu11 == 8.6.0.*; platform_machine == 'x86_64'
+nvidia-cudnn-cu12 == 9.5.0.*; platform_machine == 'x86_64'
+nvidia-cufft-cu11==10.*; platform_machine == 'x86_64'
+nvidia-cufft-cu12==11.*; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
-onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
+onnxruntime-gpu==1.20.*; platform_machine == 'x86_64'
 protobuf==3.20.3; platform_machine == 'x86_64'

docker/tensorrt/requirements-models-arm64.txt

@@ -1,2 +1,3 @@
 onnx == 1.14.0; platform_machine == 'aarch64'
 protobuf == 3.20.3; platform_machine == 'aarch64'
+numpy == 1.23.*; platform_machine == 'aarch64'  # required by python-tensorrt 8.2.1 (Jetpack 4.6)

docker/tensorrt/trt.hcl

@@ -79,13 +79,21 @@ target "trt-deps" {
   inherits = ["_build_args"]
 }
 
+target "tensorrt-base" {
+  dockerfile = "docker/tensorrt/Dockerfile.base"
+  context = "."
+  contexts = {
+    deps = "target:deps",
+  }
+  inherits = ["_build_args"]
+}
+
 target "tensorrt" {
   dockerfile = "docker/tensorrt/Dockerfile.${ARCH}"
   context = "."
   contexts = {
     wget = "target:wget",
-    wheels = "target:wheels",
-    deps = "target:deps",
+    tensorrt-base = "target:tensorrt-base",
     rootfs = "target:rootfs"
   }
   target = "frigate-tensorrt"

docs/docs/configuration/advanced.md

@@ -44,7 +44,7 @@ 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.
+This section can be used to set environment variables for those unable to modify the environment of the container (ie. within HassOS)
 
 Example:
 
@@ -172,43 +172,6 @@ listen [::]:8971 ipv6only=off ssl;
 listen [::]:5000 ipv6only=off;
 ```
 
-## Base path
-
-By default, Frigate runs at the root path (`/`). However some setups require to run Frigate under a custom path prefix (e.g. `/frigate`), especially when Frigate is located behind a reverse proxy that requires path-based routing.
-
-### Set Base Path via HTTP Header
-
-The preferred way to configure the base path is through the `X-Ingress-Path` HTTP header, which needs to be set to the desired base path in an upstream reverse proxy.
-
-For example, in Nginx:
-
-```
-location /frigate {
-    proxy_set_header X-Ingress-Path /frigate;
-    proxy_pass http://frigate_backend;
-}
-```
-
-### Set Base Path via Environment Variable
-
-When it is not feasible to set the base path via a HTTP header, it can also be set via the `FRIGATE_BASE_PATH` environment variable in the Docker Compose file.
-
-For example:
-
-```
-services:
-  frigate:
-    image: blakeblackshear/frigate:latest
-    environment:
-      - FRIGATE_BASE_PATH=/frigate
-```
-
-This can be used for example to access Frigate via a Tailscale agent (https), by simply forwarding all requests to the base path (http):
-
-```
-tailscale serve --https=443 --bg --set-path /frigate http://localhost:5000/frigate
-```
-
 ## Custom Dependencies
 
 ### Custom ffmpeg build
@@ -223,7 +186,7 @@ To do this:
 
 ### Custom go2rtc version
 
-Frigate currently includes go2rtc v1.9.10, there may be certain cases where you want to run a different version of go2rtc.
+Frigate currently includes go2rtc v1.9.2, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 

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. MQTT explorer can be used on the audio topic to see what volume level is being detected.
 
 :::tip
 
@@ -72,77 +72,3 @@ audio:
     - speech
     - yell
 ```
-
-### Audio Transcription
-
-Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI’s open-source Whisper models via `faster-whisper`. To enable transcription, it is recommended to only configure the features at the global level, and enable it at the individual camera level.
-
-```yaml
-audio_transcription:
-  enabled: False
-  device: ...
-  model_size: ...
-```
-
-Enable audio transcription for select cameras at the camera level:
-
-```yaml
-cameras:
-  back_yard:
-    ...
-    audio_transcription:
-      enabled: True
-```
-
-:::note
-
-Audio detection must be enabled and configured as described above in order to use audio transcription features.
-
-:::
-
-The optional config parameters that can be set at the global level include:
-
-- **`enabled`**: Enable or disable the audio transcription feature.
-  - Default: `False`
-  - It is recommended to only configure the features at the global level, and enable it at the individual camera level.
-- **`device`**: Device to use to run transcription and translation models.
-  - Default: `CPU`
-  - This can be `CPU` or `GPU`. The `sherpa-onnx` models are lightweight and run on the CPU only. The `whisper` models can run on GPU but are only supported on CUDA hardware.
-- **`model_size`**: The size of the model used for live transcription.
-  - Default: `small`
-  - This can be `small` or `large`. The `small` setting uses `sherpa-onnx` models that are fast, lightweight, and always run on the CPU but are not as accurate as the `whisper` model.
-  - The
-  - This config option applies to **live transcription only**. Recorded `speech` events will always use a different `whisper` model (and can be accelerated for CUDA hardware if available with `device: GPU`).
-- **`language`**: Defines the language used by `whisper` to translate `speech` audio events (and live audio only if using the `large` model).
-  - Default: `en`
-  - You must use a valid [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
-  - Transcriptions for `speech` events are translated.
-  - Live audio is translated only if you are using the `large` model. The `small` `sherpa-onnx` model is English-only.
-
-The only field that is valid at the camera level is `enabled`.
-
-#### Live transcription
-
-The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
-
-Results can be error-prone due to a number of factors, including:
-
-- Poor quality camera microphone
-- Distance of the audio source to the camera microphone
-- Low audio bitrate setting in the camera
-- Background noise
-- Using the `small` model - it's fast, but not accurate for poor quality audio
-
-For speech sources close to the camera with minimal background noise, use the `small` model.
-
-If you have CUDA hardware, you can experiment with the `large` `whisper` model on GPU. Performance is not quite as fast as the `sherpa-onnx` `small` model, but live transcription is far more accurate. Using the `large` model with CPU will likely be too slow for real-time transcription.
-
-#### Transcription and translation of `speech` audio events
-
-Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
-
-In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for in your config. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
-
-The transcribed/translated speech will appear in the description box in the Tracked Object Details pane. If Semantic Search is enabled, embeddings are generated for the transcription text and are fully searchable using the description search type.
-
-Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.

docs/docs/configuration/authentication.md

@@ -43,29 +43,13 @@ Restarting Frigate will reset the rate limits.
 
 If you are running Frigate behind a proxy, you will want to set `trusted_proxies` or these rate limits will apply to the upstream proxy IP address. This means that a brute force attack will rate limit login attempts from other devices and could temporarily lock you out of your instance. In order to ensure rate limits only apply to the actual IP address where the requests are coming from, you will need to list the upstream networks that you want to trust. These trusted proxies are checked against the `X-Forwarded-For` header when looking for the IP address where the request originated.
 
-If you are running a reverse proxy in the same Docker Compose file as Frigate, here is an example of how your auth config might look:
+If you are running a reverse proxy in the same docker compose file as Frigate, here is an example of how your auth config might look:
 
 ```yaml
 auth:
   failed_login_rate_limit: "1/second;5/minute;20/hour"
   trusted_proxies:
-    - 172.18.0.0/16 # <---- this is the subnet for the internal Docker Compose network
-```
-
-## Session Length
-
-The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required — otherwise, the user will need to log in again.
-
-While the default provides a balance of security and convenience, you can customize this duration to suit your specific security requirements and user experience preferences. The session length is configured in seconds.
-
-The default value of `86400` will expire the authentication session after 24 hours. Some other examples:
-
-- `0`: Setting the session length to 0 will require a user to log in every time they access the application or after a very short, immediate timeout.
-- `604800`: Setting the session length to 604800 will require a user to log in if the token is not refreshed for 7 days.
-
-```yaml
-auth:
-  session_length: 86400
+    - 172.18.0.0/16 # <---- this is the subnet for the internal docker compose network
 ```
 
 ## JWT Token Secret
@@ -81,8 +65,8 @@ python3 -c 'import secrets; print(secrets.token_hex(64))'
 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 Add-on options
+2. A docker secret named `FRIGATE_JWT_SECRET` in `/run/secrets/`
+3. A `jwt_secret` option from the Home Assistant Addon 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.
@@ -93,7 +77,7 @@ Changing the secret will invalidate current tokens.
 
 Frigate can be configured to leverage features of common upstream authentication proxies such as Authelia, Authentik, oauth2_proxy, or traefik-forward-auth.
 
-If you are leveraging the authentication of an upstream proxy, you likely want to disable Frigate's authentication as there is no correspondence between users in Frigate's database and users authenticated via the proxy. Optionally, if communication between the reverse proxy and Frigate is over an untrusted network, you should set an `auth_secret` in the `proxy` config and configure the proxy to send the secret value as a header named `X-Proxy-Secret`. Assuming this is an untrusted network, you will also want to [configure a real TLS certificate](tls.md) to ensure the traffic can't simply be sniffed to steal the secret.
+If you are leveraging the authentication of an upstream proxy, you likely want to disable Frigate's authentication. Optionally, if communication between the reverse proxy and Frigate is over an untrusted network, you should set an `auth_secret` in the `proxy` config and configure the proxy to send the secret value as a header named `X-Proxy-Secret`. Assuming this is an untrusted network, you will also want to [configure a real TLS certificate](tls.md) to ensure the traffic can't simply be sniffed to steal the secret.
 
 Here is an example of how to disable Frigate's authentication and also ensure the requests come only from your known proxy.
 
@@ -113,54 +97,17 @@ python3 -c 'import secrets; print(secrets.token_hex(64))'
 
 ### Header mapping
 
-If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Groups` values. Header names are not case sensitive. Multiple values can be included in the role header. Frigate expects that the character separating the roles is a comma, but this can be specified using the `separator` config entry.
-
-```yaml
-proxy:
-  ...
-  separator: "|" # This value defaults to a comma, but Authentik uses a pipe, for example.
-  header_map:
-    user: x-forwarded-user
-    role: x-forwarded-groups
-```
-
-Frigate supports `admin`, `viewer`, and custom roles (see below). When using port `8971`, Frigate validates these headers and subsequent requests use the headers `remote-user` and `remote-role` for authorization.
-
-A default role can be provided. Any value in the mapped `role` header will override the default.
-
-```yaml
-proxy:
-  ...
-  default_role: viewer
-```
-
-## Role mapping
-
-In some environments, upstream identity providers (OIDC, SAML, LDAP, etc.) do not pass a Frigate-compatible role directly, but instead pass one or more group claims. To handle this, Frigate supports a `role_map` that translates upstream group names into Frigate’s internal roles (`admin`, `viewer`, or custom).
+If you have disabled Frigate's authentication and your proxy supports passing a header with authenticated usernames and/or roles, you can use the `header_map` config to specify the header name so it is passed to Frigate. For example, the following will map the `X-Forwarded-User` and `X-Forwarded-Role` values. Header names are not case sensitive.
 
 ```yaml
 proxy:
   ...
   header_map:
     user: x-forwarded-user
-    role: x-forwarded-groups
-    role_map:
-      admin:
-        - sysadmins
-        - access-level-security
-      viewer:
-        - camera-viewer
-      operator:  # Custom role mapping
-        - operators
+    role: x-forwarded-role
 ```
 
-In this example:
-
-- If the proxy passes a role header containing `sysadmins` or `access-level-security`, the user is assigned the `admin` role.
-- If the proxy passes a role header containing `camera-viewer`, the user is assigned the `viewer` role.
-- If the proxy passes a role header containing `operators`, the user is assigned the `operator` custom role.
-- 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.
+Frigate supports both `admin` and `viewer` roles (see below). When using port `8971`, Frigate validates these headers and subsequent requests use the headers `remote-user` and `remote-role` for authorization.
 
 #### Port Considerations
 
@@ -170,7 +117,6 @@ In this example:
 - The `remote-role` header determines the user’s privileges:
   - **admin** → Full access (user management, configuration changes).
   - **viewer** → Read-only access.
-  - **Custom roles** → Read-only access limited to the cameras defined in `auth.roles[role]`.
 - Ensure your **proxy sends both user and role headers** for proper role enforcement.
 
 **Unauthenticated Port (5000)**
@@ -216,41 +162,6 @@ Frigate supports user roles to control access to certain features in the UI and
 
 - **admin**: Full access to all features, including user management and configuration.
 - **viewer**: Read-only access to the UI and API, including viewing cameras, review items, and historical footage. Configuration editor and settings in the UI are inaccessible.
-- **Custom Roles**: Arbitrary role names (alphanumeric, dots/underscores) with specific camera permissions. These extend the system for granular access (e.g., "operator" for select cameras).
-
-### Custom Roles and Camera Access
-
-The viewer role provides read-only access to all cameras in the UI and API. Custom roles allow admins to limit read-only access to specific cameras. Each role specifies an array of allowed camera names. If a user is assigned a custom role, their account is like the **viewer** role - they can only view Live, Review/History, Explore, and Export for the designated cameras. Backend API endpoints enforce this server-side (e.g., returning 403 for unauthorized cameras), and the frontend UI filters content accordingly (e.g., camera dropdowns show only permitted options).
-
-### Role Configuration Example
-
-```yaml
-cameras:
-  front_door:
-    # ... camera config
-  side_yard:
-    # ... camera config
-  garage:
-    # ... camera config
-
-auth:
-  enabled: true
-  roles:
-    operator: # Custom role
-      - front_door
-      - garage # Operator can access front and garage
-    neighbor:
-      - side_yard
-```
-
-If you want to provide access to all cameras to a specific user, just use the **viewer** role.
-
-### Managing User Roles
-
-1. Log in as an **admin** user via port `8971` (preferred), or unauthenticated via port `5000`.
-2. Navigate to **Settings**.
-3. In the **Users** section, edit a user’s role by selecting from available roles (admin, viewer, or custom).
-4. In the **Roles** section, add/edit/delete custom roles (select cameras via switches). Deleting a role auto-reassigns users to "viewer".
 
 ### Role Enforcement
 

docs/docs/configuration/bird_classification.md

@@ -1,31 +0,0 @@
----
-id: bird_classification
-title: Bird Classification
----
-
-Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
-
-## Minimum System Requirements
-
-Bird classification runs a lightweight tflite model on the CPU, there are no significantly different system requirements than running Frigate itself.
-
-## Model
-
-The classification model used is the MobileNet INat Bird Classification, [available identifiers can be found here.](https://raw.githubusercontent.com/google-coral/test_data/master/inat_bird_labels.txt)
-
-## Configuration
-
-Bird classification is disabled by default, it must be enabled in your config file before it can be used. Bird classification is a global configuration setting.
-
-```yaml
-classification:
-  bird:
-    enabled: true
-```
-
-## Advanced Configuration
-
-Fine-tune bird classification with these optional parameters:
-
-- `threshold`: Classification confidence score required to set the sub label on the object.
-  - Default: `0.9`.

docs/docs/configuration/birdseye.md

@@ -4,7 +4,7 @@ In addition to Frigate's Live camera dashboard, Birdseye allows a portable heads
 
 Birdseye can be viewed by adding the "Birdseye" camera to a Camera Group in the Web UI. Add a Camera Group by pressing the "+" icon on the Live page, and choose "Birdseye" as one of the cameras.
 
-Birdseye can also be used in Home Assistant dashboards, cast to media devices, etc.
+Birdseye can also be used in HomeAssistant dashboards, cast to media devices, etc.
 
 ## Birdseye Behavior
 

docs/docs/configuration/camera_specific.md

@@ -15,17 +15,6 @@ Many cameras support encoding options which greatly affect the live view experie
 
 :::
 
-## H.265 Cameras via Safari
-
-Some cameras support h265 with different formats, but Safari only supports the annexb format. When using h265 camera streams for recording with devices that use the Safari browser, the `apple_compatibility` option should be used.
-
-```yaml
-cameras:
-  h265_cam: # <------ Doesn't matter what the camera is called
-    ffmpeg:
-      apple_compatibility: true # <- Adds compatibility with MacOS and iPhone
-```
-
 ## MJPEG 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.
@@ -144,14 +133,7 @@ WEB Digest Algorithm  - MD5
 
 ### Reolink Cameras
 
-Reolink has many different camera models with inconsistently supported features and behavior. The below table shows a summary of various features and recommendations.
-
-| Camera Resolution | Camera Generation         | Recommended Stream Type           | Additional Notes                                                        |
-| ----------------- | ------------------------- | --------------------------------- | ----------------------------------------------------------------------- |
-| 5MP or lower      | All                       | http-flv                          | Stream is h264                                                          |
-| 6MP or higher     | Latest (ex: Duo3, CX-8##) | http-flv with ffmpeg 8.0, or rtsp | This uses the new http-flv-enhanced over H265 which requires ffmpeg 8.0 |
-| 6MP or higher     | Older (ex: RLC-8##)       | rtsp                              |                                                                         |
-
+Reolink has older cameras (ex: 410 & 520) as well as newer camera (ex: 520a & 511wa) which support different subsets of options. In both cases using the http stream is recommended.
 Frigate works much better with newer reolink cameras that are setup with the below options:
 
 If available, recommended settings are:
@@ -164,6 +146,12 @@ According to [this discussion](https://github.com/blakeblackshear/frigate/issues
 Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
 The setup of main stream can be also done via RTSP, but isn't always reliable on all hardware versions. The example configuration is working with the oldest HW version RLN16-410 device with multiple types of cameras.
 
+:::warning
+
+The below configuration only works for reolink cameras with stream resolution of 5MP or lower, 8MP+ cameras need to use RTSP as http-flv is not supported in this case.
+
+:::
+
 ```yaml
 go2rtc:
   streams:
@@ -231,7 +219,7 @@ go2rtc:
       - rtspx://192.168.1.1:7441/abcdefghijk
 ```
 
-[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-rtsp)
+[See the go2rtc docs for more information](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-rtsp)
 
 In the Unifi 2.0 update Unifi Protect Cameras had a change in audio sample rate which causes issues for ffmpeg. The input rate needs to be set for record if used directly with unifi protect.
 
