.cspell/frigate-dictionary.txt

@@ -109,7 +109,6 @@ imdecode
 imencode
 imread
 imwrite
-inpoint
 interp
 iostat
 iotop
@@ -191,7 +190,6 @@ ONVIF
 openai
 opencv
 openvino
-overfitting
 OWASP
 paddleocr
 paho
@@ -266,7 +264,6 @@ tensorrt
 tflite
 thresholded
 timelapse
-titlecase
 tmpfs
 tobytes
 toggleable

.cursor/rules/frontend-always-use-translation-files.mdc

@@ -1,6 +0,0 @@
----
-globs: ["**/*.ts", "**/*.tsx"]
-alwaysApply: false
----
-
-Never write strings in the frontend directly, always write to and reference the relevant translations file.

.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:

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  PYTHON_VERSION: 3.11
+  PYTHON_VERSION: 3.9
 
 jobs:
   amd64_build:
@@ -23,7 +23,7 @@ jobs:
     name: AMD64 Build
     steps:
       - name: Check out code
-        uses: actions/checkout@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
@@ -132,10 +161,11 @@ 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=${{ steps.setup.outputs.cache-name }}-amd64,mode=max
       - name: AMD/ROCm general build
         env:
+          AMDGPU: gfx
           HSA_OVERRIDE: 0
         uses: docker/bake-action@v6
         with:
@@ -146,7 +176,7 @@ jobs:
           set: |
             rocm.tags=${{ 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
@@ -154,7 +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
@@ -172,31 +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:
@@ -211,7 +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

.gitignore

@@ -15,7 +15,6 @@ frigate/version.py
 web/build
 web/node_modules
 web/coverage
-web/.env
 core
 !/web/**/*.ts
 .idea/*

LICENSE

@@ -1,6 +1,6 @@
 The MIT License
 
-Copyright (c) 2025 Frigate LLC (Frigate™)
+Copyright (c) 2020 Blake Blackshear
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

Makefile

@@ -1,7 +1,7 @@
 default_target: local
 
 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.17.0
+VERSION = 0.16.0
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
@@ -14,19 +14,12 @@ push-boards: $(BOARDS:%=push-%)
 
 version:
 	echo 'VERSION = "$(VERSION)-$(COMMIT_HASH)"' > frigate/version.py
-	echo 'VITE_GIT_COMMIT_HASH=$(COMMIT_HASH)' > web/.env
 
 local: version
 	docker buildx build --target=frigate --file docker/main/Dockerfile . \
 		--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

@@ -1,10 +1,8 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/frigate.png">
 </p>
 
-# Frigate NVR™ - Realtime Object Detection for IP Cameras
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+# Frigate - NVR With Realtime Object Detection for IP Cameras
 
 <a href="https://hosted.weblate.org/engage/frigate-nvr/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/language-badge.svg" alt="Translation status" />
@@ -14,7 +12,7 @@
 
 A complete and local NVR designed for [Home Assistant](https://www.home-assistant.io) with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
 
-Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported [object detectors](https://docs.frigate.video/configuration/object_detectors/).
+Use of a GPU or AI accelerator such as a [Google Coral](https://coral.ai/products/) or [Hailo](https://hailo.ai/) is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
 
 - Tight integration with Home Assistant via a [custom component](https://github.com/blakeblackshear/frigate-hass-integration)
 - Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
@@ -35,15 +33,6 @@ View the documentation at https://docs.frigate.video
 
 If you would like to make a donation to support development, please use [Github Sponsors](https://github.com/sponsors/blakeblackshear).
 
-## License
-
-This project is licensed under the **MIT License**.
-
-- **Code:** The source code, configuration files, and documentation in this repository are available under the [MIT License](LICENSE). You are free to use, modify, and distribute the code as long as you include the original copyright notice.
-- **Trademarks:** The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are **trademarks of Frigate LLC** and are **not** covered by the MIT License.
-
-Please see our [Trademark Policy](TRADEMARK.md) for details on acceptable use of our brand assets.
-
 ## Screenshots
 
 ### Live dashboard
@@ -77,7 +66,3 @@ We use [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) to support la
 <a href="https://hosted.weblate.org/engage/frigate-nvr/">
 <img src="https://hosted.weblate.org/widget/frigate-nvr/multi-auto.svg" alt="Translation status" />
 </a>
-
----
-
-**Copyright © 2025 Frigate LLC.**

README_CN.md

@@ -1,89 +1,64 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/frigate.png">
 </p>
 
-# Frigate NVR™ - 一个具有实时目标检测的本地 NVR
+# Frigate - 一个具有实时目标检测的本地NVR
 
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
-
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[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 目标/物体检测功能。使用 OpenCV 和 TensorFlow 在本地为 IP 摄像头执行实时物体检测。
+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/)等）。它们的运行效率远远高于现在的顶级 CPU，并且功耗也极低。
-
-- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与 Home Assistant 紧密集成
-- 设计上通过仅在必要时和必要地点寻找目标，最大限度地减少资源使用并最大化性能
+强烈推荐使用GPU或者AI加速器（例如[Google Coral加速器](https://coral.ai/products/) 或者 [Hailo](https://hailo.ai/)）。它们的性能甚至超过目前的顶级CPU，并且可以以极低的耗电实现更优的性能。
+- 通过[自定义组件](https://github.com/blakeblackshear/frigate-hass-integration)与Home Assistant紧密集成
+- 设计上通过仅在必要时和必要地点寻找物体，最大限度地减少资源使用并最大化性能
 - 大量利用多进程处理，强调实时性而非处理每一帧
-- 使用非常低开销的画面变动检测（也叫运动检测）来确定运行目标检测的位置
-- 使用 TensorFlow 进行目标检测，并运行在单独的进程中以达到最大 FPS
-- 通过 MQTT 进行通信，便于集成到其他系统中
+- 使用非常低开销的运动检测来确定运行物体检测的位置
+- 使用TensorFlow进行物体检测，运行在单独的进程中以达到最大FPS
+- 通过MQTT进行通信，便于集成到其他系统中
 - 根据检测到的物体设置保留时间进行视频录制
-- 24/7 全天候录制
-- 通过 RTSP 重新流传输以减少摄像头的连接数
-- 支持 WebRTC 和 MSE，实现低延迟的实时观看
+- 24/7全天候录制
+- 通过RTSP重新流传输以减少摄像头的连接数
+- 支持WebRTC和MSE，实现低延迟的实时观看
 
-## 社区中文翻译文档
+## 文档（英文）
 
-你可以在这里查看文档 https://docs.frigate-cn.video
+你可以在这里查看文档 https://docs.frigate.video
+
+文档还暂时没有提供翻译，将会在未来提供。
 
 ## 赞助
 
 如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
 
-## 协议
-
-本项目采用 **MIT 许可证**授权。
-**代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
-
-**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate LLC 的商标**，**不在** MIT 许可证覆盖范围内。
-有关品牌资产的规范使用详情，请参阅我们的[《商标政策》](TRADEMARK.md)。
-
 ## 截图
 
 ### 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
-
+### 简单的审查工作流程
 <div>
 <img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
 </div>
 
 ### 多摄像头可按时间轴查看
-
 <div>
 <img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
 </div>
 
 ### 内置遮罩和区域编辑器
-
 <div>
 <img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
-## 翻译
 
+## 翻译
 我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
 
-## 非官方中文讨论社区
-
-欢迎加入中文讨论 QQ 群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
-
-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
-
----
-
-**Copyright © 2025 Frigate LLC.**
+## 中文讨论社区
+欢迎加入非官方中文讨论QQ群：1043861059

TRADEMARK.md

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

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 (
     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/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="4.20.1"
 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_${TARGETARCH}" go2rtc
+ADD --link --chmod=755 "https://github.com/AlexxIT/go2rtc/releases/download/v1.9.9/go2rtc_linux_${TARGETARCH}" go2rtc
 
 FROM wget AS tempio
 ARG TARGETARCH
@@ -148,12 +148,11 @@ 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 \
@@ -178,8 +177,6 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O 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 +184,7 @@ 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 +206,6 @@ 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 +224,9 @@ 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
@@ -256,10 +243,6 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
 RUN --mount=type=bind,from=wheels,source=/wheels,target=/deps/wheels \
     pip3 install -U /deps/wheels/*.whl
 
-# Install 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
@@ -277,7 +260,7 @@ 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

docker/main/build_pysqlite3.sh

@@ -2,31 +2,18 @@
 
 set -euxo pipefail
 
-SQLITE3_VERSION="3.46.1"
+SQLITE3_VERSION="96c92aba00c8375bc32fafcdf12429c58bd8aabfcadab6683e35bbb9cdebf19e" # 3.46.0
 PYSQLITE3_VERSION="0.5.3"
 
-# Install libsqlite3-dev if not present (needed for some base images like NVIDIA TensorRT)
-if ! dpkg -l | grep -q libsqlite3-dev; then
-  echo "Installing libsqlite3-dev for compilation..."
-  apt-get update && apt-get install -y libsqlite3-dev && rm -rf /var/lib/apt/lists/*
-fi
-
-# 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

@@ -19,9 +19,7 @@ apt-get -qq install --no-install-recommends -y \
     nethogs \
     libgl1 \
     libglib2.0-0 \
-    libusb-1.0.0 \
-    python3-h2 \
-    libgomp1  # memryx detector
+    libusb-1.0.0
 
 update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1
 
@@ -33,18 +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
@@ -71,16 +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
 
@@ -92,41 +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
-
-    # install libtbb12 for NPU support
-    apt-get -qq install -y libtbb12
+        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
-    # npu packages
-    wget https://github.com/oneapi-src/level-zero/releases/download/v1.21.9/level-zero_1.21.9+u22.04_amd64.deb
-    wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-driver-compiler-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
-    wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-fw-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
-    wget https://github.com/intel/linux-npu-driver/releases/download/v1.17.0/intel-level-zero-npu_1.17.0.20250508-14912879441_ubuntu22.04_amd64.deb
-
-    dpkg -i *.deb
-    rm *.deb
 fi
 
 if [[ "${TARGETARCH}" == "arm64" ]]; then
@@ -145,6 +94,6 @@ rm -rf /var/lib/apt/lists/*
 
 # Install yq, for frigate-prepare and go2rtc echo source
 curl -fsSL \
-    "https://github.com/mikefarah/yq/releases/download/v4.48.2/yq_linux_$(dpkg --print-architecture)" \
+    "https://github.com/mikefarah/yq/releases/download/v4.33.3/yq_linux_$(dpkg --print-architecture)" \
     --output /usr/local/bin/yq
 chmod +x /usr/local/bin/yq

docker/main/install_hailort.sh

@@ -2,7 +2,7 @@
 
 set -euxo pipefail
 
-hailo_version="4.21.0"
+hailo_version="4.20.1"
 
 if [[ "${TARGETARCH}" == "amd64" ]]; then
     arch="x86_64"

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/tags/v2.1.0.zip -O /tmp/mxaccl.zip
-unzip /tmp/mxaccl.zip -d /tmp
-mv /tmp/mx_accl_frigate-2.1.0 /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 +1,24 @@
 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.*
+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'
+python-multipart == 0.0.12
 # 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 +27,24 @@ 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.*
@@ -56,7 +52,7 @@ pywebpush == 2.0.*
 # alpr
 pyclipper == 1.3.*
 shapely == 2.0.*
-rapidfuzz==3.12.*
+Levenshtein==0.26.*
 # HailoRT Wheels
 appdirs==1.4.*
 argcomplete==2.0.*
@@ -74,12 +70,3 @@ 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.*
-# Memory profiling
-memray == 1.15.*

docker/main/requirements.txt

@@ -1 +1,2 @@
 scikit-build == 0.18.*
+nvidia-pyindex

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

@@ -4,11 +4,6 @@
 
 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

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
@@ -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

@@ -85,7 +85,7 @@ python3 /usr/local/nginx/get_base_path.py | \
        -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

@@ -138,9 +138,5 @@ function migrate_db_from_media_to_config() {
     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/usr/local/ffmpeg/get_ffmpeg_path.py

@@ -1,6 +1,5 @@
 import json
 import sys
-from typing import Any
 
 from ruamel.yaml import YAML
 
@@ -22,11 +21,11 @@ try:
         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
 
@@ -38,13 +37,13 @@ try:
         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 +53,7 @@ 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:
@@ -135,7 +134,7 @@ 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;
 
@@ -73,8 +71,6 @@ http {
         vod_manifest_segment_durations_mode accurate;
         vod_ignore_edit_list on;
         vod_segment_duration 10000;
-
-        # MPEG-TS settings (not used when fMP4 is enabled, kept for reference)
         vod_hls_mpegts_align_frames off;
         vod_hls_mpegts_interleave_frames on;
 
@@ -107,10 +103,6 @@ http {
             aio threads;
             vod hls;
 
-            # Use fMP4 (fragmented MP4) instead of MPEG-TS for better performance
-            # Smaller segments, faster generation, better browser compatibility
-            vod_hls_container_format fmp4;
-
             secure_token $args;
             secure_token_types application/vnd.apple.mpegurl;
 
@@ -280,18 +272,6 @@ http {
                 include proxy.conf;
             }
 
-            # Allow unauthenticated access to the first_time_login endpoint
-            # so the login page can load help text before authentication.
-            location /api/auth/first_time_login {
-                auth_request off;
-                limit_except GET {
-                    deny all;
-                }
-                rewrite ^/api(/.*)$ $1 break;
-                proxy_pass http://frigate_api;
-                include proxy.conf;
-            }
-
             location /api/stats {
                 include auth_request.conf;
                 access_log off;
@@ -320,12 +300,6 @@ http {
                 add_header Cache-Control "public";
             }
 
-            location /fonts/ {
-                access_log off;
-                expires 1y;
-                add_header Cache-Control "public";
-            }
-
             location /locales/ {
                 access_log off;
                 add_header Cache-Control "public";

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

@@ -2,10 +2,9 @@
 
 import json
 import os
-from typing import Any
 
 base_path = os.environ.get("FRIGATE_BASE_PATH", "")
 
-result: dict[str, Any] = {"base_path": base_path}
+result: dict[str, any] = {"base_path": base_path}
 
 print(json.dumps(result))

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

@@ -2,7 +2,6 @@
 
 import json
 import sys
-from typing import Any
 
 from ruamel.yaml import YAML
 
@@ -20,16 +19,12 @@ try:
         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] = {}
 
 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))
+print(json.dumps(tls_config))

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

@@ -1,44 +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 specific SDK 2.1 packages
-echo "Installing MemryX SDK 2.1 packages..."
-sudo apt update
-sudo apt install -y memx-drivers=2.1.* memx-accl=2.1.* mxa-manager=2.1.*
-
-# Hold packages to prevent automatic upgrades
-sudo apt-mark hold memx-drivers memx-accl mxa-manager
-
-# 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"
-
-echo "MemryX SDK 2.1 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
@@ -30,9 +28,7 @@ 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 --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/rocm/Dockerfile

@@ -2,7 +2,8 @@
 
 # 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
 
@@ -10,17 +11,18 @@ ARG HSA_OVERRIDE
 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/7.1.1/ubuntu/jammy/amdgpu-install_7.1.1.70101-1_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
@@ -31,13 +33,7 @@ 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 && \
-    # Install C++ standard library headers for HIPRTC kernel compilation fallback
-    apt-get install -qq -y libstdc++-12-dev && \
-    rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y libnuma1
 
 WORKDIR /opt/frigate
 COPY --from=rootfs / /
@@ -48,29 +44,25 @@ RUN wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
 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
 
 #######################################################################
 FROM scratch AS rocm-dist
 