@@ -244,37 +232,3 @@ ffmpeg:
 ### TP-Link VIGI Cameras
 
 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.`.
-
-## 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:
-
-- Preparation outside of Frigate:
-
-  - Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
-  - Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
-  - If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
-
-- In your Frigate Configuration File, add the go2rtc stream and roles as appropriate:
-
-```
-go2rtc:
-  streams:
-    usb_camera:
-      - "ffmpeg:device?video=0&video_size=1024x576#video=h264"
-
-cameras:
-  usb_camera:
-    enabled: true
-    ffmpeg:
-      inputs:
-        - path: rtsp://127.0.0.1:8554/usb_camera
-          input_args: preset-rtsp-restream
-          roles:
-            - detect
-            - record
-    detect:
-      enabled: false # <---- disable detection until you have a working camera feed
-      width: 1024
-      height: 576
-```

docs/docs/configuration/cameras.md

@@ -89,9 +89,7 @@ An ONVIF-capable camera that supports relative movement within the field of view
 
 ## ONVIF PTZ camera recommendations
 
-This list of working and non-working PTZ cameras is based on user feedback. If you'd like to report specific quirks or issues with a manufacturer or camera that would be helpful for other users, open a pull request to add to this list.
-
-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`, plus `PTZAuxiliary`. 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.
+This list of working and non-working PTZ cameras is based on user feedback.
 
 | Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                           |
 | ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -99,19 +97,17 @@ The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.or
 | 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" |
 | 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                        |      ✅      |      ✅      |                                                                                                                                                 |
 | 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 R5                    |      ✅      |      ❌      |                                                                                                                                                 |
-| Foscam SD4                   |      ✅      |      ❌      |                                                                                                                                                 |
 | Hanwha XNP-6550RH            |      ✅      |      ❌      |                                                                                                                                                 |
 | Hikvision                    |      ✅      |      ❌      | Incomplete ONVIF support (MoveStatus won't update even on latest firmware) - reported with HWP-N4215IH-DE and DS-2DE3304W-DE, but likely others |
 | Hikvision DS-2DE3A404IWG-E/W |      ✅      |      ✅      |                                                                                                                                                 |
-| Reolink                      |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink 511WA                |      ✅      |      ❌      | Zoom only                                                                                                                                       |
+| Reolink E1 Pro               |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink E1 Zoom              |      ✅      |      ❌      |                                                                                                                                                 |
+| Reolink RLC-823A 16x         |      ✅      |      ❌      |                                                                                                                                                 |
 | Speco O8P32X                 |      ✅      |      ❌      |                                                                                                                                                 |
 | Sunba 405-D20X               |      ✅      |      ❌      | Incomplete ONVIF support reported on original, and 4k models. All models are suspected incompatable.                                            |
 | Tapo                         |      ✅      |      ❌      | Many models supported, ONVIF Service Port: 2020                                                                                                 |
@@ -138,6 +134,3 @@ camera_groups:
     icon: LuCar
     order: 0
 ```
-
-## Two-Way Audio
-See the guide [here](/configuration/live/#two-way-talk)

docs/docs/configuration/custom_classification/object_classification.md

@@ -1,73 +0,0 @@
----
-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.
-
-## Minimum System Requirements
-
-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.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
-
-### Sub label vs Attribute
-
-- **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 /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.
-
-## Example use cases
-
-### Sub label
-
-- **Known pet vs unknown**: For `dog` objects, set sub label to your pet’s name (e.g., `buddy`) or `none` for others.
-- **Mail truck vs normal car**: For `car`, classify as `mail_truck` vs `car` to filter important arrivals.
-- **Delivery vs non-delivery person**: For `person`, classify `delivery` vs `visitor` based on uniform/props.
-
-### Attributes
-
-- **Backpack**: For `person`, add attribute `backpack: yes/no`.
-- **Helmet**: For `person` (worksite), add `helmet: yes/no`.
-- **Leash**: For `dog`, add `leash: yes/no` (useful for park or yard rules).
-- **Ladder rack**: For `truck`, add `ladder_rack: yes/no` to flag service vehicles.
-
-## Configuration
-
-Object classification is configured as a custom classification model. Each model has its own name and settings. You must list which object labels should be classified.
-
-```yaml
-classification:
-  custom:
-    dog:
-      threshold: 0.8
-      object_config:
-        objects: [dog] # object labels to classify
-        classification_type: sub_label # or: attribute
-```
-
-## Training the model
-
-Creating and training the model is done within the Frigate UI using the `Classification` page.
-
-### Getting Started
-
-When choosing which objects to classify, start with a small number of visually distinct classes and ensure your training samples match camera viewpoints and distances typical for those objects.
-
-// TODO add this section once UI is implemented. Explain process of selecting objects and curating training examples.
-
-### Improving the Model
-
-- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
-- **Data collection**: Use the model’s Train tab to gather balanced examples across times of day, weather, and distances.
-- **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
-- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
-- **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.

docs/docs/configuration/custom_classification/state_classification.md

@@ -1,52 +0,0 @@
----
-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.
-
-## Minimum System Requirements
-
-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.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
-
-## Example use cases
-
-- **Door state**: Detect if a garage or front door is open vs closed.
-- **Gate state**: Track if a driveway gate is open or closed.
-- **Trash day**: Bins at curb vs no bins present.
-- **Pool cover**: Cover on vs off.
-
-## Configuration
-
-State classification is configured as a custom classification model. Each model has its own name and settings. You must provide at least one camera crop under `state_config.cameras`.
-
-```yaml
-classification:
-  custom:
-    front_door:
-      threshold: 0.8
-      state_config:
-        motion: true # run when motion overlaps the crop
-        interval: 10 # also run every N seconds (optional)
-        cameras:
-          front:
-            crop: [0, 180, 220, 400]
-```
-
-## Training the model
-
-Creating and training the model is done within the Frigate UI using the `Classification` page.
-
-### Getting Started
-
-When choosing a portion of the camera frame for state classification, it is important to make the crop tight around the area of interest to avoid extra signals unrelated to what is being classified.
-
-// TODO add this section once UI is implemented. Explain process of selecting a crop.
-
-### Improving the Model
-
-- **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
-- **Data collection**: Use the model’s Train tab to gather balanced examples across times of day and weather.

docs/docs/configuration/face_recognition.md

@@ -3,90 +3,20 @@ id: face_recognition
 title: Face Recognition
 ---
 
-Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known `person` is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
+Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
 
-## Model Requirements
-
-### 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/#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.
-
-:::note
-
-Frigate needs to first detect a `person` before it can detect and recognize a face.
-
-:::
-
-### Face Recognition
-
-Frigate has support for two face recognition model types:
-
-- **small**: Frigate will run a FaceNet embedding model to recognize faces, which runs locally on the CPU. This model is optimized for efficiency and is not as accurate.
-- **large**: Frigate will run a large ArcFace embedding model that is optimized for accuracy. It is only recommended to be run when an integrated or dedicated GPU / NPU is available.
-
-In both cases, a lightweight face landmark detection model is also used to align faces before running recognition.
-
-All of these features run locally on your system.
-
-## Minimum System Requirements
-
-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.
+Frigate has support for CV2 Local Binary Pattern Face Recognizer to recognize faces, which runs locally. A lightweight face landmark detection model is also used to align faces before running them through the face recognizer.
 
 ## Configuration
 
-Face recognition is disabled by default, face recognition must be enabled in the UI or in your config file before it can be used. Face recognition is a global configuration setting.
+Face recognition is disabled by default, face recognition must be enabled in your config file before it can be used. Face recognition is a global configuration setting.
 
 ```yaml
 face_recognition:
   enabled: true
 ```
 
-Like the other real-time processors in Frigate, face recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
-
-## Advanced Configuration
-
-Fine-tune face recognition with these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
-
-### Detection
-
-- `detection_threshold`: Face detection confidence score required before recognition runs:
-  - Default: `0.7`
-  - Note: This is field only applies to the standalone face detection model, `min_score` should be used to filter for models that have face detection built in.
-- `min_area`: Defines the minimum size (in pixels) a face must be before recognition runs.
-  - Default: `500` pixels.
-  - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
-
-### Recognition
-
-- `model_size`: Which model size to use, options are `small` or `large`
-- `unknown_score`: Min score to mark a person as a potential match, matches at or below this will be marked as unknown.
-  - Default: `0.8`.
-- `recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
-  - Default: `0.9`.
-- `min_faces`: Min face recognitions for the sub label to be applied to the person object.
-  - Default: `1`
-- `save_attempts`: Number of images of recognized faces to save for training.
-  - Default: `100`.
-- `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
-  - Default: `True`.
-- `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
-  - Default: `None`.
-  - Note: This setting is only applicable when using the `large` model. See [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)
-
-## Usage
-
-Follow these steps to begin:
-
-1. **Enable face recognition** in your configuration file and restart Frigate.
-2. **Upload one face** using the **Add Face** button's wizard in the Face Library section of the Frigate UI. Read below for the best practices on expanding your training set.
-3. When Frigate detects and attempts to recognize a face, it will appear in the **Train** tab of the Face Library, along with its associated recognition confidence.
-4. From the **Train** tab, you can **assign the face** to a new or existing person to improve recognition accuracy for the future.
-
-## Creating a Robust Training Set
+## Dataset
 
 The number of images needed for a sufficient training set for face recognition varies depending on several factors:
 
@@ -95,9 +25,11 @@ The number of images needed for a sufficient training set for face recognition v
 
 However, here are some general guidelines:
 
-- Minimum: For basic face recognition tasks, a minimum of 5-10 images per person is often recommended.
-- Recommended: For more robust and accurate systems, 20-30 images per person is a good starting point.
-- Ideal: For optimal performance, especially in challenging conditions, 50-100 images per person can be beneficial.
+- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
+- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
+- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.
+
+## Creating a Robust Training Set
 
 The accuracy of face recognition is heavily dependent on the quality of data given to it for training. It is recommended to build the face training library in phases.
 
@@ -106,106 +38,19 @@ The accuracy of face recognition is heavily dependent on the quality of data giv
 When choosing images to include in the face training set it is recommended to always follow these recommendations:
 
 - If it is difficult to make out details in a persons face it will not be helpful in training.
-- Avoid images with extreme under/over-exposure.
+- Avoid images with under/over-exposure.
 - Avoid blurry / pixelated images.
-- Avoid training on infrared (gray-scale). The models are trained on color images and will be able to extract features from gray-scale images.
-- Using images of people wearing hats / sunglasses may confuse the model.
-- Do not upload too many similar images at the same time, it is recommended to train no more than 4-6 similar images for each person to avoid over-fitting.
+- Be careful when uploading images of people when they are wearing clothing that covers a lot of their face as this may confuse the training.
+- Do not upload too many images at the same time, it is recommended to train 4-6 images for each person each day so it is easier to know if the previously added images helped or hurt performance.
 
 :::
 
-### Understanding the Train Tab
-
-The Train tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
-
-Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
-
-Refer to the guidelines below for best practices on selecting images for training.
-
 ### Step 1 - Building a Strong Foundation
 
-When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 photos containing just this person's face. It is important that the person's face in the photo is front-facing and not turned, this will ensure a good starting point.
+When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-2 photos taken by a smartphone for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
 
-Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are front-facing. Ignore images from cameras that recognize faces from an angle. Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have over-fitting.
-
-You do not want to train images that are 90%+ as these are already being confidently recognized. In this step the goal is to train on clear, lower scoring front-facing images until the majority of front-facing images for a given person are consistently recognized correctly. Then it is time to move on to step 2.
+Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle. Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
 
 ### Step 2 - Expanding The Dataset
 
-Once front-facing images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone, and you still only want to train on images that score lower.
-
-## FAQ
-
-### How do I debug Face Recognition issues?
-
-Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
-
-1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Train 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).
-
-### Detection does not work well with blurry images?
-
-Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
-
-### Why can't I bulk upload photos?
-
-It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.
-
-### Why can't I bulk reprocess faces?
-
-Face embedding models work by breaking apart faces into different features. This means that when reprocessing an image, only images from a similar angle will have its score affected.
-
-### Why do unknown people score similarly to known people?
-
-This can happen for a few different reasons, but this is usually an indicator that the training set needs to be improved. This is often related to over-fitting:
-
-- If you train with only a few images per person, especially if those images are very similar, the recognition model becomes overly specialized to those specific images.
-- When you provide images with different poses, lighting, and expressions, the algorithm extracts features that are consistent across those variations.
-- By training on a diverse set of images, the algorithm becomes less sensitive to minor variations and noise in the input image.
-
-Review your face collections and remove most of the unclear or low-quality images. Then, use the **Reprocess** button on each face in the **Train** tab to evaluate how the changes affect recognition scores.
-
-Avoid training on images that already score highly, as this can lead to over-fitting. Instead, focus on relatively clear images that score lower - ideally with different lighting, angles, and conditions—to help the model generalize more effectively.
-
-### Frigate misidentified a face. Can I tell it that a face is "not" a specific person?
-
-No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
-For more guidance, refer to the section above on improving recognition accuracy.
-
-### I see scores above the threshold in the train tab, but a sub label wasn't assigned?
-
-The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.
-
-### Can I use other face recognition software like DoubleTake at the same time as the built in face recognition?
-
-No, using another face recognition service will interfere with Frigate's built in face recognition. When using double-take the sub_label feature must be disabled if the built in face recognition is also desired.
-
-### Does face recognition run on the recording stream?
-
-Face recognition does not run on the recording stream, this would be suboptimal for many reasons:
-
-1. The latency of accessing the recordings means the notifications would not include the names of recognized people because recognition would not complete until after.
-2. The embedding models used run on a set image size, so larger images will be scaled down to match this anyway.
-3. Motion clarity is much more important than extra pixels, over-compression and motion blur are much more detrimental to results than resolution.
-
-### I get an unknown error when taking a photo directly with my iPhone
-
-By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
-
-### How can I delete the face database and start over?
-
-Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.
+Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.

docs/docs/configuration/ffmpeg_presets.md

@@ -9,7 +9,7 @@ Some presets of FFmpeg args are provided by default to make the configuration ea
 
 It is highly recommended to use hwaccel presets in the config. These presets not only replace the longer args, but they also give Frigate hints of what hardware is available and allows Frigate to make other optimizations using the GPU such as when encoding the birdseye restream or when scaling a stream that has a size different than the native stream size.
 
-See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more info on how to setup hwaccel for your GPU / iGPU.
+See [the hwaccel docs](/configuration/hardware_acceleration.md) for more info on how to setup hwaccel for your GPU / iGPU.
 
 | Preset                | Usage                          | Other Notes                                           |
 | --------------------- | ------------------------------ | ----------------------------------------------------- |
@@ -21,7 +21,8 @@ See [the hwaccel docs](/configuration/hardware_acceleration_video.md) for more i
 | preset-nvidia         | Nvidia GPU                     |                                                       |
 | preset-jetson-h264    | Nvidia Jetson with h264 stream |                                                       |
 | preset-jetson-h265    | Nvidia Jetson with h265 stream |                                                       |
-| preset-rkmpp        | Rockchip MPP  | Use image with \*-rk suffix and privileged mode       |
+| preset-rk-h264        | Rockchip MPP with h264 stream  | Use image with \*-rk suffix and privileged mode       |
+| preset-rk-h265        | Rockchip MPP with h265 stream  | Use image with \*-rk suffix and privileged mode       |
 
 ### Input Args Presets
 
@@ -70,11 +71,11 @@ cameras:
 
 Output args presets help make the config more readable and handle use cases for different types of streams to ensure consistent recordings.
 
-| Preset                           | Usage                             | Other Notes                                                                                                                                                                                              |
-| -------------------------------- | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| preset-record-generic            | Record WITHOUT audio              | If your camera doesn’t have audio, or if you don’t want to record audio, use this option                                                                                                                 |
-| preset-record-generic-audio-copy | Record WITH original audio        | Use this to enable audio in recordings                                                                                                                                                                   |
-| preset-record-generic-audio-aac  | Record WITH transcoded aac audio  | This is the default when no option is specified. Use it to transcode audio to AAC. If the source is already in AAC format, use preset-record-generic-audio-copy instead to avoid unnecessary re-encoding |
-| preset-record-mjpeg              | Record an mjpeg stream            | Recommend restreaming mjpeg stream instead                                                                                                                                                               |
-| preset-record-jpeg               | Record live jpeg                  | Recommend restreaming live jpeg instead                                                                                                                                                                  |
-| preset-record-ubiquiti           | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio                                                                                                                                                              |
+| Preset                           | Usage                             | Other Notes                                                                                                                          |
+| -------------------------------- | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
+| preset-record-generic            | Record WITHOUT audio              | This is the default when nothing is specified                                                                                        |
+| preset-record-generic-audio-copy | Record WITH original audio        | Use this to enable audio in recordings                                                                                               |
+| preset-record-generic-audio-aac  | Record WITH transcoded aac audio  | Use this to transcode to aac audio. If your source is already aac, use preset-record-generic-audio-copy instead to avoid re-encoding |
+| preset-record-mjpeg              | Record an mjpeg stream            | Recommend restreaming mjpeg stream instead                                                                                           |
+| preset-record-jpeg               | Record live jpeg                  | Recommend restreaming live jpeg instead                                                                                              |
+| preset-record-ubiquiti           | Record ubiquiti stream with audio | Recordings with ubiquiti non-standard audio                                                                                          |

docs/docs/configuration/genai.md

@@ -9,38 +9,24 @@ Requests for a description are sent off automatically to your AI provider at the
 
 ## 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.
+Generative AI can be enabled for all cameras or only for specific cameras. 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:
+  enabled: True
   provider: gemini
   api_key: "{FRIGATE_GEMINI_API_KEY}"
   model: gemini-1.5-flash
 
 cameras:
-  front_camera:
-    objects:
-    genai:
-        enabled: True # <- enable GenAI for your front camera
-        use_snapshot: True
-        objects:
-          - person
-        required_zones:
-          - steps
+  front_camera: ...
   indoor_camera:
-    objects:
-      genai:
-        enabled: False # <- disable GenAI for your indoor camera
+    genai: # <- disable GenAI for your indoor camera
+      enabled: False
 ```
 
-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
@@ -69,6 +55,7 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 
 ```yaml
 genai:
+  enabled: True
   provider: ollama
   base_url: http://localhost:11434
   model: llava:7b
@@ -95,17 +82,12 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
 
 ```yaml
 genai:
+  enabled: True
   provider: gemini
   api_key: "{FRIGATE_GEMINI_API_KEY}"
   model: gemini-1.5-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.
@@ -122,6 +104,7 @@ To start using OpenAI, you must first [create an API key](https://platform.opena
 
 ```yaml
 genai:
+  enabled: True
   provider: openai
   api_key: "{FRIGATE_OPENAI_API_KEY}"
   model: gpt-4o
@@ -149,6 +132,7 @@ To start using Azure OpenAI, you must first [create a resource](https://learn.mi
 
 ```yaml
 genai:
+  enabled: True
   provider: azure_openai
   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}"
@@ -183,7 +167,7 @@ Analyze the sequence of images containing the {label}. Focus on the likely inten
 
 :::tip
 
-Prompts can use variable replacements `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
+Prompts can use variable replacements like `{label}`, `{sub_label}`, and `{camera}` to substitute information from the tracked object as part of the prompt.
 