 ARG ROCM
+ARG AMDGPU
 
 COPY --from=rocm /opt/rocm-$ROCM/bin/rocminfo /opt/rocm-$ROCM/bin/migraphx-driver /opt/rocm-$ROCM/bin/
-# Copy MIOpen database files for gfx10xx and gfx11xx only (RDNA2/RDNA3)
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx10* /opt/rocm-$ROCM/share/miopen/db/
-COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx11* /opt/rocm-$ROCM/share/miopen/db/
-# Copy rocBLAS library files for gfx10xx and gfx11xx only
-COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx10* /opt/rocm-$ROCM/lib/rocblas/library/
-COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*gfx11* /opt/rocm-$ROCM/lib/rocblas/library/
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*$AMDGPU* /opt/rocm-$ROCM/share/miopen/db/
+COPY --from=rocm /opt/rocm-$ROCM/share/miopen/db/*gfx908* /opt/rocm-$ROCM/share/miopen/db/
+COPY --from=rocm /opt/rocm-$ROCM/lib/rocblas/library/*$AMDGPU* /opt/rocm-$ROCM/lib/rocblas/library/
 COPY --from=rocm /opt/rocm-dist/ /
 
 #######################################################################
 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/v7.1.0/onnxruntime_migraphx-1.23.1-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

@@ -1,5 +1,8 @@
+variable "AMDGPU" {
+  default = "gfx900"
+}
 variable "ROCM" {
-  default = "7.1.1"
+  default = "6.3.3"
 }
 variable "HSA_OVERRIDE_GFX_VERSION" {
   default = ""
@@ -35,6 +38,7 @@ target rocm {
   }
   platforms = ["linux/amd64"]
   args = {
+    AMDGPU = AMDGPU,
     ROCM = ROCM,
     HSA_OVERRIDE_GFX_VERSION = HSA_OVERRIDE_GFX_VERSION,
     HSA_OVERRIDE = HSA_OVERRIDE

docker/rocm/rocm.mk

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

docker/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 \
-    && 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

@@ -1,61 +1,9 @@
-# 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
 
@@ -99,11 +47,12 @@ 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
@@ -112,7 +61,7 @@ RUN apt-get update \
     && apt-get install -y protobuf-compiler libprotobuf-dev \
     && rm -rf /var/lib/apt/lists/*
 RUN --mount=type=bind,source=docker/tensorrt/requirements-models-arm64.txt,target=/requirements-tensorrt-models.txt \
-    pip3 wheel --wheel-dir=/trt-model-wheels --no-deps -r /requirements-tensorrt-models.txt
+    pip3 wheel --wheel-dir=/trt-model-wheels -r /requirements-tensorrt-models.txt
 
 FROM wget AS jetson-ffmpeg
 ARG DEBIAN_FRONTEND
@@ -144,13 +93,11 @@ 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 \
-    && pip3 install -U /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 +1,57 @@
+# 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/ && \
+      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

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

@@ -1,6 +1,7 @@
+/usr/local/lib
+/usr/local/cuda
+/usr/local/lib/python3.11/dist-packages/tensorrt
 /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/cufft/lib

docker/tensorrt/requirements-amd64.txt

@@ -1,18 +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'
+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-arm64.txt

@@ -1,2 +1 @@
 cuda-python == 12.6.*; platform_machine == 'aarch64'
-numpy == 1.26.*; platform_machine == 'aarch64'

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

@@ -25,7 +25,7 @@ Examples of available modules are:
 
 - `frigate.app`
 - `frigate.mqtt`
-- `frigate.object_detection.base`
+- `frigate.object_detection`
 - `detector.<detector_name>`
 - `watchdog.<camera_name>`
 - `ffmpeg.<camera_name>.<sorted_roles>` NOTE: All FFmpeg logs are sent as `error` level.
@@ -53,17 +53,6 @@ environment_vars:
   VARIABLE_NAME: variable_value
 ```
 
-#### TensorFlow Thread Configuration
-
-If you encounter thread creation errors during classification model training, you can limit TensorFlow's thread usage:
-
-```yaml
-environment_vars:
-  TF_INTRA_OP_PARALLELISM_THREADS: "2" # Threads within operations (0 = use default)
-  TF_INTER_OP_PARALLELISM_THREADS: "2" # Threads between operations (0 = use default)
-  TF_DATASET_THREAD_POOL_SIZE: "2" # Data pipeline threads (0 = use default)
-```
-
 ### `database`
 
 Tracked object and recording information is managed in a sqlite database at `/config/frigate.db`. If that database is deleted, recordings will be orphaned and will need to be cleaned up manually. They also won't show up in the Media Browser within Home Assistant.
@@ -188,11 +177,9 @@ listen [::]:5000 ipv6only=off;
 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;
@@ -201,11 +188,9 @@ location /frigate {
 ```
 
 ### 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:
@@ -215,7 +200,6 @@ services:
 ```
 
 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
 ```
@@ -234,7 +218,7 @@ 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 v1.9.9, there may be certain cases where you want to run a different version of go2rtc.
 
 To do this:
 
@@ -258,7 +242,7 @@ curl -X POST http://frigate_host:5000/api/config/save -d @config.json
 if you'd like you can use your yaml config directly by using [`yq`](https://github.com/mikefarah/yq) to convert it to json:
 
 ```bash
-yq -o=json '.' config.yaml | curl -X POST 'http://frigate_host:5000/api/config/save?save_option=saveonly' --data-binary @-
+yq r -j config.yml | curl -X POST http://frigate_host:5000/api/config/save -d @-
 ```
 
 ### Via Command Line

docs/docs/configuration/audio_detectors.md

@@ -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
 
@@ -72,106 +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`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service — automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
-
-Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.
-
-#### Configuration
-
-To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.
-
-```yaml
-audio_transcription:
-  enabled: True
-  device: ...
-  model_size: ...
-```
-
-Disable audio transcription for select cameras at the camera level:
-
-```yaml
-cameras:
-  back_yard:
-    ...
-    audio_transcription:
-      enabled: False
-```
-
-:::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.
-  - 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.
-
-:::note
-
-Only one `speech` event may be transcribed at a time. Frigate does not automatically transcribe `speech` events or implement a queue for long-running transcription model inference.
-
-:::
-
-Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
-
-#### FAQ
-
-1. Why doesn't Frigate automatically transcribe all `speech` events?
-
-   Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
-
-   Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
-
-   If you hear speech that’s actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
-
-   Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
-
-2. Why don't you save live transcription text and use that for `speech` events?
-
-   There’s no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
-
-   Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.

docs/docs/configuration/authentication.md

@@ -52,22 +52,6 @@ auth:
     - 172.18.0.0/16 # <---- this is the subnet for the internal Docker Compose network
 ```
 
-## Session Length
-
-The default session length for user authentication in Frigate is 24 hours. This setting determines how long a user's authenticated session remains active before a token refresh is required — otherwise, the user will need to log in again.
-
-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
-```
-
 ## JWT Token Secret
 
 The JWT token secret needs to be kept secure. Anyone with this secret can generate valid JWT tokens to authenticate with Frigate. This should be a cryptographically random string of at least 64 characters.
@@ -81,7 +65,7 @@ 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/`)
+2. A docker secret named `FRIGATE_JWT_SECRET` in `/run/secrets/`
 3. A `jwt_secret` option from the Home Assistant Add-on options
 4. 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/autotracking.md

@@ -21,7 +21,7 @@ Frigate autotracking functions with PTZ cameras capable of relative movement wit
 
 Many cheaper or older PTZs may not support this standard. Frigate will report an error message in the log and disable autotracking if your PTZ is unsupported.
 
-The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/) can provide a starting point to determine a camera's compatibility with Frigate's autotracking. Look to see if a camera lists `PTZRelative`, `PTZRelativePanTilt` and/or `PTZRelativeZoom`. These features are required for autotracking, but some cameras still fail to respond even if they claim support.
+Alternatively, you can download and run [this simple Python script](https://gist.github.com/hawkeye217/152a1d4ba80760dac95d46e143d37112), replacing the details on line 4 with your camera's IP address, ONVIF port, username, and password to check your camera.
 
 A growing list of cameras and brands that have been reported by users to work with Frigate's autotracking can be found [here](cameras.md).
 

docs/docs/configuration/camera_specific.md

@@ -144,14 +144,7 @@ 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:
@@ -164,35 +157,19 @@ 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.
 
-<details>
-  <summary>Example Config</summary>
+:::warning
 
-:::tip
-
-Reolink's latest cameras support two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
-
-NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
-
-Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+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:
-    # example for connecting to a standard Reolink camera
     your_reolink_camera:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
     your_reolink_camera_sub:
       - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-    # example for connectin to a Reolink camera that supports two way talk
-    your_reolink_camera_twt:
-      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub
-    your_reolink_camera_twt_sub:
-      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
-      - "rtsp://username:password@reolink_ip/Preview_01_sub
-    # example for connecting to a Reolink NVR
     your_reolink_camera_via_nvr:
       - "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
       - "ffmpeg:your_reolink_camera_via_nvr#audio=aac"
@@ -223,7 +200,22 @@ cameras:
           roles:
             - detect
 ```
-</details>
+
+#### Reolink Doorbell
+
+The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
+
+Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
+
+```yaml
+go2rtc:
+  streams:
+    your_reolink_doorbell:
+      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
+      - rtsp://reolink_ip/Preview_01_sub
+    your_reolink_doorbell_sub:
+      - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
+```
 
 ### Unifi Protect Cameras
 
@@ -238,7 +230,7 @@ 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/v1.9.9#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.
 
@@ -251,37 +243,3 @@ 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

@@ -89,35 +89,31 @@ 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.
+This list of working and non-working PTZ cameras is based on user feedback.
 
-The FeatureList on the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/) can provide a starting point to determine a camera's compatibility with Frigate's autotracking. Look to see if a camera lists `PTZRelative`, `PTZRelativePanTilt` and/or `PTZRelativeZoom`. These features are required for autotracking, but some cameras still fail to respond even if they claim support. If they are missing, autotracking will not work (though basic PTZ in the WebUI might). Avoid cameras with no database entry unless they are confirmed as working below.
-
-| Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                                                                                                                                             |
-| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --- |
-| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                                                                                                                                   |
-| Amcrest 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 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                      |      ✅      |      ❌      |                                                                                                                                                                                                                                                                   |
-| 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                                                                                                                                                                                                                   |
-| Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                                                                                                                                    |
-| Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                                                                                                                                             |
-| Vikylin PTZ-2804X-I2         |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                                                                                                                                          |
+| Brand or specific camera     | PTZ Controls | Autotracking | Notes                                                                                                                                           |
+| ---------------------------- | :----------: | :----------: | ----------------------------------------------------------------------------------------------------------------------------------------------- |
+| Amcrest                      |      ✅      |      ✅      | ⛔️ Generally, Amcrest should work, but some older models (like the common IP2M-841) don't support autotracking                                 |
+| Amcrest 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.                                                                                            |
+| Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua                        |      ✅      |      ✅      |                                                                                                                                                 |
+| Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
+| Foscam R5                    |      ✅      |      ❌      |                                                                                                                                                 |
+| 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 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                                                                                                 |
+| Uniview IPC672LR-AX4DUPK     |      ✅      |      ❌      | Firmware says FOV relative movement is supported, but camera doesn't actually move when sending ONVIF commands                                  |
+| Uniview IPC6612SR-X33-VG     |      ✅      |      ✅      | Leave `calibrate_on_startup` as `False`. A user has reported that zooming with `absolute` is working.                                           |
+| Vikylin PTZ-2804X-I2         |      ❌      |      ❌      | Incomplete ONVIF support                                                                                                                        |
 
 ## Setting up camera groups
 
@@ -138,7 +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

@@ -1,96 +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.
-
-## Classes
-
-Classes are the categories your model will learn to distinguish between. Each class represents a distinct visual category that the model will predict.
-
-For object classification:
-
-- Define classes that represent different types or attributes of the detected object
-- Examples: For `person` objects, classes might be `delivery_person`, `resident`, `stranger`
-- Include a `none` class for objects that don't fit any specific category
-- Keep classes visually distinct to improve accuracy
-
-### Classification Type
-
-- **Sub label**:
-
-  - Applied to the object’s `sub_label` field.
-  - Ideal for a single, more specific identity or type.
-  - Example: `cat` → `Leo`, `Charlie`, `None`.
-
-- **Attribute**:
-  - Added as metadata to the object (visible in /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.
-
-## Assignment Requirements
-
-Sub labels and attributes are only assigned when both conditions are met:
-
-1. **Threshold**: Each classification attempt must have a confidence score that meets or exceeds the configured `threshold` (default: `0.8`).
-2. **Class Consensus**: After at least 3 classification attempts, 60% of attempts must agree on the same class label. If the consensus class is `none`, no assignment is made.
-
-This two-step verification prevents false positives by requiring consistent predictions across multiple frames before assigning a sub label or attribute.
-
-## Example 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. The process consists of two steps:
-
-### Step 1: Name and Define
-
-Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category.
-
-### Step 2: Assign Training Examples
-
-The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically.
-
-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.
-
-### Improving the Model
-
-- **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
-- **Data collection**: Use the model’s Recent Classification tab to gather balanced examples across times of day, weather, and distances.
-- **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
-- **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
-- **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.

docs/docs/configuration/custom_classification/state_classification.md

@@ -1,74 +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.
-
-## Classes
-
-Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.
-
-For state classification:
-
-- Define classes that represent mutually exclusive states
-- Examples: `open` and `closed` for a garage door, `on` and `off` for lights
-- Use at least 2 classes (typically binary states work best)
-- Keep class names clear and descriptive
-
-## 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. The process consists of three steps:
-
-### Step 1: Name and Define
-
-Enter a name for your model and define at least 2 classes (states) that represent mutually exclusive states. For example, `open` and `closed` for a door, or `on` and `off` for lights.
-
-### Step 2: Select the Crop Area
-
-Choose one or more cameras and draw a rectangle over the area of interest for each camera. The crop should be tight around the region you want to classify to avoid extra signals unrelated to what is being classified. You can drag and resize the rectangle to adjust the crop area.
-
-### Step 3: Assign Training Examples
-
-The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state.
-
-**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.
-
-Once all images are assigned, training will begin automatically.
-
-### Improving the Model
-
-- **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
-- **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
-- **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
-- **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.

docs/docs/configuration/face_recognition.md

@@ -3,7 +3,7 @@ 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 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.
 
 ## Model Requirements
 
@@ -13,28 +13,20 @@ When running a Frigate+ model (or any custom model that natively detects faces)
 
 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.
+- **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 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.
+The `large` model is optimized for accuracy, an integrated or discrete GPU is highly recommended. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
 
 ## Configuration
 
@@ -45,11 +37,9 @@ face_recognition:
   enabled: true
 ```
 
-Like the other real-time processors in Frigate, face recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
-
 ## Advanced Configuration
 
-Fine-tune face recognition with these optional parameters at the global level of your config. The only optional parameters that can be set at the camera level are `enabled` and `min_area`.
+Fine-tune face recognition with these optional parameters:
 
 ### Detection
 
@@ -67,24 +57,10 @@ Fine-tune face recognition with these optional parameters at the global level of
   - Default: `0.8`.
 - `recognition_threshold`: Recognition confidence score required to add the face to the object as a sub label.
   - Default: `0.9`.
-- `min_faces`: Min face recognitions for the sub label to be applied to the person object.
-  - Default: `1`
 - `save_attempts`: Number of images of recognized faces to save for training.
-  - Default: `200`.
+  - 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
 
@@ -114,55 +90,22 @@ When choosing images to include in the face training set it is recommended to al
 
 :::
 
-### Understanding the Recent Recognitions Tab
-
-The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
-
-Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
-
-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 1-5 "portrait" photos 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.
+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.
 
-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.
+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.
+
+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.
+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.
 
 ## 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 Recent Recognitions tab in the Frigate UI's Face Library.
-
-   If you are using a Frigate+ or `face` detecting model:
-
-   - Watch the debug view (Settings --> Debug) to ensure that `face` is being detected along with `person`.
-   - You may need to adjust the `min_score` for the `face` object if faces are not being detected.
-
-   If you are **not** using a Frigate+ or `face` detecting model:
-
-   - Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
-   - You may need to lower your `detection_threshold` if faces are not being detected.
-
-2. Any detected faces will then be _recognized_.
-
-   - Make sure you have trained at least one face per the recommendations above.
-   - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
-
-### 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.
-
-Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
-
 ### Why can't I bulk upload photos?
 
 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.
@@ -179,16 +122,7 @@ This can happen for a few different reasons, but this is usually an indicator th
 - 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 Recent Recognitions tab, but a sub label wasn't assigned?
+### 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.
 
@@ -207,7 +141,3 @@ Face recognition does not run on the recording stream, this would be suboptimal
 ### 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.

docs/docs/configuration/ffmpeg_presets.md

@@ -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

@@ -9,37 +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-2.0-flash
+  model: gemini-1.5-flash
 
 cameras:
-  front_camera:
-    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
@@ -68,9 +55,10 @@ 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: qwen3-vl:4b
+  model: llava:7b
 ```
 
 ## Google Gemini
@@ -79,7 +67,7 @@ Google Gemini has a free tier allowing [15 queries per minute](https://ai.google
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
 
 ### Get API Key
 
@@ -94,24 +82,19 @@ 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-2.0-flash
+  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.
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
 
 ### Get API Key
 
@@ -121,6 +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
@@ -138,19 +122,19 @@ Microsoft offers several vision models through Azure OpenAI. A subscription is r
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
 
 ### Create Resource and Get API Key
 
-To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
+To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource.
 