 :::
 
@@ -191,35 +175,34 @@ You are also able to define custom prompts in your configuration.
 
 ```yaml
 genai:
+  enabled: True
   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."
+  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.
+Prompts can also be overriden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. 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.
 
 ```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
+    genai:
+      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

docs/docs/configuration/genai/config.md

@@ -1,142 +0,0 @@
----
-id: genai_config
-title: Configuring Generative AI
----
-
-## Configuration
-
-A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 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_`.
-
-## 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). Note that Frigate will not automatically download the model you specify in your config, Ollama will try to download the model but it may take longer than the timeout, it is recommended to pull the model beforehand by running `ollama pull your_model` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
-
-:::info
-
-Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger sizes are more capable of complex tasks and understanding of situations, but requires more memory and computational resources. It is recommended to try multiple models and experiment to see which performs best.
-
-:::
-
-:::tip
-
-If you are trying to use a single model for Frigate and HomeAssistant, it will need to support vision and tools calling. https://github.com/skye-harris/ollama-modelfiles contains optimized model configs for this task.
-
-:::
-
-The following models are recommended:
-
-| Model             | Notes                                                       |
-| ----------------- | ----------------------------------------------------------- |
-| `Intern3.5VL`     | Relatively fast with good vision comprehension
-| `gemma3`          | Strong frame-to-frame understanding, slower inference times |
-| `qwen2.5vl`       | Fast but capable model with good vision comprehension       |
-| `llava-phi3`      | Lightweight and fast model with vision comprehension        |
-
-:::note
-
-You should have at least 8 GB of RAM available (or VRAM if running on GPU) to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
-
-:::
-
-### Configuration
-
-```yaml
-genai:
-  provider: ollama
-  base_url: http://localhost:11434
-  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 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). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
-
-### 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-1.5-flash
-```
-
-## 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). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
-
-### 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). 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 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://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

@@ -1,77 +0,0 @@
----
-id: genai_objects
-title: Object Descriptions
----
-
-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.
-
-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 object descriptions can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
-
-## Usage and Best Practices
-
-Frigate's thumbnail search excels at identifying specific details about tracked objects – for example, using an "image caption" approach to find a "person wearing a yellow vest," "a white dog running across the lawn," or "a red car on a residential street." To enhance this further, Frigate’s default prompts are designed to ask your AI provider about the intent behind the object's actions, rather than just describing its appearance.
-
-While generating simple descriptions of detected objects is useful, understanding intent provides a deeper layer of insight. Instead of just recognizing "what" is in a scene, Frigate’s default prompts aim to infer "why" it might be there or "what" it could do next. Descriptions tell you what’s happening, but intent gives context. For instance, a person walking toward a door might seem like a visitor, but if they’re moving quickly after hours, you can infer a potential break-in attempt. Detecting a person loitering near a door at night can trigger an alert sooner than simply noting "a person standing by the door," helping you respond based on the situation’s context.
-
-## Custom Prompts
-
-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/review_summaries.md

@@ -1,56 +0,0 @@
----
-id: genai_review
-title: Review Summaries
----
-
-Generative AI can be used to automatically generate structured summaries of review items. These summaries will show up in Frigate's native notifications as well as in the UI. Generative AI can also be used to take a collection of summaries over a period of time and provide a report, which may be useful to get a quick report of everything that happened while out for some amount of time.
-
-Requests for a summary are requested automatically to your AI provider for alert review items when the activity has ended, they can also be optionally enabled for detections as well.
-
-Generative AI review summaries can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/review_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
-
-## Review Summary Usage and Best Practices
-
-Review summaries provide structured JSON responses that are saved for each review item:
-
-```
-- `scene` (string): A full description including setting, entities, actions, and any plausible supported inferences.
-- `confidence` (float): 0-1 confidence in the analysis.
-- `other_concerns` (list): List of user-defined concerns that may need additional investigation.
-- `potential_threat_level` (integer): 0, 1, or 2 as defined below.
-
-Threat-level definitions:
-- 0 — Typical or expected activity for this location/time (includes residents, guests, or known animals engaged in normal activities, even if they glance around or scan surroundings).
-- 1 — Unusual or suspicious activity: At least one security-relevant behavior is present **and not explainable by a normal residential activity**.
-- 2 — Active or immediate threat: Breaking in, vandalism, aggression, weapon display.
-```
-
-This will show in the UI as a list of concerns that each review item has along with the general description.
-
-### Defining Typical Activity
-
-Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response. The default `activity_context_prompt` is below:
-
-```
-- **Zone context is critical**: Private enclosed spaces (back yards, back decks, fenced areas, inside garages) are resident territory where brief transient activity, routine tasks, and pet care are expected and normal. Front yards, driveways, and porches are semi-public but still resident spaces where deliveries, parking, and coming/going are routine. Consider whether the zone and activity align with normal residential use.
-- **Person + Pet = Normal Activity**: When both "Person" and "Dog" (or "Cat") are detected together in residential zones, this is routine pet care activity (walking, letting out, playing, supervising). Assign Level 0 unless there are OTHER strong suspicious behaviors present (like testing doors, taking items, etc.). A person with their pet in a residential zone is baseline normal activity.
-- Brief appearances in private zones (back yards, garages) are normal residential patterns.
-- Normal residential activity includes: residents, family members, guests, deliveries, services, maintenance workers, routine property use (parking, unloading, mail pickup, trash removal).
-- Brief movement with legitimate items (bags, packages, tools, equipment) in appropriate zones is routine.
-```
-
-### Additional Concerns
-
-Along with the concern of suspicious activity or immediate threat, you may have concerns such as animals in your garden or a gate being left open. These concerns can be configured so that the review summaries will make note of them if the activity requires additional review. For example:
-
-```yaml
-review:
-  genai:
-    enabled: true
-    additional_concerns:
-      - animals in the garden
-```
-
-## Review Reports
-
-Along with individual review item summaries, Generative AI provides the ability to request a report of a given time period. For example, you can get a daily report while on a vacation of any suspicious activity or other concerns that may require review.

docs/docs/configuration/hardware_acceleration.md

@@ -1,19 +1,20 @@
 ---
-id: hardware_acceleration_video
-title: Video Decoding
+id: hardware_acceleration
+title: Hardware Acceleration
 ---
 
-# Video Decoding
+# Hardware Acceleration
 
-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.
+It is highly recommended to use a GPU for hardware acceleration 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.
 
 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
 
+# Officially Supported
 
 ## 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 Add-on, you may need to use the full access variant and turn off _Protection mode_ for hardware acceleration.
+If you are using the HA addon, 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
@@ -27,8 +28,8 @@ ffmpeg:
 
 :::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:
+If running Frigate in Docker, you either need to run in privileged mode or
+map the `/dev/video*` devices to Frigate. With Docker compose add:
 
 ```yaml
 services:
@@ -68,19 +69,18 @@ Or map in all the `/dev/video*` devices.
 
 **Recommended hwaccel Preset**
 
-| 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-\* |                                      |
+| CPU Generation | Intel Driver | Recommended Preset | Notes                               |
+| -------------- | ------------ | ------------------ | ----------------------------------- |
+| gen1 - gen7    | i965         | 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 Add-on 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 `frigate.yaml` for HA OS 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.
 
@@ -175,33 +175,23 @@ For more information on the various values across different distributions, see h
 
 Depending on your OS and kernel configuration, you may need to change the `/proc/sys/kernel/perf_event_paranoid` kernel tunable. You can test the change by running `sudo sh -c 'echo 2 >/proc/sys/kernel/perf_event_paranoid'` which will persist until a reboot. Make it permanent by running `sudo sh -c 'echo kernel.perf_event_paranoid=2 >> /etc/sysctl.d/local.conf'`
 
-#### Stats for SR-IOV or other devices
+#### Stats for SR-IOV devices
 
-When using virtualized GPUs via SR-IOV, you need to specify the device path to use to gather stats from `intel_gpu_top`. This example may work for some systems using SR-IOV:
+When using virtualized GPUs via SR-IOV, additional args are needed for GPU stats to function. This can be enabled with the following config:
 
 ```yaml
 telemetry:
   stats:
-    intel_gpu_device: "sriov"
+    sriov: True
 ```
 
-For other virtualized GPUs, try specifying the direct path to the device instead:
-
-```yaml
-telemetry:
-  stats:
-    intel_gpu_device: "drm:/dev/dri/card0"
-```
-
-If you are passing in a device path, make sure you've passed the device through to the container.
-
 ## 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).
+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 `frigate.yaml` for HA OS users](advanced.md#environment_vars).
 
 :::
 
@@ -228,7 +218,7 @@ Additional configuration is needed for the Docker container to be able to access
 services:
   frigate:
     ...
-    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt
+    image: ghcr.io/blakeblackshear/frigate:stable
     deploy:    # <------------- Add this section
       resources:
         reservations:
@@ -246,7 +236,7 @@ docker run -d \
   --name frigate \
   ...
   --gpus=all \
-  ghcr.io/blakeblackshear/frigate:stable-tensorrt
+  ghcr.io/blakeblackshear/frigate:stable
 ```
 
 ### Setup Decoder
@@ -305,7 +295,8 @@ These instructions were originally based on the [Jellyfin documentation](https:/
 ## NVIDIA Jetson (Orin AGX, Orin NX, Orin Nano\*, Xavier AGX, Xavier NX, TX2, TX1, Nano)
 
 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.
+with codecs that use the Jetson's dedicated media engine. If your Jetson host is running Jetpack 5.0+ use the `stable-tensorrt-jp5`
+tagged image, or 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:
 
@@ -315,16 +306,17 @@ You will need to use the image with the nvidia container runtime:
 docker run -d \
   ...
   --runtime nvidia
-  ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6
+  ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp5
 ```
 
 ### Docker Compose - Jetson
 
 ```yaml
+version: '2.4'
 services:
   frigate:
     ...
-    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6
+    image: ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp5
     runtime: nvidia   # Add this
 ```
 
@@ -385,8 +377,13 @@ Make sure to follow the [Rockchip specific installation instructions](/frigate/i
 Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
 
 ```yaml
+# if you try to decode a h264 encoded stream
 ffmpeg:
-  hwaccel_args: preset-rkmpp
+  hwaccel_args: preset-rk-h264
+
+# if you try to decode a h265 (hevc) encoded stream
+ffmpeg:
+  hwaccel_args: preset-rk-h265
 ```
 
 :::note
@@ -394,62 +391,3 @@ ffmpeg:
 Make sure that your SoC supports hardware acceleration for your input stream. For example, if your camera streams with h265 encoding and a 4k resolution, your SoC must be able to de- and encode h265 with a 4k resolution or higher. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
 
 :::
-
-:::warning
-
-If one or more of your cameras are not properly processed and this error is shown in the logs:
-
-```
-[segment @ 0xaaaaff694790] Timestamps are unset in a packet for stream 0. This is deprecated and will stop working in the future. Fix your code to set the timestamps properly
-[Parsed_scale_rkrga_0 @ 0xaaaaff819070] No hw context provided on input
-[Parsed_scale_rkrga_0 @ 0xaaaaff819070] Failed to configure output pad on Parsed_scale_rkrga_0
-Error initializing filters!
-Error marking filters as finished
-[out#1/rawvideo @ 0xaaaaff3d8730] Nothing was written into output file, because at least one of its streams received no packets.
-Restarting ffmpeg...
-```
-
-you should try to uprade to FFmpeg 7. This can be done using this config option:
-
-```
-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:
-
-```
-cameras:
-  name:
-    ffmpeg:
-      inputs:
-        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
-```
-
-:::
-
-## Synaptics
-
-Hardware accelerated video de-/encoding is supported on Synpatics SL-series SoC.
-
-### Prerequisites
-
-Make sure to follow the [Synaptics specific installation instructions](/frigate/installation#synaptics).
-
-### Configuration
-
-Add one of the following FFmpeg presets to your `config.yml` to enable hardware video processing:
-
-```yaml
-ffmpeg:
-  hwaccel_args: -c:v h264_v4l2m2m
-  input_args: preset-rtsp-restream
-output_args:
-  record: preset-record-generic-audio-aac
-```
-
-:::warning
-
-Make sure that your SoC supports hardware acceleration for your input stream and your input stream is h264 encoding. For example, if your camera streams with h264 encoding, your SoC must be able to de- and encode with it. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
-
-:::

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -1,35 +0,0 @@
----
-id: hardware_acceleration_enrichments
-title: Enrichments
----
-
-# Enrichments
-
-Some of Frigate's enrichments can use a discrete GPU / NPU for accelerated processing.
-
-## Requirements
-
-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 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.
-
-- **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 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
-
-A Google Coral is a TPU (Tensor Processing Unit), not a dedicated GPU (Graphics Processing Unit) and therefore does not provide any kind of acceleration for Frigate's enrichments.
-
-:::

docs/docs/configuration/index.md

@@ -3,12 +3,10 @@ id: index
 title: Frigate Configuration
 ---
 
-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 Home Assistant Addon installations, the config file needs to be in the root of your Home Assistant config directory (same location as `configuration.yaml`). It can be named `frigate.yaml` or `frigate.yml`, but if both files exist `frigate.yaml` will be preferred and `frigate.yml` will be ignored.
 
 For all other installation types, the config file should be mapped to `/config/config.yml` inside the container.
 
-It can be named `config.yml` or `config.yaml`, but if both files exist `config.yml` will be preferred and `config.yaml` will be ignored.
-
 It is recommended to start with a minimal configuration and add to it as described in [this guide](../guides/getting_started.md) and use the built in configuration editor in Frigate's UI which supports validation.
 
 ```yaml
@@ -25,24 +23,9 @@ cameras:
             - detect
 ```
 
-## Accessing the Home Assistant Add-on configuration directory {#accessing-add-on-config-dir}
+## VSCode Configuration Schema
 
-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.
-
-| 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 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 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.
+VSCode 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 VSCode 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 VSCode on another machine.
 
 ## Environment Variable Substitution
 
@@ -82,10 +65,10 @@ 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 Add-on with USB Coral
+### Raspberry Pi Home Assistant Addon with USB Coral
 
 - Single camera with 720p, 5fps stream for detect
-- MQTT connected to the Home Assistant Mosquitto Add-on
+- MQTT connected to home assistant mosquitto addon
 - 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

docs/docs/configuration/license_plate_recognition.md

@@ -3,34 +3,32 @@ id: license_plate_recognition
 title: License Plate Recognition (LPR)
 ---
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a known name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters or recognized name as a `sub_label` to objects that are of type `car`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. However, LPR does not run on stationary vehicles.
 
-When a plate is recognized, the details are:
+When a plate is recognized, the detected characters or recognized name is:
 
-- Added as a `sub_label` (if known) or the `recognized_license_plate` field (if unknown) to a tracked object.
-- Viewable in the Review Item Details pane in Review (sub labels).
-- Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
+- Added as a `sub_label` to the `car` tracked object.
+- Viewable in the Review Item Details pane in Review and the Tracked Object Details pane in Explore.
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` (known) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
-- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if known) and `plate`.
+- Published via the `frigate/events` MQTT topic as a `sub_label` for the tracked object.
 
 ## Model Requirements
 
 Users running a Frigate+ model (or any custom model that natively detects license plates) should ensure that `license_plate` is added to the [list of objects to track](https://docs.frigate.video/plus/#available-label-types) either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.
 
-Users without a model that detects license plates can still run LPR. Frigate uses a lightweight YOLOv9 license plate detection model that can be configured to run on your CPU or GPU. In this case, you should _not_ define `license_plate` in your list of objects to track.
+Users without a model that detects license plates can still run LPR. Frigate uses a lightweight YOLOv9 license plate detection model that runs on your CPU. In this case, you should _not_ define `license_plate` in your list of objects to track.
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+Frigate needs to first detect a `car` before it can recognize a license plate. If you're using a dedicated LPR camera or have a zoomed-in view, make sure the camera captures enough of the `car` for Frigate to detect it reliably.
 
 :::
 
 ## Minimum System Requirements
 
-License plate recognition works by running AI models locally on your system. The models are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
+License plate recognition works by running AI models locally on your system. The models are relatively lightweight and run on your CPU. At least 4GB of RAM is required.
 
 ## Configuration
 
@@ -41,47 +39,28 @@ lpr:
   enabled: True
 ```
 
-Like other enrichments in Frigate, LPR **must be enabled globally** to use the feature. You should disable it for specific cameras at the camera level if you don't want to run LPR on cars on those cameras:
-
-```yaml
-cameras:
-  garage:
-    ...
-    lpr:
-      enabled: False
-```
-
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+Ensure that your camera is configured to detect objects of type `car`, and that a car is actually being detected by Frigate. Otherwise, LPR will not run.
 
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
 
 ## Advanced Configuration
 
-Fine-tune the LPR feature using these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled`, `min_area`, and `enhancement`.
+Fine-tune the LPR feature using these optional parameters:
 
 ### Detection
 
 - **`detection_threshold`**: License plate object detection confidence score required before recognition runs.
   - Default: `0.7`
-  - Note: This is field only applies to the standalone license plate detection model, `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
-- **`min_area`**: Defines the minimum area (in pixels) a license plate must be before recognition runs.
-  - Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
+  - Note: If you are using a Frigate+ model and you set the `threshold` in your objects config for `license_plate` higher than this value, recognition will never run. It's best to ensure these values match, or this `detection_threshold` is lower than your object config `threshold`.
+- **`min_area`**: Defines the minimum size (in pixels) a license plate must be before recognition runs.
+  - Default: `1000` pixels.
   - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
-- **`device`**: Device to use to run license plate detection _and_ recognition models.
-  - Default: `CPU`
-  - This can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
-- **`model_size`**: The size of the model used to identify regions of text on plates.
-  - Default: `small`
-  - This can be `small` or `large`.
-  - The `small` model is fast and identifies groups of Latin and Chinese characters.
-  - The `large` model identifies Latin characters only, but uses an enhanced text detector and is more capable at finding characters on multi-line plates. It is significantly slower than the `small` model. Note that using the `large` model does not improve _text recognition_, but it may improve _text detection_.
-  - For most users, the `small` model is recommended.
 