 ### Configuration
 
 ```yaml
 genai:
+  enabled: True
   provider: azure_openai
-  base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
-  model: gpt-5-mini
+  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
   api_key: "{FRIGATE_OPENAI_API_KEY}"
 ```
 
@@ -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."
 ```
 
-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. qwen3-VL supports vision and tools simultaneously in Ollama.
-
-:::
-
-The following models are recommended:
-
-| Model             | Notes                                                                |
-| ----------------- | -------------------------------------------------------------------- |
-| `qwen3-vl`        | Strong visual and situational understanding, higher vram requirement |
-| `Intern3.5VL`     | Relatively fast with good vision comprehension                       |
-| `gemma3`          | Strong frame-to-frame understanding, slower inference times          |
-| `qwen2.5-vl`      | Fast but capable model with good vision comprehension                |
-
-:::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

@@ -1,119 +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](/integrations/mqtt/#frigatecamera_namereviewdescriptionsset).
-
-## Review Summary Usage and Best Practices
-
-Review summaries provide structured JSON responses that are saved for each review item:
-
-```
-- `title` (string): A concise, direct title that describes the purpose or overall action (e.g., "Person taking out trash", "Joe walking dog").
-- `scene` (string): A narrative description of what happens across the sequence from start to finish, including setting, detected objects, and their observable actions.
-- `confidence` (float): 0-1 confidence in the analysis. Higher confidence when objects/actions are clearly visible and context is unambiguous.
-- `other_concerns` (list): List of user-defined concerns that may need additional investigation.
-- `potential_threat_level` (integer): 0, 1, or 2 as defined below.
-```
-
-This will show in multiple places in the UI to give additional context about each activity, and allow viewing more details when extra attention is required. Frigate's built in notifications will also automatically show the title and description when the data is available.
-
-### Defining Typical Activity
-
-Each installation and even camera can have different parameters for what is considered suspicious activity. Frigate allows the `activity_context_prompt` to be defined globally and at the camera level, which allows you to define more specifically what should be considered normal activity. It is important that this is not overly specific as it can sway the output of the response.
-
-<details>
-  <summary>Default Activity Context Prompt</summary>
-
-```
-### Normal Activity Indicators (Level 0)
-- Known/verified people in any zone at any time
-- People with pets in residential areas
-- Deliveries or services during daytime/evening (6 AM - 10 PM): carrying packages to doors/porches, placing items, leaving
-- Services/maintenance workers with visible tools, uniforms, or service vehicles during daytime
-- Activity confined to public areas only (sidewalks, streets) without entering property at any time
-
-### Suspicious Activity Indicators (Level 1)
-- **Testing or attempting to open doors/windows/handles on vehicles or buildings** — ALWAYS Level 1 regardless of time or duration
-- **Unidentified person in private areas (driveways, near vehicles/buildings) during late night/early morning (11 PM - 5 AM)** — ALWAYS Level 1 regardless of activity or duration
-- Taking items that don't belong to them (packages, objects from porches/driveways)
-- Climbing or jumping fences/barriers to access property
-- Attempting to conceal actions or items from view
-- Prolonged loitering: remaining in same area without visible purpose throughout most of the sequence
-
-### Critical Threat Indicators (Level 2)
-- Holding break-in tools (crowbars, pry bars, bolt cutters)
-- Weapons visible (guns, knives, bats used aggressively)
-- Forced entry in progress
-- Physical aggression or violence
-- Active property damage or theft in progress
-
-### Assessment Guidance
-Evaluate in this order:
-
-1. **If person is verified/known** → Level 0 regardless of time or activity
-2. **If person is unidentified:**
-   - Check time: If late night/early morning (11 PM - 5 AM) AND in private areas (driveways, near vehicles/buildings) → Level 1
-   - Check actions: If testing doors/handles, taking items, climbing → Level 1
-   - Otherwise, if daytime/evening (6 AM - 10 PM) with clear legitimate purpose (delivery, service worker) → Level 0
-3. **Escalate to Level 2 if:** Weapons, break-in tools, forced entry in progress, violence, or active property damage visible (escalates from Level 0 or 1)
-
-The mere presence of an unidentified person in private areas during late night hours is inherently suspicious and warrants human review, regardless of what activity they appear to be doing or how brief the sequence is.
-```
-
-</details>
-
-### Image Source
-
-By default, review summaries use preview images (cached preview frames) which have a lower resolution but use fewer tokens per image. For better image quality and more detailed analysis, you can configure Frigate to extract frames directly from recordings at a higher resolution:
-
-```yaml
-review:
-  genai:
-    enabled: true
-    image_source: recordings # Options: "preview" (default) or "recordings"
-```
-
-When using `recordings`, frames are extracted at 480px height while maintaining the camera's original aspect ratio, providing better detail for the LLM while being mindful of context window size. This is particularly useful for scenarios where fine details matter, such as identifying license plates, reading text, or analyzing distant objects.
-
-The number of frames sent to the LLM is dynamically calculated based on:
-
-- Your LLM provider's context window size
-- The camera's resolution and aspect ratio (ultrawide cameras like 32:9 use more tokens per image)
-- The image source (recordings use more tokens than preview images)
-
-Frame counts are automatically optimized to use ~98% of the available context window while capping at 20 frames maximum to ensure reasonable inference times. Note that using recordings will:
-
-- Provide higher quality images to the LLM (480p vs 180p preview images)
-- Use more tokens per image due to higher resolution
-- Result in fewer frames being sent for ultrawide cameras due to larger image size
-- Require that recordings are enabled for the camera
-
-If recordings are not available for a given time period, the system will automatically fall back to using preview frames.
-
-### 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.
-
-### Requesting Reports Programmatically
-
-Review reports can be requested via the [API](/integrations/api#review-summarization) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
-
-For Home Assistant users, there is a built-in service (`frigate.generate_review_summary`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -5,11 +5,11 @@ title: Enrichments
 
 # Enrichments
 
-Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accelerated processing.
+Some of Frigate's enrichments can use a discrete GPU 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.
+Object detection and enrichments (like Semantic Search, Face Recognition, and License Plate Recognition) are independent features. To use a GPU 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 and configure the enrichment according to its specific documentation.
 
 - **AMD**
 
@@ -18,20 +18,9 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
 - **Intel**
 
   - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
-  - **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
 
 - **Nvidia**
-
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 
-- **RockChip**
-  - RockChip NPU will automatically be detected and used for semantic search v1 and face recognition in the `-rk` Frigate image.
-
-Utilizing a GPU for enrichments does not require you to use the same GPU for object detection. For example, you can run the `tensorrt` Docker image 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.
-
-:::
+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 for object detection.

docs/docs/configuration/hardware_acceleration_video.md

@@ -9,6 +9,7 @@ It is highly recommended to use a GPU for hardware acceleration video decoding i
 
 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
 
+# Object Detection
 
 ## Raspberry Pi 3/4
 
@@ -70,8 +71,7 @@ Or map in all the `/dev/video*` devices.
 
 | 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                 |
+| 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-\* |                                      |
@@ -175,26 +175,16 @@ 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.
@@ -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
@@ -385,8 +375,13 @@ 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 +389,3 @@ 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/license_plate_recognition.md

@@ -3,18 +3,18 @@ id: license_plate_recognition
 title: License Plate Recognition (LPR)
 ---
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+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`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
-LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
+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:
 
-- Added as a `sub_label` (if [known](#matching)) or the `recognized_license_plate` field (if unknown) to a tracked object.
-- Viewable in the Details pane in Review/History.
+- 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).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
-- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
+- Published via the `frigate/events` MQTT topic as a `sub_label` (known) or `recognized_license_plate` (unknown) for the `car` tracked object.
+- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if known) and `plate`.
 
 ## Model Requirements
 
@@ -24,13 +24,13 @@ Users without a model that detects license plates can still run LPR. Frigate use
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a `car` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` 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.
 
 :::
 
 ## Minimum System Requirements
 
-License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
+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.
 
 ## Configuration
 
@@ -51,7 +51,7 @@ cameras:
       enabled: False
 ```
 
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect 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.
 
@@ -67,15 +67,12 @@ Fine-tune the LPR feature using these optional parameters at the global level of
 - **`min_area`**: Defines the minimum area (in pixels) a license plate must be before recognition runs.
   - Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
   - Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
-- **`device`**: Device to use to run license plate detection _and_ recognition models.
+- **`device`**: Device to use to run license plate 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.
+  - This can be `CPU` or `GPU`. For users without a model that detects license plates natively, using a GPU may increase performance of the models, especially the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
+- **`model_size`**: The size of the model used to detect 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, and uses an enhanced text detector to find characters on multi-line plates. It is significantly slower than the `small` model.
-  - If your country or region does not use multi-line plates, you should use the `small` model as performance is much better for single-line plates.
+  - This can be `small` or `large`. The `large` model uses an enhanced text detector and is more accurate at finding text on plates but slower than the `small` model. For most users, the small model is recommended. For users in countries with multiple lines of text on plates, the large model is recommended. Note that using the large model does not improve _text recognition_, but it may improve _text detection_.
 
 ### Recognition
 
@@ -90,7 +87,7 @@ 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.
@@ -105,32 +102,6 @@ Fine-tune the LPR feature using these optional parameters at the global level of
   - 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 and are applied _before_ the `format` regex, if specified. Each rule must have a `pattern` (which can be a string or a regex, prepended by `r`) and `replacement` (a string, which also supports [backrefs](https://docs.python.org/3/library/re.html#re.sub) like `\1`). These rules are useful for dealing with common OCR issues like noise characters, separators, or confusions (e.g., 'O'→'0').
-
-These rules must be defined at the global level of your `lpr` config.
-
-```yaml
-lpr:
-  replace_rules:
-    - pattern: 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.
@@ -165,9 +136,6 @@ lpr:
   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"
@@ -178,7 +146,7 @@ lpr:
 
 :::note
 
-If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If 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:
@@ -196,17 +164,11 @@ Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to c
 
 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.
+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, zones, 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:
 
@@ -251,11 +213,11 @@ cameras:
 With this setup:
 
 - License plates are treated as normal objects in Frigate.
-- Scores, alerts, detections, and snapshots work as expected.
+- Scores, alerts, detections, snapshots, zones, and object masks 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.
+- 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` 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+)
 
@@ -306,7 +268,7 @@ With this setup:
 - 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](#matching) plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
+- 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.
 
@@ -317,6 +279,7 @@ With this setup:
 | 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                              |
+| Zones & Object Masks    | Supported                                              | Not supported                                                   |
 | 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                     |
@@ -348,9 +311,9 @@ Recognized plates will show as object labels in the debug view and will appear i
 
 If you are still having issues detecting plates, start with a basic configuration and see the debugging tips below.
 
-### Can I run LPR without detecting `car` or `motorcycle` objects?
+### Can I run LPR without detecting `car` 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.
+In normal LPR mode, Frigate requires a `car` 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.
 
 ### How can I improve detection accuracy?
 
@@ -372,37 +335,19 @@ Use `match_distance` to allow small character mismatches. Alternatively, define
 
 ### 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.
+- View MQTT messages for `frigate/events` to verify detected plates.
+- 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`.
+- Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` label will change to the recognized plate when LPR is enabled and working.
+- Adjust `detection_threshold` and `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
+- 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.
+- 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.
 
-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).
+  ```yaml
+  logger:
+    default: info
+    logs:
+      frigate.data_processing.common.license_plate: debug
+  ```
 
 ### Will LPR slow down my system?
 
@@ -412,16 +357,12 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
 
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting `car` 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?
+### It looks like Frigate picked up my camera's timestamp as the license plate. How can I prevent this?
 
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if cars travel close to your camera's timestamp. You could either move the timestamp 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 using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your timestamp.
 
-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.
+If you are using dedicated LPR camera mode, only a _motion mask_ over your timestamp is required.

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.
 
@@ -172,11 +172,9 @@ 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-cameras)
-
-As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.
+To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell)
 
 ### Streaming options on camera group dashboards
 
@@ -191,12 +189,7 @@ 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.
 
 :::
 
@@ -204,52 +197,10 @@ 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.
 
-### Live player error messages
-
-When your browser runs into problems playing back your camera streams, it will log short error messages to the browser console. They indicate playback, codec, or network issues on the client/browser side, not something server side with Frigate itself. Below are the common messages you may see and simple actions you can take to try to resolve them.
-
-- **startup**
-
-  - What it means: The player failed to initialize or connect to the live stream (network or startup error).
-  - What to try: Reload the Live view or click _Reset_. Verify `go2rtc` is running and the camera stream is reachable. Try switching to a different stream from the Live UI dropdown (if available) or use a different browser.
-
-  - Possible console messages from the player code:
-
-    - `Error opening MediaSource.`
-    - `Browser reported a network error.`
-    - `Max error count ${errorCount} exceeded.` (the numeric value will vary)
-
-- **mse-decode**
-
-  - What it means: The browser reported a decoding error while trying to play the stream, which usually is a result of a codec incompatibility or corrupted frames.
-  - What to try: Check the browser console for the supported and negotiated codecs. Ensure your camera/restream is using H.264 video and AAC audio (these are the most compatible). If your camera uses a non-standard audio codec, configure `go2rtc` to transcode the stream to AAC. Try another browser (some browsers have stricter MSE/codec support) and, for iPhone, ensure you're on iOS 17.1 or newer.
-
-  - Possible console messages from the player code:
-
-    - `Safari cannot open MediaSource.`
-    - `Safari reported InvalidStateError.`
-    - `Safari reported decoding errors.`
-
-- **stalled**
-
-  - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
-
-  - Possible console messages from the player code:
-
-    - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
-    - `Media playback has stalled after <n> seconds due to insufficient buffering or a network interruption.` (the seconds value will vary)
-
 ## Live view FAQ
 
 1. **Why don't I have audio in my Live view?**
@@ -266,27 +217,7 @@ When your browser runs into problems playing back your camera streams, it will l
 
    If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
 
-   Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
-
-   - Network issues (e.g., MSE or WebRTC network connection problems).
-   - Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
-   - Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
-   - Browser compatibility problems (e.g., iOS Safari limitations with MSE).
-
-   To view browser console logs:
-
-   1. Open the Frigate Live View in your browser.
-   2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
-   3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
-   4. Look for messages prefixed with the camera name.
-
-   These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
-
-   - Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera_settings_recommendations)).
-   - Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
-   - Test with a different stream via the UI dropdown (if `live -> streams` is configured).
-   - 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](#camera_settings_recommendations).
 