 ### Recognition
 
-- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
+- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a sub label.
   - Default: `0.9`.
-- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label` to an object.
+- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a sub label to an object.
   - Use this to filter out short, incomplete, or incorrect detections.
 - **`format`**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
   - `"^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$"` matches plates like "B AB 1234" or "M X 7"
@@ -90,61 +69,18 @@ Fine-tune the LPR feature using these optional parameters at the global level of
 
 ### Matching
 
-- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` and `motorcycle` objects when a recognized plate matches a known value.
+- **`known_plates`**: List of strings or regular expressions that assign custom a `sub_label` to `car` objects when a recognized plate matches a known value.
   - These labels appear in the UI, filters, and notifications.
-  - Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **`match_distance`**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
   - For example, setting `match_distance: 1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`.
   - This parameter will _not_ operate on known plates that are defined as regular expressions. You should define the full string of your plate in `known_plates` in order to use `match_distance`.
 
-### Image Enhancement
-
-- **`enhancement`**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. This preprocessing step can sometimes improve accuracy but may also have the opposite effect.
-  - Default: `0` (no enhancement)
-  - Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters, actually making them much harder for Frigate to recognize.
-  - This setting is best adjusted at the camera level if running LPR on multiple cameras.
-  - If Frigate is already recognizing plates correctly, leave this setting at the default of `0`. However, if you're experiencing frequent character issues or incomplete plates and you can already easily read the plates yourself, try increasing the value gradually, starting at 5 and adjusting as needed. You should see how different enhancement levels affect your plates. Use the `debug_save_plates` configuration option (see below).
-
-### Normalization Rules
-
-- **`replace_rules`**: List of regex replacement rules to normalize detected plates. These rules are applied sequentially. Each rule must have a `pattern` (which can be a string or a regex, prepended by `r`) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
-
-These rules must be defined at the global level of your `lpr` config.
-
-```yaml
-lpr:
-  replace_rules:
-    - pattern: r'[%#*?]' # Remove noise symbols
-      replacement: ""
-    - pattern: r'[= ]' # Normalize = or space to dash
-      replacement: "-"
-    - pattern: "O" # Swap 'O' to '0' (common OCR error)
-      replacement: "0"
-    - pattern: r'I' # Swap 'I' to '1'
-      replacement: "1"
-    - 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.
-- Backrefs (`\1`, `\2`) allow dynamic replacements (e.g., capture groups).
-- Any changes made by the rules are printed to the LPR debug log.
-- Tip: You can test patterns with tools like regex101.com.
-
-### Debugging
-
-- **`debug_save_plates`**: Set to `True` to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `<camera>/<event_id>`, and named based on the capture timestamp.
-  - These saved images are not full plates but rather the specific areas of text detected on the plates. It is normal for the text detection model to sometimes find multiple areas of text on the plate. Use them to analyze what text Frigate recognized and how image enhancement affects detection.
-  - **Note:** Frigate does **not** automatically delete these debug images. Once LPR is functioning correctly, you should disable this option and manually remove the saved files to free up storage.
-
 ## Configuration Examples
 
-These configuration parameters are available at the global level of your config. The only optional parameters that should be set at the camera level are `enabled`, `min_area`, and `enhancement`.
-
 ```yaml
 lpr:
   enabled: True
-  min_area: 1500 # Ignore plates with an area (length x width) smaller than 1500 pixels
+  min_area: 1500 # Ignore plates smaller than 1500 pixels
   min_plate_length: 4 # Only recognize plates with 4 or more characters
   known_plates:
     Wife's Car:
@@ -161,13 +97,10 @@ lpr:
 ```yaml
 lpr:
   enabled: True
-  min_area: 4000 # Run recognition on larger plates only (4000 pixels represents a 63x63 pixel square in your image)
+  min_area: 4000 # Run recognition on larger plates only
   recognition_threshold: 0.85
   format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers
   match_distance: 1 # Allow one character variation in plate matching
-  replace_rules:
-    - pattern: "O"
-      replacement: "0" # Replace the letter O with the number 0 in every plate
   known_plates:
     Delivery Van:
       - "RJ K5678"
@@ -176,181 +109,22 @@ lpr:
       - "MN D3163"
 ```
 
-:::note
-
-If you want to detect cars on cameras but don't want to use resources to run LPR on those cars, you should disable LPR for those specific cameras.
-
-```yaml
-cameras:
-  side_yard:
-    lpr:
-      enabled: False
-    ...
-```
-
-:::
-
-## Dedicated LPR Cameras
-
-Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to capture license plates on distant vehicles, often with fine-tuned settings to capture plates at night.
-
-To mark a camera as a dedicated LPR camera, add `type: "lpr"` the camera configuration.
-
-:::note
-
-Frigate's dedicated LPR mode is optimized for cameras with a narrow field of view, specifically positioned and zoomed to capture license plates exclusively. If your camera provides a general overview of a scene rather than a tightly focused view, this mode is not recommended.
-
-:::
-
-Users can configure Frigate's dedicated LPR mode in two different ways depending on whether a Frigate+ (or native `license_plate` detecting) model is used:
-
-### Using a Frigate+ (or Native `license_plate` Detecting) Model
-
-Users running a Frigate+ model (or any model that natively detects `license_plate`) can take advantage of `license_plate` detection. This allows license plates to be treated as standard objects in dedicated LPR mode, meaning that alerts, detections, snapshots, and other Frigate features work as usual, and plates are detected efficiently through your configured object detector.
-
-An example configuration for a dedicated LPR camera using a `license_plate`-detecting model:
-
-```yaml
-# LPR global configuration
-lpr:
-  enabled: True
-  device: CPU # can also be GPU if available
-
-# Dedicated LPR camera configuration
-cameras:
-  dedicated_lpr_camera:
-    type: "lpr" # required to use dedicated LPR camera mode
-    ffmpeg: ... # add your streams
-    detect:
-      enabled: True
-      fps: 5 # increase to 10 if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended.
-      min_initialized: 2
-      width: 1920
-      height: 1080
-    objects:
-      track:
-        - license_plate
-      filters:
-        license_plate:
-          threshold: 0.7
-    motion:
-      threshold: 30
-      contour_area: 60 # use an increased value to tune out small motion changes
-      improve_contrast: false
-      mask: 0.704,0.007,0.709,0.052,0.989,0.055,0.993,0.001 # ensure your camera's timestamp is masked
-    record:
-      enabled: True # disable recording if you only want snapshots
-    snapshots:
-      enabled: True
-    review:
-      detections:
-        labels:
-          - license_plate
-```
-
-With this setup:
-
-- License plates are treated as normal objects in Frigate.
-- Scores, alerts, detections, and snapshots work as expected.
-- Snapshots will have license plate bounding boxes on them.
-- The `frigate/events` MQTT topic will publish tracked object updates.
-- Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
-
-### Using the Secondary LPR Pipeline (Without Frigate+)
-
-If you are not running a Frigate+ model, you can use Frigate’s built-in secondary dedicated LPR pipeline. In this mode, Frigate bypasses the standard object detection pipeline and runs a local license plate detector model on the full frame whenever motion activity occurs.
-
-An example configuration for a dedicated LPR camera using the secondary pipeline:
-
-```yaml
-# LPR global configuration
-lpr:
-  enabled: True
-  device: CPU # can also be GPU if available and correct Docker image is used
-  detection_threshold: 0.7 # change if necessary
-
-# Dedicated LPR camera configuration
-cameras:
-  dedicated_lpr_camera:
-    type: "lpr" # required to use dedicated LPR camera mode
-    lpr:
-      enabled: True
-      enhancement: 3 # optional, enhance the image before trying to recognize characters
-    ffmpeg: ... # add your streams
-    detect:
-      enabled: False # disable Frigate's standard object detection pipeline
-      fps: 5 # increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU
-      width: 1920
-      height: 1080
-    objects:
-      track: [] # required when not using a Frigate+ model for dedicated LPR mode
-    motion:
-      threshold: 30
-      contour_area: 60 # use an increased value here to tune out small motion changes
-      improve_contrast: false
-      mask: 0.704,0.007,0.709,0.052,0.989,0.055,0.993,0.001 # ensure your camera's timestamp is masked
-    record:
-      enabled: True # disable recording if you only want snapshots
-    review:
-      detections:
-        enabled: True
-        retain:
-          default: 7
-```
-
-With this setup:
-
-- The standard object detection pipeline is bypassed. Any detected license plates on dedicated LPR cameras are treated similarly to manual events in Frigate. You must **not** specify `license_plate` as an object to track.
-- The license plate detector runs on the full frame whenever motion is detected and processes frames according to your detect `fps` setting.
-- Review items will always be classified as a `detection`.
-- Snapshots will always be saved.
-- Zones and object masks are **not** used.
-- The `frigate/events` MQTT topic will **not** publish tracked object updates with the license plate bounding box and score, though `frigate/reviews` will publish if recordings are enabled. If a plate is recognized as a known plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
-- License plate snapshots are saved at the highest-scoring moment and appear in Explore.
-- Debug view will not show `license_plate` bounding boxes.
-
-### Summary
-
-| Feature                 | Native `license_plate` detecting Model (like Frigate+) | Secondary Pipeline (without native model or Frigate+)           |
-| ----------------------- | ------------------------------------------------------ | --------------------------------------------------------------- |
-| License Plate Detection | Uses `license_plate` as a tracked object               | Runs a dedicated LPR pipeline                                   |
-| FPS Setting             | 5 (increase for fast-moving cars)                      | 5 (increase for fast-moving cars, but it may use much more CPU) |
-| Object Detection        | Standard Frigate+ detection applies                    | Bypasses standard object detection                              |
-| Debug View              | May show `license_plate` bounding boxes                | May **not** show `license_plate` bounding boxes                 |
-| MQTT `frigate/events`   | Publishes tracked object updates                       | Publishes limited updates                                       |
-| Explore                 | Recognized plates available in More Filters            | Recognized plates available in More Filters                     |
-
-By selecting the appropriate configuration, users can optimize their dedicated LPR cameras based on whether they are using a Frigate+ model or the secondary LPR pipeline.
-
-### Best practices for using Dedicated LPR camera mode
-
-- Tune your motion detection and increase the `contour_area` until you see only larger motion boxes being created as cars pass through the frame (likely somewhere between 50-90 for a 1920x1080 detect stream). Increasing the `contour_area` filters out small areas of motion and will prevent excessive resource use from looking for license plates in frames that don't even have a car passing through it.
-- Disable the `improve_contrast` motion setting, especially if you are running LPR at night and the frame is mostly dark. This will prevent small pixel changes and smaller areas of motion from triggering license plate detection.
-- Ensure your camera's timestamp is covered with a motion mask so that it's not incorrectly detected as a license plate.
-- For non-Frigate+ users, you may need to change your camera settings for a clearer image or decrease your global `recognition_threshold` config if your plates are not being accurately recognized at night.
-- The secondary pipeline mode runs a local AI model on your CPU or GPU (depending on how `device` is configured) to detect plates. Increasing detect `fps` will increase resource usage proportionally.
-
 ## FAQ
 
 ### Why isn't my license plate being detected and recognized?
 
 Ensure that:
 
-- Your camera has a clear, human-readable, well-lit view of the plate. If you can't read the plate's characters, Frigate certainly won't be able to, even if the model is recognizing a `license_plate`. This may require changing video size, quality, or frame rate settings on your camera, depending on your scene and how fast the vehicles are traveling.
+- Your camera has a clear, human-readable, well-lit view of the plate. If you can't read the plate, Frigate certainly won't be able to. This may require changing video size, quality, or frame rate settings on your camera, depending on your scene and how fast the vehicles are traveling.
 - The plate is large enough in the image (try adjusting `min_area`) or increasing the resolution of your camera's stream.
-- Your `enhancement` level (if you've changed it from the default of `0`) is not too high. Too much enhancement will run too much denoising and cause the plate characters to become blurry and unreadable.
+- A `car` is detected first, as LPR only runs on recognized vehicles.
 
 If you are using a Frigate+ model or a custom model that detects license plates, ensure that `license_plate` is added to your list of objects to track.
 If you are using the free model that ships with Frigate, you should _not_ add `license_plate` to the list of objects to track.
 
-Recognized plates will show as object labels in the debug view and will appear in the "Recognized License Plates" select box in the More Filters popout in Explore.
+### Can I run LPR without detecting `car` objects?
 
-If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.
-
-### Can I run LPR without detecting `car` or `motorcycle` objects?
-
-In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+No, Frigate requires a `car` to be detected first before recognizing a license plate.
 
 ### How can I improve detection accuracy?
 
@@ -362,66 +136,17 @@ In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected firs
 
 Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.
 
-### Can I limit LPR to specific zones?
-
-LPR, like other Frigate enrichments, runs at the camera level rather than the zone level. While you can't restrict LPR to specific zones directly, you can control when recognition runs by setting a `min_area` value to filter out smaller detections.
-
 ### How can I match known plates with minor variations?
 
 Use `match_distance` to allow small character mismatches. Alternatively, define multiple variations in `known_plates`.
 
 ### How do I debug LPR issues?
 
-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. 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.
-
-     ```yaml
-     logger:
-       default: info
-       logs:
-         frigate.data_processing.common.license_plate: debug
-     ```
-
-2. Ensure your plates are being _detected_.
-
-   If you are using a Frigate+ or `license_plate` detecting model:
-
-   - Watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected.
-   - View MQTT messages for `frigate/events` to verify detected plates.
-   - You may need to adjust your `min_score` and/or `threshold` for the `license_plate` object if your plates are not being detected.
-
-   If you are **not** using a Frigate+ or `license_plate` detecting model:
-
-   - Watch the debug logs for messages from the YOLOv9 plate detector.
-   - You may need to adjust your `detection_threshold` if your plates are not being detected.
-
-3. Ensure the characters on detected plates are being _recognized_.
-
-   - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
-   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
-   - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
+- View MQTT messages for `frigate/events` to verify detected plates.
+- Adjust `detection_threshold` and `recognition_threshold` settings.
+- If you are using a Frigate+ model or a model that detects license plates, watch the debug view (Settings --> Debug) to ensure that `license_plate` is being detected with a `car`.
+- 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 enable this when necessary.
 
 ### Will LPR slow down my system?
 
-LPR's performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU or GPU for optimal results. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.
-
-### I am seeing a YOLOv9 plate detection metric in Enrichment Metrics, but I have a Frigate+ or custom model that detects `license_plate`. Why is the YOLOv9 model running?
-
-The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
-
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
-
-### It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?
-
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
-
-If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
-
-If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
-
-### I see "Error running ... model" in my logs. 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.
+LPR runs on the CPU, so performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU for optimal results.

docs/docs/configuration/live.md

@@ -23,7 +23,7 @@ If you are using go2rtc, you should adjust the following settings in your camera
 
 - Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
 - Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
+- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
 
 The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
 
@@ -42,16 +42,6 @@ go2rtc:
       - "ffmpeg:http_cam#audio=opus" # <- copy of the stream which transcodes audio to the missing codec (usually will be opus)
 ```
 
-If your camera does not support AAC audio or are having problems with Live view, try transcoding to AAC audio directly:
-
-```yaml
-go2rtc:
-  streams:
-    rtsp_cam: # <- for RTSP streams
-      - "ffmpeg:rtsp://192.168.1.5:554/live0#video=copy#audio=aac" # <- copies video stream and transcodes to aac audio
-      - "ffmpeg:rtsp_cam#audio=opus" # <- provides support for WebRTC
-```
-
 If your camera does not have audio and you are having problems with Live view, you should have go2rtc send video only:
 
 ```yaml
@@ -114,9 +104,9 @@ 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 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:
+- For internal/local access, unless you are running through the 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"
+  ```yaml title="/config/frigate.yaml"
   go2rtc:
     streams:
       test_cam: ...
@@ -131,9 +121,9 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
 
 :::tip
 
-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.
+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 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:
+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
@@ -172,12 +162,10 @@ For devices that support two way talk, Frigate can be configured to use the feat
 
 - Set up go2rtc with [WebRTC](#webrtc-extra-configuration).
 - Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
-- For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/2-way-audio) for the correct source.
+- For the Home Assistant Frigate card, [follow the docs](https://github.com/dermotduffy/frigate-hass-card?tab=readme-ov-file#using-2-way-audio) for the correct source.
 
 To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
 
-As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
-
 ### Streaming options on camera group dashboards
 
 Frigate provides a dialog in the Camera Group Edit pane with several options for streaming on a camera group's dashboard. These settings are _per device_ and are saved in your device's local storage.
@@ -191,12 +179,7 @@ Frigate provides a dialog in the Camera Group Edit pane with several options for
 
 :::note
 
-The default dashboard ("All Cameras") will always use:
-
-- Smart Streaming, unless you've disabled the global Automatic Live View in Settings.
-- The first entry set in your `streams` configuration, if defined.
-
-Use a camera group if you want to change any of these settings from the defaults.
+The default dashboard ("All Cameras") will always use Smart Streaming and the first entry set in your `streams` configuration, if defined. Use a camera group if you want to change any of these settings from the defaults.
 
 :::
 
@@ -204,12 +187,6 @@ Use a camera group if you want to change any of these settings from the defaults
 
 Cameras can be temporarily disabled through the Frigate UI and through [MQTT](/integrations/mqtt#frigatecamera_nameenabledset) to conserve system resources. When disabled, Frigate's ffmpeg processes are terminated — recording stops, object detection is paused, and the Live dashboard displays a blank image with a disabled message. Review items, tracked objects, and historical footage for disabled cameras can still be accessed via the UI.
 
-:::note
-
-Disabling a camera via the Frigate UI or MQTT is temporary and does not persist through restarts of Frigate.
-
-:::
-
 For restreamed cameras, go2rtc remains active but does not use system resources for decoding or processing unless there are active external consumers (such as the Advanced Camera Card in Home Assistant using a go2rtc source).
 
 Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
@@ -226,31 +203,9 @@ Note that disabling a camera through the config file (`enabled: False`) removes
 
    Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
 
-   When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
+   When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. You can also try using the _Reset_ button to force a reload of your stream.
 
-   If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
-
-   Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
-
-   - Network issues (e.g., MSE or WebRTC network connection problems).
-   - Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
-   - Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
-   - Browser compatibility problems (e.g., iOS Safari limitations with MSE).
-
-   To view browser console logs:
-
-   1. Open the Frigate Live View in your browser.
-   2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
-   3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
-   4. Look for messages prefixed with the camera name.
-
-   These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
-
-   - Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera_settings_recommendations)).
-   - Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
-   - Test with a different stream via the UI dropdown (if `live -> streams` is configured).
-   - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see (WebRTC Extra Configuration)(#webrtc-extra-configuration)).
-   - If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
+   If you are still experiencing Frigate falling back to low bandwidth mode, you may need to adjust your camera's settings per the recommendations above or ensure you have enough bandwidth available.
 
 3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
 
@@ -266,14 +221,8 @@ Note that disabling a camera through the config file (`enabled: False`) removes
 
    This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
 
-   Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
-
    This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
 
 6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
 
    If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
-
-7. **My camera streams have lots of visual artifacts / distortion.**
-
-   Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.

docs/docs/configuration/motion_detection.md

@@ -77,7 +77,7 @@ At this point if motion is working as desired there is no reason to continue wit
 
 Once daytime motion detection is tuned, there is a chance that the settings will work well for motion detection during the night as well. If this is the case then the preferred settings can be written to the config file and left alone.
 
-However, if the preferred day settings do not work well at night it is recommended to use Home Assistant or some other solution to automate changing the settings. That way completely separate sets of motion settings can be used for optimal day and night motion detection.
+However, if the preferred day settings do not work well at night it is recommended to use HomeAssistant or some other solution to automate changing the settings. That way completely separate sets of motion settings can be used for optimal day and night motion detection.
 
 ## Tuning For Large Changes In Motion
 
@@ -104,4 +104,4 @@ Lightning threshold does not stop motion based recordings from being saved.
 
 :::
 
-Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in a pause in object detection. This is done via the `lightning_threshold` configuration. It is defined as the percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera.
+Large changes in motion like PTZ moves and camera switches between Color and IR mode should result in no motion detection. This is done via the `lightning_threshold` configuration. It is defined as the percentage of the image used to detect lightning or other substantial changes where motion detection needs to recalibrate. Increasing this value will make motion detection more likely to consider lightning or IR mode changes as valid motion. Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching a doorbell camera.

docs/docs/configuration/pwa.md

@@ -20,5 +20,5 @@ In order to install Frigate as a PWA, the following requirements must be met:
 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
+- Android: Use the `Install as App` button in the more options menu
+- iOS: Use the `Add to Homescreen` button in the share menu

docs/docs/configuration/record.md

@@ -13,15 +13,14 @@ H265 recordings can be viewed in Chrome 108+, Edge and Safari only. All other br
 
 ### Most conservative: Ensure all video is saved
 
-For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion will be saved for 7 days. After 7 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
+For users deploying Frigate in environments where it is important to have contiguous video stored even if there was no detectable motion, the following config will store all video for 3 days. After 3 days, only video containing motion and overlapping with alerts or detections will be retained until 30 days have passed.
 
 ```yaml
 record:
   enabled: True
-  continuous:
+  retain:
     days: 3
-  motion:
-    days: 7
+    mode: all
   alerts:
     retain:
       days: 30
@@ -39,8 +38,9 @@ In order to reduce storage requirements, you can adjust your config to only reta
 ```yaml
 record:
   enabled: True
-  motion:
+  retain:
     days: 3
+    mode: motion
   alerts:
     retain:
       days: 30
@@ -58,7 +58,7 @@ If you only want to retain video that occurs during a tracked object, this confi
 ```yaml
 record:
   enabled: True
-  continuous:
+  retain:
     days: 0
   alerts:
     retain:
@@ -80,17 +80,15 @@ Retention configs support decimals meaning they can be configured to retain `0.5
 
 :::
 
-### Continuous and Motion Recording
+### Continuous Recording
 
-The number of days to retain continuous and motion recordings can be set via the following config where X is a number, by default continuous recording is disabled.
+The number of days to retain continuous recordings can be set via the following config where X is a number, by default continuous recording is disabled.
 
 ```yaml
 record:
   enabled: True
-  continuous:
+  retain:
     days: 1 # <- number of days to keep continuous recordings
-  motion:
-    days: 2 # <- number of days to keep motion recordings
 ```
 
 Continuous recording supports different retention modes [which are described below](#what-do-the-different-retain-modes-mean)
@@ -114,9 +112,41 @@ This configuration will retain recording segments that overlap with alerts and d
 
 **WARNING**: Recordings still must be enabled in the config. If a camera has recordings disabled in the config, enabling via the methods listed above will have no effect.
 
+## What do the different retain modes mean?
+
+Frigate saves from the stream with the `record` role in 10 second segments. These options determine which recording segments are kept for continuous recording (but can also affect tracked objects).
+
+Let's say you have Frigate configured so that your doorbell camera would retain the last **2** days of continuous recording.
+
+- With the `all` option all 48 hours of those two days would be kept and viewable.
+- With the `motion` option the only parts of those 48 hours would be segments that Frigate detected motion. This is the middle ground option that won't keep all 48 hours, but will likely keep all segments of interest along with the potential for some extra segments.
+- With the `active_objects` option the only segments that would be kept are those where there was a true positive object that was not considered stationary.
+
+The same options are available with alerts and detections, except it will only save the recordings when it overlaps with a review item of that type.
+
+A configuration example of the above retain modes where all `motion` segments are stored for 7 days and `active objects` are stored for 14 days would be as follows:
+
+```yaml
+record:
+  enabled: True
+  retain:
+    days: 7
+    mode: motion
+  alerts:
+    retain:
+      days: 14
+      mode: active_objects
+  detections:
+    retain:
+      days: 14
+      mode: active_objects
+```
+
+The above configuration example can be added globally or on a per camera basis.
+
 ## Can I have "continuous" recordings, but only at certain times?
 
-Using Frigate UI, Home Assistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
+Using Frigate UI, HomeAssistant, or MQTT, cameras can be automated to only record in certain situations or at certain times.
 
 ## How do I export recordings?
 
@@ -144,10 +174,6 @@ To reduce the output file size the ffmpeg parameter `-qp n` can be utilized (whe
 
 :::
 
-## Apple Compatibility with H.265 Streams
-
-Apple devices running the Safari browser may fail to playback h.265 recordings. The [apple compatibility option](../configuration/camera_specific.md#h265-cameras-via-safari) should be used to ensure seamless playback on Apple devices.
-
 ## Syncing Recordings With Disk
 
 In some cases the recordings files may be deleted but Frigate will not know this has happened. Recordings sync can be enabled which will tell Frigate to check the file system and delete any db entries for files which don't exist.

docs/docs/configuration/reference.md

@@ -73,38 +73,21 @@ tls:
   # Optional: Enable TLS for port 8971 (default: shown below)
   enabled: True
 
-# Optional: IPv6 configuration
-networking:
-  # Optional: Enable IPv6 on 5000, and 8971 if tls is configured (default: shown below)
-  ipv6:
-    enabled: False
-
 # Optional: Proxy configuration
 proxy:
   # Optional: Mapping for headers from upstream proxies. Only used if Frigate's auth
   # is disabled.
   # NOTE: Many authentication proxies pass a header downstream with the authenticated
-  #       user name and role. Not all values are supported. It must be a whitelisted header.
+  #       user name. Not all values are supported. It must be a whitelisted header.
   #       See the docs for more info.
   header_map:
     user: x-forwarded-user
-    role: x-forwarded-groups
-    role_map:
-      admin:
-        - sysadmins
-        - access-level-security
-      viewer:
-        - camera-viewer
   # Optional: Url for logging out a user. This sets the location of the logout url in
   # the UI.
   logout_url: /api/logout
   # Optional: Auth secret that is checked against the X-Proxy-Secret header sent from
   # the proxy. If not set, all requests are trusted regardless of origin.
   auth_secret: None
-  # Optional: The default role to use for proxy auth. Must be "admin" or "viewer"
-  default_role: viewer
-  # Optional: The character used to separate multiple values in the proxy headers. (default: shown below)
-  separator: ","
 
 # Optional: Authentication configuration
 auth:
@@ -142,7 +125,7 @@ auth:
 # NOTE: The default values are for the EdgeTPU detector.
 # Other detectors will require the model config to be set.
 model:
-  # Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
+  # Required: path to the model (default: automatic based on detector)
   path: /edgetpu_model.tflite
   # Required: path to the labelmap (default: shown below)
   labelmap_path: /labelmap.txt
@@ -287,9 +270,6 @@ detect:
   max_disappeared: 25
   # Optional: Configuration for stationary object tracking
   stationary:
-    # Optional: Stationary classifier that uses visual characteristics to determine if an object
-    # is stationary even if the box changes enough to be considered motion (default: shown below).
-    classifier: True
     # Optional: Frequency for confirming stationary objects (default: same as threshold)
     # When set to 1, object detection will run to confirm the object still exists on every frame.
     # If set to 10, object detection will run to confirm the object still exists on every 10th frame.
@@ -354,33 +334,6 @@ objects:
       # Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
       # Checks based on the bottom center of the bounding box of the object
       mask: 0.000,0.000,0.781,0.000,0.781,0.278,0.000,0.278
-  # Optional: Configuration for AI generated tracked object descriptions
-  genai:
-    # Optional: Enable AI object description generation (default: shown below)
-    enabled: False
-    # Optional: Use the object snapshot instead of thumbnails for description generation (default: shown below)
-    use_snapshot: False
-    # Optional: The default prompt for generating descriptions. Can use replacement
-    # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
-    prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
-    # Optional: Object specific prompts to customize description results
-    # Format: {label}: {prompt}
-    object_prompts:
-      person: "My special person prompt."
-    # Optional: objects to generate descriptions for (default: all objects that are tracked)
-    objects:
-      - person
-      - cat
-    # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify)
-    required_zones: []
-    # Optional: What triggers to use to send frames for a tracked object to generative AI (default: shown below)
-    send_triggers:
-      # Once the object is no longer tracked
-      tracked_object_end: True
-      # Optional: After X many significant updates are received (default: shown below)
-      after_significant_updates: None
-    # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below)
-    debug_save_thumbnails: False
 
 # Optional: Review configuration
 # NOTE: Can be overridden at the camera level
@@ -393,8 +346,6 @@ review:
     labels:
       - car
       - person
-    # Time to cutoff alerts after no alert-causing activity has occurred (default: shown below)
-    cutoff_time: 40
     # Optional: required zones for an object to be marked as an alert (default: none)
     # NOTE: when settings required zones globally, this zone must exist on all cameras
     #       or the config will be considered invalid. In that case the required_zones
@@ -409,27 +360,12 @@ review:
     labels:
       - car
       - person
-    # Time to cutoff detections after no detection-causing activity has occurred (default: shown below)
-    cutoff_time: 30
     # Optional: required zones for an object to be marked as a detection (default: none)
     # NOTE: when settings required zones globally, this zone must exist on all cameras
     #       or the config will be considered invalid. In that case the required_zones
     #       should be configured at the camera level.
     required_zones:
       - driveway
-  # Optional: GenAI Review Summary Configuration
-  genai:
-    # Optional: Enable the GenAI review summary feature (default: shown below)
-    enabled: False
-    # Optional: Enable GenAI review summaries for alerts (default: shown below)
-    alerts: True
-    # Optional: Enable GenAI review summaries for detections (default: shown below)
-    detections: False
-    # Optional: Additional concerns that the GenAI should make note of (default: None)
-    additional_concerns:
-      - Animals in the garden
-    # Optional: Preferred response language (default: English)
-    preferred_language: English
 
 # Optional: Motion configuration
 # NOTE: Can be overridden at the camera level
@@ -497,20 +433,20 @@ record:
   # Optional: Number of minutes to wait between cleanup runs (default: shown below)
   # This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
   expire_interval: 60
-  # Optional: Two-way sync recordings database with disk on startup and once a day (default: shown below).
+  # Optional: Sync recordings with disk on startup and once a day (default: shown below).
   sync_recordings: False
-  # Optional: Continuous retention settings
-  continuous:
-    # Optional: Number of days to retain recordings regardless of tracked objects or motion (default: shown below)
-    # NOTE: This should be set to 0 and retention should be defined in alerts and detections section below
-    #       if you only want to retain recordings of alerts and detections.
-    days: 0
-  # Optional: Motion retention settings
-  motion:
+  # Optional: Retention settings for recording
+  retain:
     # Optional: Number of days to retain recordings regardless of tracked objects (default: shown below)
     # NOTE: This should be set to 0 and retention should be defined in alerts and detections section below
     #       if you only want to retain recordings of alerts and detections.
     days: 0
+    # Optional: Mode for retention. Available options are: all, motion, and active_objects
+    #   all - save all recording segments regardless of activity
+    #   motion - save all recordings segments with any detected motion
+    #   active_objects - save all recording segments with active/moving objects
+    # NOTE: this mode only applies when the days setting above is greater than 0
+    mode: all
   # Optional: Recording Export Settings
   export:
     # Optional: Timelapse Output Args (default: shown below).
@@ -605,46 +541,19 @@ semantic_search:
   # Optional: Set the model size used for embeddings. (default: shown below)
   # NOTE: small model runs on CPU and large model runs on GPU
   model_size: "small"
-  # Optional: Target a specific device to run the model (default: shown below)
-  # NOTE: See https://onnxruntime.ai/docs/execution-providers/ for more information
-  device: None
 
 # Optional: Configuration for face recognition capability
-# NOTE: enabled, min_area can be overridden at the camera level
 face_recognition:
-  # Optional: Enable face recognition (default: shown below)
+  # Optional: Enable semantic search (default: shown below)
   enabled: False
-  # Optional: Minimum face distance score required to mark as a potential match (default: shown below)
-  unknown_score: 0.8
-  # Optional: Minimum face detection score required to detect a face (default: shown below)
-  # NOTE: This only applies when not running a Frigate+ model
-  detection_threshold: 0.7
-  # Optional: Minimum face distance score required to be considered a match (default: shown below)
-  recognition_threshold: 0.9
-  # Optional: Min area of detected face box to consider running face recognition (default: shown below)
-  min_area: 500
-  # Optional: Min face recognitions for the sub label to be applied to the person object (default: shown below)
-  min_faces: 1
-  # Optional: Number of images of recognized faces to save for training (default: shown below)
-  save_attempts: 100
-  # Optional: Apply a blur quality filter to adjust confidence based on the blur level of the image (default: shown below)
-  blur_confidence_filter: True
-  # Optional: Set the model size used face recognition. (default: shown below)
-  model_size: small
-  # Optional: Target a specific device to run the model (default: shown below)
-  # NOTE: See https://onnxruntime.ai/docs/execution-providers/ for more information
-  device: None
+  # Optional: Set the model size used for embeddings. (default: shown below)
+  # NOTE: small model runs on CPU and large model runs on GPU
+  model_size: "small"
 
 # Optional: Configuration for license plate recognition capability
-# NOTE: enabled, min_area, and enhancement can be overridden at the camera level
 lpr:
   # Optional: Enable license plate recognition (default: shown below)
   enabled: False
-  # Optional: The device to run the models on (default: shown below)
-  # NOTE: See https://onnxruntime.ai/docs/execution-providers/ for more information
-  device: CPU
-  # Optional: Set the model size used for text detection. (default: shown below)
-  model_size: small
   # Optional: License plate object confidence score required to begin running recognition (default: shown below)
   detection_threshold: 0.7
   # Optional: Minimum area of license plate to begin running recognition (default: shown below)
@@ -659,13 +568,6 @@ lpr:
   match_distance: 1
   # Optional: Known plates to track (strings or regular expressions) (default: shown below)
   known_plates: {}
-  # Optional: Enhance the detected plate image with contrast adjustment and denoising (default: shown below)
-  # A value between 0 and 10. Higher values are not always better and may perform worse than lower values.
-  enhancement: 0
-  # Optional: Save plate images to /media/frigate/clips/lpr for debugging purposes (default: shown below)
-  debug_save_plates: False
-  # Optional: List of regex replacement rules to normalize detected plates (default: shown below)
-  replace_rules: {}
 
 # Optional: Configuration for AI generated tracked object descriptions
 # WARNING: Depending on the provider, this will send thumbnails over the internet
@@ -680,27 +582,16 @@ genai:
   base_url: http://localhost::11434
   # Required if gemini or openai
   api_key: "{FRIGATE_GENAI_API_KEY}"
-  # Required if enabled: The model to use with the provider.
-  model: gemini-1.5-flash
-  # Optional additional args to pass to the GenAI Provider (default: None)
-  provider_options:
-    keep_alive: -1
-
-# Optional: Configuration for audio transcription
-# NOTE: only the enabled option can be overridden at the camera level
-audio_transcription:
-  # Optional: Enable license plate recognition (default: shown below)
-  enabled: False
-  # Optional: The device to run the models on (default: shown below)
-  device: CPU
-  # Optional: Set the model size used for transcription. (default: shown below)
-  model_size: small
-  # Optional: Set the language used for transcription translation. (default: shown below)
-  # List of language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
-  language: en
+  # Optional: The default prompt for generating descriptions. Can use replacement
+  # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
+  prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
+  # Optional: Object specific prompts to customize description results
+  # Format: {label}: {prompt}
+  object_prompts:
+    person: "My special person prompt."
 
 # Optional: Restream configuration
-# Uses https://github.com/AlexxIT/go2rtc (v1.9.10)
+# Uses https://github.com/AlexxIT/go2rtc (v1.9.2)
 # NOTE: The default go2rtc API port (1984) must be used,
 #       changing this port for the integrated go2rtc instance is not supported.
 go2rtc:
@@ -754,9 +645,6 @@ cameras:
     # If disabled: config is used but no live stream and no capture etc.
     # Events/Recordings are still viewable.
     enabled: True
-    # Optional: camera type used for some Frigate features (default: shown below)
-    # Options are "generic" and "lpr"
-    type: "generic"
     # Required: ffmpeg settings for the camera
     ffmpeg:
       # Required: A list of input streams for the camera. See documentation for more information.
@@ -906,22 +794,33 @@ cameras:
       # By default the cameras are sorted alphabetically.
       order: 0
 
-    # Optional: Configuration for triggers to automate actions based on semantic search results.
-    triggers:
-      # Required: Unique identifier for the trigger (generated automatically from friendly_name if not specified).
-      trigger_name:
-        # Required: Enable or disable the trigger. (default: shown below)
-        enabled: true
-        # Type of trigger, either `thumbnail` for image-based matching or `description` for text-based matching. (default: none)
-        type: thumbnail
-        # Reference data for matching, either an event ID for `thumbnail` or a text string for `description`. (default: none)
-        data: 1751565549.853251-b69j73
-        # Similarity threshold for triggering. (default: none)
-        threshold: 0.7
-        # List of actions to perform when the trigger fires. (default: none)
-        # Available options: `notification` (send a webpush notification)
-        actions:
-          - notification
+    # Optional: Configuration for AI generated tracked object descriptions
+    genai:
+      # Optional: Enable AI description generation (default: shown below)
+      enabled: False
+      # Optional: Use the object snapshot instead of thumbnails for description generation (default: shown below)
+      use_snapshot: False
+      # Optional: The default prompt for generating descriptions. Can use replacement
+      # variables like "label", "sub_label", "camera" to make more dynamic. (default: shown below)
+      prompt: "Describe the {label} in the sequence of images with as much detail as possible. Do not describe the background."
+      # Optional: Object specific prompts to customize description results
+      # Format: {label}: {prompt}
+      object_prompts:
+        person: "My special person prompt."
+      # Optional: objects to generate descriptions for (default: all objects that are tracked)
+      objects:
+        - person
+        - cat
+      # Optional: Restrict generation to objects that entered any of the listed zones (default: none, all zones qualify)
+      required_zones: []
+      # Optional: What triggers to use to send frames for a tracked object to generative AI (default: shown below)
+      send_triggers:
+        # Once the object is no longer tracked
+        tracked_object_end: True
+        # Optional: After X many significant updates are received (default: shown below)
+        after_significant_updates: None
+      # Optional: Save thumbnails sent to generative AI for review/debugging purposes (default: shown below)
+      debug_save_thumbnails: False
 
 # Optional
 ui:
@@ -971,12 +870,12 @@ telemetry:
     # Optional: Enable Intel GPU stats (default: shown below)
     intel_gpu_stats: True
     # Optional: Treat GPU as SR-IOV to fix GPU stats (default: shown below)
-    intel_gpu_device: None
+    sriov: False
     # Optional: Enable network bandwidth stats monitoring for camera ffmpeg processes, go2rtc, and object detectors. (default: shown below)
     # NOTE: The container must either be privileged or have cap_net_admin, cap_net_raw capabilities enabled.
     network_bandwidth: False
   # Optional: Enable the latest version outbound check (default: shown below)
-  # NOTE: If you use the Home Assistant integration, disabling this will prevent it from reporting new versions
+  # NOTE: If you use the HomeAssistant integration, disabling this will prevent it from reporting new versions
   version_check: True
 
 # Optional: Camera groups (default: no groups are setup)

docs/docs/configuration/restream.md

@@ -7,7 +7,7 @@ title: Restream
 
 Frigate can restream your video feed as an RTSP feed for other applications such as Home Assistant to utilize it at `rtsp://<frigate_host>:8554/<camera_name>`. Port 8554 must be open. [This allows you to use a video feed for detection in Frigate and Home Assistant live view at the same time without having to make two separate connections to the camera](#reduce-connections-to-camera). The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.
 
-Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.10) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#configuration) for more advanced configurations and features.
+Frigate uses [go2rtc](https://github.com/AlexxIT/go2rtc/tree/v1.9.2) to provide its restream and MSE/WebRTC capabilities. The go2rtc config is hosted at the `go2rtc` in the config, see [go2rtc docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#configuration) for more advanced configurations and features.
 
 :::note
 
@@ -134,7 +134,7 @@ cameras:
 
 ## Handling Complex Passwords
 
-go2rtc expects URL-encoded passwords in the config, [urlencoder.org](https://urlencoder.org) can be used for this purpose.
+go2rtc expects URL-encoded passwords in the config, [urlencoder.org](https://urlencoder.org) can be used for this purpose. 
 
 For example:
 
@@ -152,11 +152,11 @@ go2rtc:
     my_camera: rtsp://username:$%40foo%25@192.168.1.100
 ```
 
-See [this comment](https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-2242296489) for more information.
+See [this comment(https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-2242296489) for more information.
 
 ## 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. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.2#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
 NOTE: The output will need to be passed with two curly braces `{{output}}`
 

docs/docs/configuration/review.md

@@ -21,21 +21,6 @@ In 0.14 and later, all of that is bundled into a single review item which starts
 
 Not every segment of video captured by Frigate may be of the same level of interest to you. Video of people who enter your property may be a different priority than those walking by on the sidewalk. For this reason, Frigate 0.14 categorizes review items as _alerts_ and _detections_. By default, all person and car objects are considered alerts. You can refine categorization of your review items by configuring required zones for them.
 
-:::note
-
-Alerts and detections categorize the tracked objects in review items, but Frigate must first detect those objects with your configured object detector (Coral, OpenVINO, etc). By default, the object tracker only detects `person`. Setting `labels` for `alerts` and `detections` does not automatically enable detection of new objects. To detect more than `person`, you should add the following to your config:
-
-```yaml
-objects:
-  track:
-    - person
-    - car
-    - ...
-```
-
-See the [objects documentation](objects.md) for the list of objects that Frigate's default model tracks.
-:::
-
 ## Restricting alerts to specific labels
 
 By default a review item will only be marked as an alert if a person or car is detected. This can be configured to include any object or audio label using the following config:

docs/docs/configuration/semantic_search.md

@@ -19,7 +19,7 @@ For best performance, 16GB or more of RAM and a dedicated GPU are recommended.
 
 ## Configuration
 
-Semantic Search is disabled by default, and must be enabled in your config file or in the UI's Enrichments Settings page before it can be used. Semantic Search is a global configuration setting.
+Semantic Search is disabled by default, and must be enabled in your config file or in the UI's Settings page before it can be used. Semantic Search is a global configuration setting.
 
 ```yaml
 semantic_search:
@@ -29,9 +29,9 @@ semantic_search:
 
 :::tip
 
-The embeddings database can be re-indexed from the existing tracked objects in your database by pressing the "Reindex" button in the Enrichments Settings in the UI or by adding `reindex: True` to your `semantic_search` configuration and restarting Frigate. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing.
+The embeddings database can be re-indexed from the existing tracked objects in your database by adding `reindex: True` to your `semantic_search` configuration or by toggling the switch on the Search Settings page in the UI and restarting Frigate. Depending on the number of tracked objects you have, it can take a long while to complete and may max out your CPU while indexing. Make sure to turn the UI's switch off or set the config back to `False` before restarting Frigate again.
 
-If you are enabling Semantic Search for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to reindex as described above.
+If you are enabling Semantic Search for the first time, be advised that Frigate does not automatically index older tracked objects. You will need to enable the `reindex` feature in order to do that.
 
 :::
 
@@ -39,7 +39,7 @@ If you are enabling Semantic Search for the first time, be advised that Frigate
 
 The [V1 model from Jina](https://huggingface.co/jinaai/jina-clip-v1) has a vision model which is able to embed both images and text into the same vector space, which allows `image -> image` and `text -> image` similarity searches. Frigate uses this model on tracked objects to encode the thumbnail image and store it in the database. When searching for tracked objects via text in the search box, Frigate will perform a `text -> image` similarity search against this embedding. When clicking "Find Similar" in the tracked object detail pane, Frigate will perform an `image -> image` similarity search to retrieve the closest matching thumbnails.
 
-The V1 text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the object description docs](/configuration/genai/objects.md) for more information on how to automatically generate tracked object descriptions.
+The V1 text model is used to embed tracked object descriptions and perform searches against them. Descriptions can be created, viewed, and modified on the Explore page when clicking on thumbnail of a tracked object. See [the Generative AI docs](/configuration/genai.md) for more information on how to automatically generate tracked object descriptions.
 
 Differently weighted versions of the Jina models are available and can be selected by setting the `model_size` config option as `small` or `large`:
 
@@ -72,29 +72,37 @@ For most users, especially native English speakers, the V1 model remains the rec
 
 :::note
 
-Switching between V1 and V2 requires reindexing your embeddings. The embeddings from V1 and V2 are incompatible, and failing to reindex will result in incorrect search results.
+Switching between V1 and V2 requires reindexing your embeddings. To do this, set `reindex: True` in your Semantic Search configuration and restart Frigate. The embeddings from V1 and V2 are incompatible, and failing to reindex will result in incorrect search results.
 
 :::
 
 ### GPU Acceleration
 
-The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU / NPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
+The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used.
 
 ```yaml
 semantic_search:
   enabled: True
   model_size: large
-  # Optional, if using the 'large' model in a multi-GPU installation
-  device: 0
 ```
 
 :::info
 
-If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU / NPU will be detected and used automatically.
-Specify the `device` option to target a specific GPU in a multi-GPU system (see [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)).
-If you do not specify a device, the first available GPU will be used.
+If the correct build is used for your GPU and the `large` model is configured, then the GPU will be detected and used automatically.
 
-See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
+**NOTE:** Object detection and Semantic Search are independent features. If you want to use your GPU with Semantic Search, you must choose the appropriate Frigate Docker image for your GPU.
+
+- **AMD**
+
+  - ROCm will automatically be detected and used for Semantic Search in the `-rocm` Frigate image.
+
+- **Intel**
+
+  - OpenVINO will automatically be detected and used for Semantic Search in the default Frigate image.
+
+- **Nvidia**
+  - Nvidia GPUs will automatically be detected and used for Semantic Search in the `-tensorrt` Frigate image.
+  - Jetson devices will automatically be detected and used for Semantic Search in the `-tensorrt-jp(4/5)` Frigate image.
 
 :::
 
@@ -106,49 +114,3 @@ See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_
 4. Make your search language and tone closely match exactly what you're looking for. If you are using thumbnail search, **phrase your query as an image caption**. Searching for "red car" may not work as well as "red sedan driving down a residential street on a sunny day".
 5. Semantic search on thumbnails tends to return better results when matching large subjects that take up most of the frame. Small things like "cat" tend to not work well.
 6. Experiment! Find a tracked object you want to test and start typing keywords and phrases to see what works for you.
-
-## Triggers
-
-Triggers utilize semantic search to automate actions when a tracked object matches a specified image or description. Triggers can be configured so that Frigate executes a specific actions when a tracked object's image or description matches a predefined image or text, based on a similarity threshold. Triggers are managed per camera and can be configured via the Frigate UI in the Settings page under the Triggers tab.
-
-### Configuration
-
-Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires.
-
-#### Managing Triggers in the UI
-
-1. Navigate to the **Settings** page and select the **Triggers** tab.
-2. Choose a camera from the dropdown menu to view or manage its triggers.
-3. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
-4. In the **Create Trigger** dialog:
-   - Enter a **Name** for the trigger (e.g., "red_car_alert").
-   - Select the **Type** (`Thumbnail` or `Description`).
-   - For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
-   - For `Description`, enter text to trigger this action when a similar tracked object description is detected.
-   - Set the **Threshold** for similarity matching.
-   - Select **Actions** to perform when the trigger fires.
-5. Save the trigger to update the configuration and store the embedding in the database.
-
-When a trigger fires, the UI highlights the trigger with a blue outline for 3 seconds for easy identification.
-
-### Usage and Best Practices
-
-1. **Thumbnail Triggers**: Select a representative image (event ID) from the Explore page that closely matches the object you want to detect. For best results, choose images where the object is prominent and fills most of the frame.
-2. **Description Triggers**: Write concise, specific text descriptions (e.g., "Person in a red jacket") that align with the tracked object’s description. Avoid vague terms to improve matching accuracy.
-3. **Threshold Tuning**: Adjust the threshold to balance sensitivity and specificity. A higher threshold (e.g., 0.8) requires closer matches, reducing false positives but potentially missing similar objects. A lower threshold (e.g., 0.6) is more inclusive but may trigger more often.
-4. **Using Explore**: Use the context menu or right-click / long-press on a tracked object in the Grid View in Explore to quickly add a trigger based on the tracked object's thumbnail.
-5. **Editing triggers**: For the best experience, triggers should be edited via the UI. However, Frigate will ensure triggers edited in the config will be synced with triggers created and edited in the UI.
-
-### Notes
-
-- Triggers rely on the same Jina AI CLIP models (V1 or V2) used for semantic search. Ensure `semantic_search` is enabled and properly configured.
-- Reindexing embeddings (via the UI or `reindex: True`) does not affect trigger configurations but may update the embeddings used for matching.
-- For optimal performance, use a system with sufficient RAM (8GB minimum, 16GB recommended) and a GPU for `large` model configurations, as described in the Semantic Search requirements.
-
-### FAQ
-
-#### Why can't I create a trigger on thumbnails for some text, like "person with a blue shirt" and have it trigger when a person with a blue shirt is detected?
-
-TL;DR: Text-to-image triggers aren’t supported because CLIP can confuse similar images and give inconsistent scores, making automation unreliable.
-
-Text-to-image triggers are not supported due to fundamental limitations of CLIP-based similarity search. While CLIP works well for exploratory, manual queries, it is unreliable for automated triggers based on a threshold. Issues include embedding drift (the same text–image pair can yield different cosine distances over time), lack of true semantic grounding (visually similar but incorrect matches), and unstable thresholding (distance distributions are dataset-dependent and often too tightly clustered to separate relevant from irrelevant results). Instead, it is recommended to set up a workflow with thumbnail triggers: first use text search to manually select 3–5 representative reference tracked objects, then configure thumbnail triggers based on that visual similarity. This provides robust automation without the semantic ambiguity of text to image matching.

docs/docs/configuration/snapshots.md

@@ -5,7 +5,7 @@ title: Snapshots
 
 Frigate can save a snapshot image to `/media/frigate/clips` for each object that is detected named as `<camera>-<id>.jpg`. They are also accessible [via the api](../integrations/api/event-snapshot-events-event-id-snapshot-jpg-get.api.mdx)
 
-Snapshots are accessible in the UI in the Explore pane. This allows for quick submission to the Frigate+ service.
+For users with Frigate+ enabled, snapshots are accessible in the UI in the Frigate+ pane to allow for quick submission to the Frigate+ service.
 
 To only save snapshots for objects that enter a specific zone, [see the zone docs](./zones.md#restricting-snapshots-to-specific-zones)
 

docs/docs/configuration/tls.md

@@ -36,8 +36,8 @@ Note that certbot uses symlinks, and those can't be followed by the container un
 frigate:
   ...
   volumes:
-    - /etc/letsencrypt/live/your.fqdn.net:/etc/letsencrypt/live/frigate:ro
-    - /etc/letsencrypt/archive/your.fqdn.net:/etc/letsencrypt/archive/your.fqdn.net:ro
+    - /etc/letsencrypt/live/frigate:/etc/letsencrypt/live/frigate:ro
+    - /etc/letsencrypt/archive/frigate:/etc/letsencrypt/archive/frigate:ro
   ...
 
 ```

docs/docs/configuration/zones.md

@@ -84,15 +84,7 @@ Only car objects can trigger the `front_yard_street` zone and only person can tr
 
 ### Zone Loitering
 
-Sometimes objects are expected to be passing through a zone, but an object loitering in an area is unexpected. Zones can be configured to have a minimum loitering time after which the object will be considered in the zone.
-
-:::note
-
-When using loitering zones, a review item will behave in the following way:
-- When a person is in a loitering zone, the review item will remain active until the person leaves the loitering zone, regardless of if they are stationary.
-- When any other object is in a loitering zone, the review item will remain active until the loitering time is met. Then if the object is stationary the review item will end.
-
-:::
+Sometimes objects are expected to be passing through a zone, but an object loitering in an area is unexpected. Zones can be configured to have a minimum loitering time before the object will be considered in the zone.
 
 ```yaml
 cameras:
@@ -138,7 +130,7 @@ Your zone must be defined with exactly 4 points and should be aligned to the gro
 
 ![Ground plane 4-point zone](/img/ground-plane.jpg)
 
-Speed estimation requires a minimum number of frames for your object to be tracked before a valid estimate can be calculated, so create your zone away from places where objects enter and exit for the best results. The object's bounding box must be stable and remain a constant size as it enters and exits the zone. _Your zone should not take up the full frame, and the zone does **not** need to be the same size or larger than the objects passing through it._ An object's speed is tracked while it passes through the zone and then saved to Frigate's database.
+Speed estimation requires a minimum number of frames for your object to be tracked before a valid estimate can be calculated, so create your zone away from places where objects enter and exit for the best results. _Your zone should not take up the full frame._ An object's speed is tracked while it is in the zone and then saved to Frigate's database.
 
 Accurate real-world distance measurements are required to estimate speeds. These distances can be specified in your zone config through the `distances` field.
 
@@ -167,9 +159,8 @@ These speed values are output as a number in miles per hour (mph) or kilometers
 
 #### Best practices and caveats
 
-- Speed estimation works best with a straight road or path when your object travels in a straight line across that path. Avoid creating your zone near intersections or anywhere that objects would make a turn.
-- Create a zone where the bottom center of your object's bounding box travels directly through it and does not become obscured at any time.
-- A large zone can be used (as in the photo example above), but it may cause inaccurate estimation if the object's bounding box changes shape (such as when it turns or becomes partially hidden). Generally it's best to make your zone large enough to capture a few frames, but small enough so that the bounding box doesn't change size as it enters, travels through, and exits the zone.
+- Speed estimation works best with a straight road or path when your object travels in a straight line across that path. Avoid creating your zone near intersections or anywhere that objects would make a turn. If the bounding box changes shape (either because the object made a turn or became partially obscured, for example), speed estimation will not be accurate.
+- Create a zone where the bottom center of your object's bounding box travels directly through it and does not become obscured at any time. See the photo example above.
 - Depending on the size and location of your zone, you may want to decrease the zone's `inertia` value from the default of 3.
 - The more accurate your real-world dimensions can be measured, the more accurate speed estimation will be. However, due to the way Frigate's tracking algorithm works, you may need to tweak the real-world distance values so that estimated speeds better match real-world speeds.
 - Once an object leaves the zone, speed accuracy will likely decrease due to perspective distortion and misalignment with the calibrated area. Therefore, speed values will show as a zero through MQTT and will not be visible on the debug view when an object is outside of a speed tracking zone.

docs/docs/development/contributing-boards.md

@@ -72,17 +72,17 @@ COPY --from=rootfs / /
 The images for each board will be built for each Frigate release, this is done in the `.github/workflows/ci.yml` file. The board build workflow will need to be added here.
 