 3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
 
@@ -309,42 +240,3 @@ When your browser runs into problems playing back your camera streams, it will l
 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.
-
-8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
-
-   Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
-
-   To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
-
-   Example: Resolutions from two streams
-
-   - Mismatched (may cause aspect ratio switching on the dashboard):
-
-     - Live/go2rtc stream: 1920x1080 (16:9)
-     - Detect stream: 640x352 (~1.82:1, not 16:9)
-
-   - Matched (prevents switching):
-     - Live/go2rtc stream: 1920x1080 (16:9)
-     - Detect stream: 640x360 (16:9)
-
-   You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
-
-   ```yaml
-   cameras:
-     front_door:
-       detect:
-         width: 640
-         height: 360 # set this to 360 instead of 352
-       ffmpeg:
-         inputs:
-           - path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
-             roles:
-               - record
-           - path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
-             roles:
-               - detect
-   ```

docs/docs/configuration/motion_detection.md

@@ -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

@@ -13,34 +13,34 @@ 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
-      mode: all
+      mode: motion
   detections:
     retain:
       days: 30
-      mode: all
+      mode: motion
 ```
 
 ### Reduced storage: Only saving video when motion is detected
 
-In order to reduce storage requirements, you can adjust your config to only retain video where motion / activity was detected.
+In order to reduce storage requirements, you can adjust your config to only retain video where motion was detected.
 
 ```yaml
 record:
   enabled: True
-  motion:
+  retain:
     days: 3
+    mode: motion
   alerts:
     retain:
       days: 30
@@ -53,12 +53,12 @@ record:
 
 ### Minimum: Alerts only
 
-If you only want to retain video that occurs during activity caused by tracked object(s), this config will discard video unless an alert is ongoing.
+If you only want to retain video that occurs during a tracked object, this config will discard video unless an alert is ongoing.
 
 ```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)
@@ -114,6 +112,38 @@ 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.

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:
@@ -240,13 +223,11 @@ birdseye:
     scaling_factor: 2.0
     # Optional: Maximum number of cameras to show at one time, showing the most recent (default: show all cameras)
     max_cameras: 1
-  # Optional: Frames-per-second to re-send the last composed Birdseye frame when idle (no motion or active updates). (default: shown below)
-  idle_heartbeat_fps: 0.0
 
 # Optional: ffmpeg configuration
 # More information about presets at https://docs.frigate.video/configuration/ffmpeg_presets
 ffmpeg:
-  # Optional: ffmpeg binary path (default: shown below)
+  # Optional: ffmpeg binry path (default: shown below)
   # can also be set to `7.0` or `5.0` to specify one of the included versions
   # or can be set to any path that holds `bin/ffmpeg` & `bin/ffprobe`
   path: "default"
@@ -270,8 +251,6 @@ ffmpeg:
   retry_interval: 10
   # Optional: Set tag on HEVC (H.265) recording stream to improve compatibility with Apple players. (default: shown below)
   apple_compatibility: false
-  # Optional: Set the index of the GPU to use for hardware acceleration. (default: shown below)
-  gpu: 0
 
 # Optional: Detect configuration
 # NOTE: Can be overridden at the camera level
@@ -291,9 +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.
@@ -358,33 +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
@@ -397,8 +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
@@ -413,36 +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: Activity Context Prompt to give context to the GenAI what activity is and is not suspicious.
-    # It is important to be direct and detailed. See documentation for the default prompt structure.
-    activity_context_prompt: """Define what is and is not suspicious
-"""
-    # Optional: Image source for GenAI (default: preview)
-    # Options: "preview" (uses cached preview frames at ~180p) or "recordings" (extracts frames from recordings at 480p)
-    # Using "recordings" provides better image quality but uses more tokens per image.
-    # Frame count is automatically calculated based on context window size, aspect ratio, and image source (capped at 20 frames).
-    image_source: preview
-    # 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
@@ -510,20 +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).
@@ -548,7 +471,7 @@ record:
     # Optional: Retention settings for recordings of alerts
     retain:
       # Required: Retention days (default: shown below)
-      days: 10
+      days: 14
       # Optional: Mode for retention. (default: shown below)
       #   all - save all recording segments for alerts regardless of activity
       #   motion - save all recordings segments for alerts with any detected motion
@@ -568,7 +491,7 @@ record:
     # Optional: Retention settings for recordings of detections
     retain:
       # Required: Retention days (default: shown below)
-      days: 10
+      days: 14
       # Optional: Mode for retention. (default: shown below)
       #   all - save all recording segments for detections regardless of activity
       #   motion - save all recordings segments for detections with any detected motion
@@ -585,7 +508,7 @@ record:
 snapshots:
   # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
   enabled: False
-  # Optional: save a clean copy of the snapshot image (default: shown below)
+  # Optional: save a clean PNG copy of the snapshot image (default: shown below)
   clean_copy: True
   # Optional: print a timestamp on the snapshots (default: shown below)
   timestamp: False
@@ -618,14 +541,11 @@ 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
+# NOTE: Can (enabled, min_area) 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
@@ -636,17 +556,10 @@ face_recognition:
   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: 200
+  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: Configuration for license plate recognition capability
 # NOTE: enabled, min_area, and enhancement can be overridden at the camera level
@@ -654,7 +567,6 @@ 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
@@ -677,79 +589,30 @@ lpr:
   enhancement: 0
   # Optional: Save plate images to /media/frigate/clips/lpr for debugging purposes (default: shown below)
   debug_save_plates: False
-  # Optional: List of regex replacement rules to normalize detected plates (default: shown below)
-  replace_rules: {}
 
-# Optional: Configuration for AI / LLM provider
+# Optional: Configuration for AI generated tracked object descriptions
 # WARNING: Depending on the provider, this will send thumbnails over the internet
-# to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
-# the camera level to enhance privacy for indoor cameras.
+# to Google or OpenAI's LLMs to generate descriptions. It can be overridden at
+# the camera level (enabled: False) to enhance privacy for indoor cameras.
 genai:
-  # Required: Provider must be one of ollama, gemini, or openai
+  # Optional: Enable AI description generation (default: shown below)
+  enabled: False
+  # Required if enabled: Provider must be one of ollama, gemini, or openai
   provider: ollama
   # Required if provider is ollama. May also be used for an OpenAI API compatible backend with the openai provider.
   base_url: http://localhost::11434
   # Required if gemini or openai
   api_key: "{FRIGATE_GENAI_API_KEY}"
-  # Required: The model to use with the provider.
-  model: gemini-1.5-flash
-  # Optional additional args to pass to the GenAI Provider (default: None)
-  provider_options:
-    keep_alive: -1
-
-# Optional: Configuration for audio transcription
-# NOTE: only the enabled option can be overridden at the camera level
-audio_transcription:
-  # Optional: Enable live and speech event audio transcription (default: shown below)
-  enabled: False
-  # Optional: The device to run the models on for live transcription. (default: shown below)
-  device: CPU
-  # Optional: Set the model size used for live transcription. (default: shown below)
-  model_size: small
-  # Optional: Set the language used for transcription translation. (default: shown below)
-  # List of language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
-  language: en
-
-# Optional: Configuration for classification models
-classification:
-  # Optional: Configuration for bird classification
-  bird:
-    # Optional: Enable bird classification (default: shown below)
-    enabled: False
-    # Optional: Minimum classification score required to be considered a match (default: shown below)
-    threshold: 0.9
-  custom:
-    # Required: name of the classification model
-    model_name:
-      # Optional: Enable running the model (default: shown below)
-      enabled: True
-      # Optional: Name of classification model (default: shown below)
-      name: None
-      # Optional: Classification score threshold to change the state (default: shown below)
-      threshold: 0.8
-      # Optional: Number of classification attempts to save in the recent classifications tab (default: shown below)
-      # NOTE: Defaults to 200 for object classification and 100 for state classification if not specified
-      save_attempts: None
-      # Optional: Object classification configuration
-      object_config:
-        # Required: Object types to classify
-        objects: [dog]
-        # Optional: Type of classification that is applied (default: shown below)
-        classification_type: sub_label
-      # Optional: State classification configuration
-      state_config:
-        # Required: Cameras to run classification on
-        cameras:
-          camera_name:
-            # Required: Crop of image frame on this camera to run classification on
-            crop: [0, 180, 220, 400]
-        # Optional: If classification should be run when motion is detected in the crop (default: shown below)
-        motion: False
-        # Optional: Interval to run classification on in seconds (default: shown below)
-        interval: None
+  # Optional: 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 (v1.9.9)
 # NOTE: The default go2rtc API port (1984) must be used,
 #       changing this port for the integrated go2rtc instance is not supported.
 go2rtc:
@@ -848,8 +711,6 @@ cameras:
       # NOTE: This must be different than any camera names, but can match with another zone on another
       #       camera.
       front_steps:
-        # Optional: A friendly name or descriptive text for the zones
-        friendly_name: ""
         # Required: List of x,y coordinates to define the polygon of the zone.
         # NOTE: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
         coordinates: 0.033,0.306,0.324,0.138,0.439,0.185,0.042,0.428
@@ -957,27 +818,33 @@ 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
-        # Optional: A friendly name or descriptive text for the trigger
-        friendly_name: Unique name or descriptive text
-        # 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: shown below)
-        threshold: 0.8
-        # List of actions to perform when the trigger fires. (default: none)
-        # Available options:
-        # - `notification` (send a webpush notification)
-        # - `sub_label` (add trigger friendly name as a sub label to the triggering tracked object)
-        # - `attribute` (add trigger's name and similarity score as a data attribute to the triggering tracked object)
-        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:
@@ -1027,7 +894,7 @@ 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

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/v1.9.9) 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/v1.9.9#configuration) for more advanced configurations and features.
 
 :::note
 
@@ -24,11 +24,6 @@ birdseye:
   restream: True
 ```
 