 ```yml
-- name: Build and push board build
-  uses: docker/bake-action@v3
-  with:
-    push: true
-    targets: board # this is the target in the board.hcl file
-    files: docker/board/board.hcl # this should be updated with the actual board type
-    # the tags should be updated with the actual board types as well
-    # the community board builds should never push to cache, but it can pull from cache
-    set: |
-      board.tags=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ github.ref_name }}-${{ env.SHORT_SHA }}-board
-      *.cache-from=type=gha
+      - name: Build and push board build
+        uses: docker/bake-action@v3
+        with:
+          push: true
+          targets: board # this is the target in the board.hcl file
+          files: docker/board/board.hcl # this should be updated with the actual board type
+          # the tags should be updated with the actual board types as well
+          # the community board builds should never push to cache, but it can pull from cache
+          set: |
+            board.tags=ghcr.io/${{ steps.lowercaseRepo.outputs.lowercase }}:${{ github.ref_name }}-${{ env.SHORT_SHA }}-board
+            *.cache-from=type=gha
 ```
 
 ### Code Owner File
@@ -91,4 +91,4 @@ The `CODEOWNERS` file should be updated to include the `docker/board` along with
 
 # Docs
 
-At a minimum the `installation`, `object_detectors`, `hardware_acceleration_video`, and `ffmpeg-presets` docs should be updated (if applicable) to reflect the configuration of this community board.
+At a minimum the `installation`, `object_detectors`, `hardware_acceleration`, and `ffmpeg-presets` docs should be updated (if applicable) to reflect the configuration of this community board.

docs/docs/development/contributing.md

@@ -17,15 +17,15 @@ From here, follow the guides for:
 - [Web Interface](#web-interface)
 - [Documentation](#documentation)
 
-### Frigate Home Assistant Add-on
+### Frigate Home Assistant Addon
 
-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.
+This repository holds the Home Assistant Addon, 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 Add-on](#frigate-home-assistant-add-on).
+This repository holds the custom integration that allows your Home Assistant installation to automatically create entities for your Frigate instance, whether you run that with the [addon](#frigate-home-assistant-addon) or in a separate Docker instance.
 
 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.
 
@@ -77,15 +77,14 @@ Create and place these files in a `debug` folder in the root of the repo. This i
 
 #### 4. Run Frigate from the command line
 
-VS Code will start the Docker Compose file for you and open a terminal window connected to `frigate-dev`.
+VSCode will start the docker compose file for you and open a terminal window connected to `frigate-dev`.
 
-- Depending on what hardware you're developing on, you may need to amend `docker-compose.yml` in the project root to pass through a USB Coral or GPU for hardware acceleration.
 - Run `python3 -m frigate` to start the backend.
 - In a separate terminal window inside VS Code, change into the `web` directory and run `npm install && npm run dev` to start the frontend.
 
 #### 5. Teardown
 
-After closing VS Code, you may still have containers running. To close everything down, just run `docker-compose down -v` to cleanup all containers.
+After closing VSCode, you may still have containers running. To close everything down, just run `docker-compose down -v` to cleanup all containers.
 
 ### Testing
 
@@ -236,11 +235,3 @@ When testing nginx config changes from within the dev container, the following c
 ```console
 sudo cp docker/main/rootfs/usr/local/nginx/conf/* /usr/local/nginx/conf/ && sudo /usr/local/nginx/sbin/nginx -s reload
 ```
-
-## Contributing translations of the Web UI
-
-Frigate uses [Weblate](https://weblate.org) to manage translations of the Web UI. To contribute translation, sign up for an account at Weblate and navigate to the Frigate NVR project:
-
-https://hosted.weblate.org/projects/frigate-nvr/
-
-When translating, maintain the existing key structure while translating only the values. Ensure your translations maintain proper formatting, including any placeholder variables (like `{{example}}`).

docs/docs/frigate/camera_setup.md

@@ -3,7 +3,7 @@ id: camera_setup
 title: Camera setup
 ---
 
-Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Firefox 134+/136+/137+ (Windows/Mac/Linux & Android), Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
+Cameras configured to output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. H.265 has better compression, but less compatibility. Chrome 108+, Safari and Edge are the only browsers able to play H.265 and only support a limited number of H.265 profiles. Ideally, cameras should be configured directly for the desired resolutions and frame rates you want to use in Frigate. Reducing frame rates within Frigate will waste CPU resources decoding extra frames that are discarded. There are three different goals that you want to tune your stream configurations around.
 
 - **Detection**: This is the only stream that Frigate will decode for processing. Also, this is the stream where snapshots will be generated from. The resolution for detection should be tuned for the size of the objects you want to detect. See [Choosing a detect resolution](#choosing-a-detect-resolution) for more details. The recommended frame rate is 5fps, but may need to be higher (10fps is the recommended maximum for most users) for very fast moving objects. Higher resolutions and frame rates will drive higher CPU usage on your server.
 
@@ -28,7 +28,7 @@ For the Dahua/Loryta 5442 camera, I use the following settings:
 - Encode Mode: H.264
 - Resolution: 2688\*1520
 - Frame Rate(FPS): 15
-- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](/configuration/live#camera_settings_recommendations) for more info)
+- I Frame Interval: 30 (15 can also be used to prioritize streaming performance - see the [camera settings recommendations](../configuration/live) for more info)
 
 **Sub Stream (Detection)**
 

docs/docs/frigate/glossary.md

@@ -66,4 +66,4 @@ The time period starting when a tracked object entered the frame and ending when
 
 ## Zone
 
-Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create a [review item](#review-item). [See the zone docs for more info](/configuration/zones)
+Zones are areas of interest, zones can be used for notifications and for limiting the areas where Frigate will create an [event](#event). [See the zone docs for more info](/configuration/zones)

docs/docs/frigate/hardware.md

@@ -9,257 +9,111 @@ Cameras that output H.264 video and AAC audio will offer the most compatibility
 
 I recommend Dahua, Hikvision, and Amcrest in that order. Dahua edges out Hikvision because they are easier to find and order, not because they are better cameras. I personally use Dahua cameras because they are easier to purchase directly. In my experience Dahua and Hikvision both have multiple streams with configurable resolutions and frame rates and rock solid streams. They also both have models with large sensors well known for excellent image quality at night. Not all the models are equal. Larger sensors are better than higher resolutions; especially at night. Amcrest is the fallback recommendation because they are rebranded Dahuas. They are rebranding the lower end models with smaller sensors or less configuration options.
 
-WiFi cameras are not recommended as [their streams are less reliable and cause connection loss and/or lost video data](https://ipcamtalk.com/threads/camera-conflicts.68142/#post-738821), especially when more than a few WiFi cameras will be used at the same time.
+Many users have reported various issues with Reolink cameras, so I do not recommend them. If you are using Reolink, I suggest the [Reolink specific configuration](../configuration/camera_specific.md#reolink-cameras). Wifi cameras are also not recommended. Their streams are less reliable and cause connection loss and/or lost video data.
 
-Many users have reported various issues with 4K-plus Reolink cameras, it is best to stick with 5MP and lower for Reolink cameras. If you are using Reolink, I suggest the [Reolink specific configuration](../configuration/camera_specific.md#reolink-cameras).
-
-Here are some of the cameras I recommend:
+Here are some of the camera's 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://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 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).
-
-I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
-
-:::warning
-
-If the EQ13 is out of stock, the link below may take you to a suggested alternative on Amazon. The Beelink EQ14 has some known compatibility issues, so you should avoid that model for now.
-
-:::
+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. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
 | 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. |
+| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" 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
 
-A detector is a device which is optimized for running inferences efficiently to detect objects. Using a recommended detector means there will be less latency between detections and more detections can be run per second. Frigate is designed around the expectation that a detector is used to achieve very low inference speeds. Offloading TensorFlow to a detector is an order of magnitude faster and will reduce your CPU load dramatically.
-
-:::info
-
-Frigate supports multiple different detectors that work on different types of hardware:
-
-**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)
-
-- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
-  - [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
-  - Runs best with tiny, small, or medium-size models
-
-**AMD**
-
-- [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
-  - [Supports limited model architectures](../../configuration/object_detectors#rocm-supported-models)
-  - Runs best on discrete AMD GPUs
-
-**Apple Silicon**
-
-- [Apple Silicon](#apple-silicon): Apple Silicon is usable on all M1 and newer Apple Silicon devices to provide efficient and fast object detection
-  - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-supported-models)
-  - Runs well with any size models including large
-  - Runs via ZMQ proxy which adds some latency, only recommended for local connection
-
-**Intel**
-
-- [OpenVino](#openvino---intel): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
-  - [Supports majority of model architectures](../../configuration/object_detectors#openvino-supported-models)
-  - Runs best with tiny, small, or medium models
-
-**Nvidia**
-
-- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices.
-  - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
-  - Runs well with any size models including large
-
-**Rockchip**
-
-- [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
-  - [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
-  - Runs best with tiny or small size models
-  - Runs efficiently on low power hardware
-
-**Synaptics**
-
-- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
-
-:::
-
-### Synaptics
-
-- **Synaptics** Default model is **mobilenet**
-
-| Name             | Synaptics SL1680 Inference Time |
-| ---------------- | ------------------------------- |
-| ssd mobilenet    | ~ 25 ms                         |
-| yolov5m          | ~ 118 ms                        |
-
-### Hailo-8
-
-Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
-
-**Default Model Configuration:**
-
-- **Hailo-8L:** Default model is **YOLOv6n**.
-- **Hailo-8:** Default model is **YOLOv6n**.
-
-In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
-
-| Name             | Hailo‑8 Inference Time | Hailo‑8L Inference Time |
-| ---------------- | ---------------------- | ----------------------- |
-| ssd mobilenet v1 | ~ 6 ms                 | ~ 10 ms                 |
-| yolov9-tiny      |                        | 320: 18ms               |
-| yolov6n          | ~ 7 ms                 | ~ 11 ms                 |
+A detector is a device which is optimized for running inferences efficiently to detect objects. Using a recommended detector means there will be less latency between detections and more detections can be run per second. Frigate is designed around the expectation that a detector is used to achieve very low inference speeds. Offloading TensorFlow to a detector is an order of magnitude faster and will reduce your CPU load dramatically. As of 0.12, Frigate supports a handful of different detector types with varying inference speeds and performance.
 
 ### Google Coral TPU
 
-Frigate supports both the USB and M.2 versions of the Google Coral.
+It is strongly recommended to use a Google Coral. A $60 device will outperform $2000 CPU. Frigate should work with any supported Coral device from https://coral.ai
 
-- 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. Follow the instructions for your version from https://coral.ai
+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. 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.
 
-### OpenVINO - Intel
+### OpenVINO
 
 The OpenVINO detector type is able to run on:
 
 - 6th Gen Intel Platforms and newer that have an iGPU
-- x86 hosts with an Intel Arc GPU
+- x86 & Arm64 hosts with VPU Hardware (ex: Intel NCS2)
 - Most modern AMD CPUs (though this is officially not supported by Intel)
-- x86 & Arm64 hosts via CPU (generally not recommended)
-
-:::note
-
-Intel NPUs have seen [limited success in community deployments](https://github.com/blakeblackshear/frigate/discussions/13248#discussioncomment-12347357), although they remain officially unsupported.
-
-In testing, the NPU delivered performance that was only comparable to — or in some cases worse than — the integrated GPU.
-
-:::
 
 More information is available [in the detector docs](/configuration/object_detectors#openvino-detector)
 
 Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:
 
-| Name           | MobileNetV2 Inference Time | YOLOv9                                            | YOLO-NAS Inference Time   | RF-DETR Inference Time | Notes                              |
-| -------------- | -------------------------- | ------------------------------------------------- | ------------------------- | ---------------------- | ---------------------------------- |
-| Intel HD 530   | 15 - 35 ms                 |                                                   |                           |                        | Can only run one detector instance |
-| Intel HD 620   | 15 - 25 ms                 |                                                   | 320: ~ 35 ms              |                        |                                    |
-| Intel HD 630   | ~ 15 ms                    |                                                   | 320: ~ 30 ms              |                        |                                    |
-| Intel UHD 730  | ~ 10 ms                    |                                                   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
-| Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
-| Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
-| Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
-| Intel Iris XE  | ~ 10 ms                    | s-320: 12 ms s-640: 30 ms                         | 320: ~ 18 ms 640: ~ 50 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               |                        |                                    |
+| Name                 | MobileNetV2 Inference Time | YOLO-NAS Inference Time   | Notes                                  |
+| -------------------- | -------------------------- | ------------------------- | -------------------------------------- |
+| Intel Celeron J4105  | ~ 25 ms                    |                           | Can only run one detector instance     |
+| Intel Celeron N3060  | 130 - 150 ms               |                           | Can only run one detector instance     |
+| Intel Celeron N3205U | ~ 120 ms                   |                           | Can only run one detector instance     |
+| Intel Celeron N4020  | 50 - 200 ms                |                           | Inference speed depends on other loads |
+| Intel i3 6100T       | 15 - 35 ms                 |                           | Can only run one detector instance     |
+| Intel i3 8100        | ~ 15 ms                    |                           |                                        |
+| Intel i5 4590        | ~ 20 ms                    |                           |                                        |
+| Intel i5 6500        | ~ 15 ms                    |                           |                                        |
+| Intel i5 7200u       | 15 - 25 ms                 |                           |                                        |
+| Intel i5 7500        | ~ 15 ms                    |                           |                                        |
+| Intel i5 1135G7      | 10 - 15 ms                 |                           |                                        |
+| Intel i3 12000       |                            | 320: ~ 19 ms 640: ~ 54 ms |                                        |
+| Intel i5 12600K      | ~ 15 ms                    | 320: ~ 20 ms 640: ~ 46 ms |                                        |
+| Intel Arc A380       | ~ 6 ms                     | 320: ~ 10 ms              |                                        |
+| Intel Arc A750       | ~ 4 ms                     | 320: ~ 8 ms               |                                        |
 
 ### TensorRT - Nvidia GPU
 
-Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA libraries.
-
-#### Minimum Hardware Support
-
-12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
-
-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/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)
+The TensortRT detector is able to run on x86 hosts that have an Nvidia GPU which supports the 12.x series of CUDA libraries. The minimum driver version on the host system must be `>=525.60.13`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the [TensorRT docs for more info](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 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:
+`tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
 
-✅ - Accelerated with CUDA Graphs
-❌ - Not accelerated with CUDA Graphs
+| Name            | YoloV7 Inference Time | YOLO-NAS Inference Time   |
+| --------------- | --------------------- | ------------------------- |
+| GTX 1060 6GB    | ~ 7 ms                |                           |
+| GTX 1070        | ~ 6 ms                |                           |
+| GTX 1660 SUPER  | ~ 4 ms                |                           |
+| RTX 3050        | 5 - 7 ms              | 320: ~ 10 ms 640: ~ 16 ms |
+| RTX 3070 Mobile | ~ 5 ms                |                           |
+| Quadro P400 2GB | 20 - 25 ms            |                           |
+| Quadro P2000    | ~ 12 ms               |                           |
 
-| Name      | ✅ YOLOv9 Inference Time              | ✅ RF-DETR Inference Time | ❌ YOLO-NAS Inference Time |
-| --------- | ------------------------------------- | ------------------------- | -------------------------- |
-| GTX 1070  | s-320: 16 ms                          |                           | 320: 14 ms                 |
-| RTX 3050  | t-320: 8 ms s-320: 10 ms s-640: 28 ms | Nano-320: ~ 12 ms         | 320: ~ 10 ms 640: ~ 16 ms  |
-| RTX 3070  | t-320: 6 ms s-320: 8 ms s-640: 25 ms  | Nano-320: ~ 9 ms          | 320: ~ 8 ms 640: ~ 14 ms   |
-| RTX A4000 |                                       |                           | 320: ~ 15 ms               |
-| Tesla P40 |                                       |                           | 320: ~ 105 ms              |
+### AMD GPUs
 
-### Apple Silicon
+With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
 
-With the [Apple Silicon](../configuration/object_detectors.md#apple-silicon-detector) detector Frigate can take advantage of the NPU in M1 and newer Apple Silicon.
+### Hailo-8
 
-:::warning
+| Name            | Hailo‑8 Inference Time | Hailo‑8L Inference Time |
+| --------------- | ---------------------- | ----------------------- |
+| ssd mobilenet v1| ~ 6 ms                 | ~ 10 ms                 |
+| yolov6n         | ~ 7 ms                 | ~ 11 ms                 |
 
-Apple Silicon can not run within a container, so a ZMQ proxy is utilized to communicate with [the Apple Silicon Frigate detector](https://github.com/frigate-nvr/apple-silicon-detector) which runs on the host. This should add minimal latency when run on the same device.
 
-:::
+Frigate supports both the Hailo-8 and Hailo-8L AI Acceleration Modules on compatible hardware platforms—including the Raspberry Pi 5 with the PCIe hat from the AI kit. The Hailo detector integration in Frigate automatically identifies your hardware type and selects the appropriate default model when a custom model isn’t provided.
 
-| Name   | YOLOv9 Inference Time                |
-| ------ | ------------------------------------ |
-| M4     | s-320: 10 ms                         |
-| M3 Pro | t-320: 6 ms s-320: 8 ms s-640: 20 ms |
-| M1     | s-320: 9ms                           |
+**Default Model Configuration:**
+- **Hailo-8L:** Default model is **YOLOv6n**.
+- **Hailo-8:** Default model is **YOLOv6n**.
 
-### ROCm - AMD GPU
+In real-world deployments, even with multiple cameras running concurrently, Frigate has demonstrated consistent performance. Testing on x86 platforms—with dual PCIe lanes—yields further improvements in FPS, throughput, and latency compared to the Raspberry Pi setup.
 
-With the [ROCm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
-
-| Name      | YOLOv9 Inference Time       | YOLO-NAS Inference Time   |
-| --------- | --------------------------- | ------------------------- |
-| AMD 780M  | t-320: ~ 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms |
-| AMD 8700G |                             | 320: ~ 20 ms 640: ~ 40 ms |
 
 ## Community Supported Detectors
 
-### MemryX MX3
-
-Frigate supports the MemryX MX3 M.2 AI Acceleration Module on compatible hardware platforms, including both x86 (Intel/AMD) and ARM-based SBCs such as Raspberry Pi 5.
-
-A single MemryX MX3 module is capable of handling multiple camera streams using the default models, making it sufficient for most users. For larger deployments with more cameras or bigger models, multiple MX3 modules can be used. Frigate supports multi-detector configurations, allowing you to connect multiple MX3 modules to scale inference capacity.
-
-Detailed information is available [in the detector docs](/configuration/object_detectors#memryx-mx3).
-
-**Default Model Configuration:**
-
-- Default model is **YOLO-NAS-Small**.
-
-The MX3 is a pipelined architecture, where the maximum frames per second supported (and thus supported number of cameras) cannot be calculated as `1/latency` (1/"Inference Time") and is measured separately. When estimating how many camera streams you may support with your configuration, use the **MX3 Total FPS** column to approximate of the detector's limit, not the Inference Time.
-
-| Model                | Input Size | MX3 Inference Time | MX3 Total FPS |
-| -------------------- | ---------- | ------------------ | ------------- |
-| YOLO-NAS-Small       | 320        | ~ 9 ms             | ~ 378         |
-| YOLO-NAS-Small       | 640        | ~ 21 ms            | ~ 138         |
-| YOLOv9s              | 320        | ~ 16 ms            | ~ 382         |
-| YOLOv9s              | 640        | ~ 41 ms            | ~ 110         |
-| YOLOX-Small          | 640        | ~ 16 ms            | ~ 263         |
-| SSDlite MobileNet v2 | 320        | ~ 5 ms             | ~ 1056        |
-
-Inference speeds may vary depending on the host platform. The above data was measured on an **Intel 13700 CPU**. Platforms like Raspberry Pi, Orange Pi, and other ARM-based SBCs have different levels of processing capability, which may limit total FPS.
-
 ### Nvidia Jetson
 
-Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
+Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
@@ -273,11 +127,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 - RK3576
 - RK3588
 
-| Name           | YOLOv9 Inference Time | YOLO-NAS Inference Time     | YOLOx Inference Time    |
-| -------------- | --------------------- | --------------------------- | ----------------------- |
-| rk3588 3 cores | tiny: ~ 35 ms         | small: ~ 20 ms med: ~ 30 ms | nano: 14 ms tiny: 18 ms |
-| rk3566 1 core  |                       | small: ~ 96 ms              |                         |
-
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
@@ -300,4 +149,4 @@ Basically - When you increase the resolution and/or the frame rate of the stream
 
 YES! The Coral does not help with decoding video streams.
 
-Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://support.video.ibm.com/hc/en-us/articles/18106203580316-Keyframes-InterFrame-Video-Compression). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.
+Decompressing video streams takes a significant amount of CPU power. Video compression uses key frames (also known as I-frames) to send a full frame in the video stream. The following frames only include the difference from the key frame, and the CPU has to compile each frame by merging the differences with the key frame. [More detailed explanation](https://blog.video.ibm.com/streaming-video-tips/keyframes-interframe-video-compression/). Higher resolutions and frame rates mean more processing power is needed to decode the video stream, so try and set them on the camera to avoid unnecessary decoding work.

docs/docs/frigate/index.md

@@ -6,7 +6,7 @@ slug: /
 
 A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
-Use of a [Recommended Detector](/frigate/hardware#detectors) is optional, but strongly recommended. CPU detection should only be used for testing purposes.
+Use of a [Google Coral Accelerator](https://coral.ai/products/) is optional, but strongly recommended. CPU detection should only be used for testing purposes. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.
 
 - Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary

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 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.
+Frigate is a Docker container that can be run on any Docker host including as a [HassOS Addon](https://www.home-assistant.io/addons/). Note that a Home Assistant Addon is **not** the same thing as the integration. The [integration](/integrations/home-assistant) is required to integrate Frigate into Home Assistant.
 
 :::tip
 
-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.
+If you already have Frigate installed as a Home Assistant addon, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
 
 :::
 
@@ -43,9 +43,9 @@ The following ports are used by Frigate and can be mapped via docker as required
 | `8971` | Authenticated UI and API access without TLS. Reverse proxies should use this port.                                                                                         |
 | `5000` | Internal unauthenticated UI and API access. Access to this port should be limited. Intended to be used within the docker network for services that integrate with Frigate. |
 | `8554` | RTSP restreaming. By default, these streams are unauthenticated. Authentication can be configured in go2rtc section of config.                                             |
-| `8555` | WebRTC connections for cameras with two-way talk support.                                                                                                                  |
+| `8555` | WebRTC connections for low latency live views.                                                                                                                             |
 
-#### Common Docker Compose storage configurations
+#### Common docker compose storage configurations
 
 Writing to a local disk or external USB drive:
 
@@ -73,7 +73,7 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your
 
 Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.
 
-The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose).
+The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in docker-compose).
 
 The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
 
@@ -132,77 +132,6 @@ If you are using `docker run`, add this option to your command `--device /dev/ha
 
 Finally, configure [hardware object detection](/configuration/object_detectors#hailo-8l) to complete the setup.
 
-### MemryX MX3  
-
-The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVMe SSD), and supports a variety of configurations:
-- x86 (Intel/AMD) PCs
-- Raspberry Pi 5
-- Orange Pi 5 Plus/Max
-- Multi-M.2 PCIe carrier cards
-
-#### Configuration  
-
-
-#### Installation  
-
-To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/get_started/hardware_setup.html).
-
-Then follow these steps for installing the correct driver/runtime configuration:
-
-1. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/memryx/user_installation.sh).
-2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
-3. Run the script with `./user_installation.sh`
-4. **Restart your computer** to complete driver installation.
-
-#### Setup  
-
-To set up Frigate, follow the default installation instructions, for example:   `ghcr.io/blakeblackshear/frigate:stable`
-
-Next, grant Docker permissions to access your hardware by adding the following lines to your `docker-compose.yml` file:
-
-```yaml
-devices:
-  - /dev/memx0
-```
-
-During configuration, you must run Docker in privileged mode and ensure the container can access the max-manager.
-
-In your `docker-compose.yml`, also add:
-
-```yaml
-privileged: true
-
-volumes:
-    /run/mxa_manager:/run/mxa_manager
-```
-
-If you can't use Docker Compose, you can run the container with something similar to this:
-
-```bash
-  docker run -d \
-    --name frigate-memx \
-    --restart=unless-stopped \
-    --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000 \
-    --shm-size=256m \
-    -v /path/to/your/storage:/media/frigate \
-    -v /path/to/your/config:/config \
-    -v /etc/localtime:/etc/localtime:ro \
-    -v /run/mxa_manager:/run/mxa_manager \
-    -e FRIGATE_RTSP_PASSWORD='password' \
-    --privileged=true \
-    -p 8971:8971 \
-    -p 8554:8554 \
-    -p 5000:5000 \
-    -p 8555:8555/tcp \
-    -p 8555:8555/udp \
-    --device /dev/memx0 \
-    ghcr.io/blakeblackshear/frigate:stable
-```
-
-#### Configuration
-
-Finally, configure [hardware object detection](/configuration/object_detectors#memryx-mx3) to complete the setup.
-
 ### Rockchip platform
 
 Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and necessary drivers (especially rkvdec2 and rknpu). To check, enter the following commands:
@@ -216,7 +145,7 @@ $ sudo cat /sys/kernel/debug/rknpu/version
 RKNPU driver: v0.9.2 # or later version
 ```
 
-I recommend [Armbian](https://www.armbian.com/download/?arch=aarch64), if your board is supported.
+I recommend [Joshua Riek's Ubuntu for Rockchip](https://github.com/Joshua-Riek/ubuntu-rockchip), if your board is supported.
 
 #### Setup
 
@@ -236,8 +165,6 @@ devices:
   - /dev/dma_heap
   - /dev/rga
   - /dev/mpp_service
-volumes:
-  - /sys/:/sys/:ro
 ```
 
 or add these options to your `docker run` command:
@@ -248,50 +175,19 @@ or add these options to your `docker run` command:
 --device /dev/dri \
 --device /dev/dma_heap \
 --device /dev/rga \
---device /dev/mpp_service \
---volume /sys/:/sys/:ro
+--device /dev/mpp_service
 ```
 
 #### Configuration
 
-Next, you should configure [hardware object detection](/configuration/object_detectors#rockchip-platform) and [hardware video processing](/configuration/hardware_acceleration_video#rockchip-platform).
-
-### Synaptics
-
-- SL1680
-
-#### Setup
-
-Follow Frigate's default installation instructions, but use a docker image with `-synaptics` suffix for example `ghcr.io/blakeblackshear/frigate:stable-synaptics`.
-
-Next, you need to grant docker permissions to access your hardware:
-
-- During the configuration process, you should run docker in privileged mode to avoid any errors due to insufficient permissions. To do so, add `privileged: true` to your `docker-compose.yml` file or the `--privileged` flag to your docker run command.
-
-```yaml
-devices:
-  - /dev/synap
-  - /dev/video0
-  - /dev/video1
-```
-
-or add these options to your `docker run` command:
-
-```
---device /dev/synap \
---device /dev/video0 \
---device /dev/video1 
-```
-
-#### Configuration
-
-Next, you should configure [hardware object detection](/configuration/object_detectors#synaptics) and [hardware video processing](/configuration/hardware_acceleration_video#synaptics).
+Next, you should configure [hardware object detection](/configuration/object_detectors#rockchip-platform) and [hardware video processing](/configuration/hardware_acceleration#rockchip-platform).
 
 ## Docker
 
-Running through Docker with Docker Compose is the recommended install method.
+Running in Docker with compose is the recommended install method.
 
 ```yaml
+version: "3.9"
 services:
   frigate:
     container_name: frigate
@@ -323,7 +219,7 @@ services:
       FRIGATE_RTSP_PASSWORD: "password"
 ```
 
-If you can't use Docker Compose, you can run the container with something similar to this:
+If you can't use docker compose, you can run the container with something similar to this:
 
 ```bash
 docker run -d \
@@ -347,23 +243,25 @@ docker run -d \
 
 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 Frigate build for amd64 & RPi Optimized Frigate build for arm64
 - `stable-standard-arm64` - Standard Frigate build for arm64
 - `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-jp5` - Frigate build optimized for nvidia Jetson devices running Jetpack 5
 - `stable-tensorrt-jp6` - Frigate build optimized for nvidia Jetson devices running Jetpack 6
 - `stable-rk` - Frigate build for SBCs with Rockchip SoC
+- `stable-rocm` - Frigate build for [AMD GPUs](../configuration/object_detectors.md#amdrocm-gpu-detector)
+  - `stable-h8l` - Frigate build for the Hailo-8L M.2 PICe Raspberry Pi 5 hat
 
-## Home Assistant Add-on
+## Home Assistant Addon
 
 :::warning
 
-As of Home Assistant Operating System 10.2 and Home Assistant 2023.6 defining separate network storage for media is supported.
+As of HomeAssistant OS 10.2 and Core 2023.6 defining separate network storage for media is supported.
 
-There are important limitations in HA OS to be aware of:
+There are important limitations in Home Assistant Operating System 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.
@@ -377,27 +275,24 @@ See [the network storage guide](/guides/ha_network_storage.md) for instructions
 
 :::
 
-Home Assistant OS users can install via the Add-on repository.
+HassOS users can install via the addon repository.
 
-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 Add-on (see below)
+1. Navigate to Supervisor > Add-on Store > Repositories
+2. Add https://github.com/blakeblackshear/frigate-hass-addons
+3. Install your desired Frigate NVR Addon and navigate to it's page
 4. Setup your network configuration in the `Configuration` tab
-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
+5. (not for proxy addon) Create the file `frigate.yaml` in your `config` directory with your detailed Frigate configuration
+6. Start the addon container
+7. (not for proxy addon) If you are using hardware acceleration for ffmpeg, you may need to disable "Protection mode"
 
-There are several variants of the Add-on available:
+There are several versions of the addon available:
 
-| 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 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 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).
+| Addon Version                  | Description                                                |
+| ------------------------------ | ---------------------------------------------------------- |
+| Frigate NVR                    | Current release with protection mode on                    |
+| Frigate NVR (Full Access)      | Current release with the option to disable protection mode |
+| Frigate NVR Beta               | Beta release with protection mode on                       |
+| Frigate NVR Beta (Full Access) | Beta release with the option to disable protection mode    |
 
 ## Kubernetes
 
@@ -418,8 +313,7 @@ If you choose to run Frigate via LXC in Proxmox the setup can be complex so be p
 
 :::
 
-Suggestions include:
-
+ Suggestions include:
 - For Intel-based hardware acceleration, to allow access to the `/dev/dri/renderD128` device with major number 226 and minor number 128, add the following lines to the `/etc/pve/lxc/<id>.conf` LXC configuration:
   - `lxc.cgroup2.devices.allow: c 226:128 rwm`
   - `lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file`
@@ -510,7 +404,7 @@ mkdir -p /share/share_vol2/frigate/media
 # Also replace the time zone value for 'TZ' in the sample command.
 # Example command will create a docker container that uses at most 2 CPUs and 4G RAM.
 # You may need to add "--env=LIBVA_DRIVER_NAME=i965 \" to the following docker run command if you
-# have certain CPU (e.g., J4125). See https://docs.frigate.video/configuration/hardware_acceleration_video.
+# have certain CPU (e.g., J4125). See https://docs.frigate.video/configuration/hardware_acceleration.
 docker run \
   --name=frigate \
   --shm-size=256m \

docs/docs/frigate/planning_setup.md

@@ -1,74 +0,0 @@
----
-id: planning_setup
-title: Planning a New Installation
----
-
-Choosing the right hardware for your Frigate NVR setup is important for optimal performance and a smooth experience. This guide will walk you through the key considerations, focusing on the number of cameras and the hardware required for efficient object detection.
-
-## Key Considerations
-
-### Number of Cameras and Simultaneous Activity
-
-The most fundamental factor in your hardware decision is the number of cameras you plan to use. However, it's not just about the raw count; it's also about how many of those cameras are likely to see activity and require object detection simultaneously.
-
-When motion is detected in a camera's feed, regions of that frame are sent to your chosen [object detection hardware](/configuration/object_detectors).
-
-- **Low Simultaneous Activity (1-6 cameras with occasional motion)**: If you have a few cameras in areas with infrequent activity (e.g., a seldom-used backyard, a quiet interior), the demand on your object detection hardware will be lower. A single, entry-level AI accelerator will suffice.
-- **Moderate Simultaneous Activity (6-12 cameras with some overlapping motion)**: For setups with more cameras, especially in areas like a busy street or a property with multiple access points, it's more likely that several cameras will capture activity at the same time. This increases the load on your object detection hardware, requiring more processing power.
-- **High Simultaneous Activity (12+ cameras or highly active zones)**: Large installations or scenarios where many cameras frequently capture activity (e.g., busy street with overview, identification, dedicated LPR cameras, etc.) will necessitate robust object detection capabilities. You'll likely need multiple entry-level AI accelerators or a more powerful single unit such as a discrete GPU.
-- **Commercial Installations (40+ cameras)**: Commercial installations or scenarios where a substantial number of cameras capture activity (e.g., a commercial property, an active public space) will necessitate robust object detection capabilities. You'll likely need a modern discrete GPU.
-
-### Video Decoding
-
-Modern CPUs with integrated GPUs (Intel Quick Sync, AMD VCN) or dedicated GPUs can significantly offload video decoding from the main CPU, freeing up resources. This is highly recommended, especially for multiple cameras.
-
-:::tip
-
-For commercial installations it is important to verify the number of supported concurrent streams on your GPU, many consumer GPUs max out at ~20 concurrent camera streams.
-
-:::
-
-## Hardware Considerations
-
-### Object Detection
-
-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.
-
-### 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.
-
-- Longevity: Modern SSDs are designed with advanced wear-leveling algorithms and significantly higher "Terabytes Written" (TBW) ratings than earlier models. For typical home NVR use, a good quality SSD will likely outlast the useful life of your NVR hardware itself.
-- Performance: SSDs excel at handling the numerous small write operations that occur during continuous video recording and can significantly improve the responsiveness of the Frigate UI and clip retrieval.
-- Silence and Efficiency: SSDs produce no noise and consume less power than traditional HDDs.
-
-#### HDDs (Hard Disk Drives)
-
-Traditional Hard Disk Drives (HDDs) remain a great and often more cost-effective option for long-term video storage, especially for larger setups where raw capacity is prioritized.
-
-- Cost-Effectiveness: HDDs offer the best cost per gigabyte, making them ideal for storing many days, weeks, or months of continuous footage.
-- Capacity: HDDs are available in much larger capacities than most consumer SSDs, which is beneficial for extensive video archives.
-- NVR-Rated Drives: If choosing an HDD, consider drives specifically designed for surveillance (NVR) use, such as Western Digital Purple or Seagate SkyHawk. These drives are engineered for 24/7 operation and continuous write workloads, offering improved reliability compared to standard desktop drives.
-
-Determining Your Storage Needs
-The amount of storage you need will depend on several factors:
-
-- Number of Cameras: More cameras naturally require more space.
-- Resolution and Framerate: Higher resolution (e.g., 4K) and higher framerate (e.g., 30fps) streams consume significantly more storage.
-- Recording Method: Continuous recording uses the most space. motion-only recording or object-triggered recording can save space, but may miss some footage.
-- Retention Period: How many days, weeks, or months of footage do you want to keep?
-
-#### Network Storage (NFS/SMB)
-
-While supported, using network-attached storage (NAS) for recordings can introduce latency and network dependency considerations. For optimal performance and reliability, it is generally recommended to have local storage for your Frigate recordings. If using a NAS, ensure your network connection to it is robust and fast (Gigabit Ethernet at minimum) and that the NAS itself can handle the continuous write load.
-
-### RAM (Memory)
-
-- **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.