-:::tip 
-
-To improve connection speed when using Birdseye via restream you can enable a small idle heartbeat by setting `birdseye.idle_heartbeat_fps` to a low value (e.g. `1–2`).  This makes Frigate periodically push the last frame even when no motion is detected, reducing initial connection latency.
-
-:::
 ### Securing Restream With Authentication
 
 The go2rtc restream can be secured with RTSP based username / password authentication. Ex:
@@ -161,7 +156,7 @@ See [this comment](https://github.com/AlexxIT/go2rtc/issues/1217#issuecomment-22
 
 ## 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/v1.9.9#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}}`
 
@@ -169,4 +164,4 @@ NOTE: The output will need to be passed with two curly braces `{{output}}`
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
-```
+```

docs/docs/configuration/review.md

@@ -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`:
 
@@ -72,27 +72,23 @@ 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 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 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.
 
@@ -106,61 +102,3 @@ 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.
-
-:::note
-
-Semantic Search must be enabled to use Triggers.
-
-:::
-
-### Configuration
-
-Triggers are defined within the `semantic_search` configuration for each camera in your Frigate configuration file or through the UI. Each trigger consists of a `friendly_name`, a `type` (either `thumbnail` or `description`), a `data` field (the reference image event ID or text), a `threshold` for similarity matching, and a list of `actions` to perform when the trigger fires - `notification`, `sub_label`, and `attribute`.
-
-Triggers are best configured through the Frigate UI.
-
-#### Managing Triggers in the UI
-
-1. Navigate to the **Settings** page and select the **Triggers** tab.
-2. Choose a camera from the dropdown menu to view or manage its triggers.
-3. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
-4. In the **Create Trigger** wizard:
-   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
-   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
-   - Select the **Type** (`Thumbnail` or `Description`).
-   - 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.
-     If native webpush notifications are enabled, check the `Send Notification` box to send a notification.
-     Check the `Add Sub Label` box to add the trigger's friendly name as a sub label to any triggering tracked objects.
-     Check the `Add Attribute` box to add the trigger's internal ID (e.g., "red_car_alert") to a data attribute on the tracked object that can be processed via the API or MQTT.
-5. Save the trigger to update the configuration and store the embedding in the database.
-
-When a trigger fires, the UI highlights the trigger with a blue dot for 3 seconds for easy identification. Additionally, the UI will show the last date/time and tracked object ID that activated your trigger. The last triggered timestamp is not saved to the database or persisted through restarts of Frigate.
-
-### Usage and Best Practices
-
-1. **Thumbnail Triggers**: Select a representative image (event ID) from the Explore page that closely matches the object you want to detect. For best results, choose images where the object is prominent and fills most of the frame.
-2. **Description Triggers**: Write concise, specific text descriptions (e.g., "Person in a red jacket") that align with the tracked object’s description. Avoid vague terms to improve matching accuracy.
-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. The same word–image pair can give different scores and the score ranges can be too close together to set a clear cutoff.
-
-Text-to-image triggers are not supported due to fundamental limitations of CLIP-based similarity search. While CLIP works well for exploratory, manual queries, it is unreliable for automated triggers based on a threshold. Issues include embedding drift (the same text–image pair can yield different cosine distances over time), lack of true semantic grounding (visually similar but incorrect matches), and unstable thresholding (distance distributions are dataset-dependent and often too tightly clustered to separate relevant from irrelevant results). Instead, it is recommended to set up a workflow with thumbnail triggers: first use text search to manually select 3–5 representative reference tracked objects, then configure thumbnail triggers based on that visual similarity. This provides robust automation without the semantic ambiguity of text to image matching.

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

@@ -27,7 +27,6 @@ cameras:
           - entire_yard
     zones:
       entire_yard:
-        friendly_name: Entire yard # You can use characters from any language text
         coordinates: ...
 ```
 
@@ -45,10 +44,8 @@ cameras:
           - edge_yard
     zones:
       edge_yard:
-        friendly_name: Edge yard # You can use characters from any language text
         coordinates: ...
       inner_yard:
-        friendly_name: Inner yard # You can use characters from any language text
         coordinates: ...
 ```
 
@@ -62,7 +59,6 @@ cameras:
         - entire_yard
     zones:
       entire_yard:
-        friendly_name: Entire yard
         coordinates: ...
 ```
 
@@ -86,16 +82,13 @@ cameras:
 
 Only car objects can trigger the `front_yard_street` zone and only person can trigger the `entire_yard`. Objects will be tracked for any `person` that enter anywhere in the yard, and for cars only if they enter the street.
 
-
 ### Zone Loitering
 
 Sometimes objects are expected to be passing through a zone, but an object loitering in an area is unexpected. Zones can be configured to have a minimum loitering time after which the object will be considered in the zone.
 
 :::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.
+When using loitering zones, a review item will remain active until the object leaves. Loitering zones are only meant to be used in areas where loitering is not expected behavior.
 
 :::
 
@@ -143,7 +136,7 @@ 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.
 
@@ -172,9 +165,8 @@ 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/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.
 

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

@@ -3,44 +3,29 @@ id: hardware
 title: Recommended hardware
 ---
 
-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 ## Cameras
 
 Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
 
 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.
+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 Hailo8 or Google Coral. I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.
 
-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.
-
-:::
-
-| 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. |
+| Name                                                                                                          | Notes                                                                                     |
+| ------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4iQaBKu" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
 
 ## Detectors
 
@@ -58,52 +43,33 @@ Frigate supports multiple different detectors that work on different types of ha
   - Runs best with tiny or small size models
 
 - [Google Coral EdgeTPU](#google-coral-tpu): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
-
   - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
 
-- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
-  - [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)
+- [ROCm](#amd-gpus): ROCm can run on AMD Discrete GPUs to provide efficient object detection
+  - [Supports limited model architectures](../../configuration/object_detectors#supported-models-1)
   - 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 NPUs to provide efficient object detection.
-  - [Supports majority of model architectures](../../configuration/object_detectors#openvino-supported-models)
+- [OpenVino](#openvino): 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#supported-models)
   - Runs best with tiny, small, or medium models
 
 **Nvidia**
 
-- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs to provide efficient object detection.
-
-  - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#onnx-supported-models)
+- [TensortRT](#tensorrt---nvidia-gpu): TensorRT can run on Nvidia GPUs and Jetson devices.
+  - [Supports majority of model architectures via ONNX](../../configuration/object_detectors#supported-models-2)
   - Runs well with any size models including large
 
-- <CommunityBadge /> [Jetson](#nvidia-jetson): Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6.
-
-**Rockchip** <CommunityBadge />
+**Rockchip**
 
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs to provide efficient object detection.
   - [Supports limited model architectures](../../configuration/object_detectors#choosing-a-model)
   - Runs best with tiny or small size models
   - Runs efficiently on low power hardware
 
-**Synaptics** <CommunityBadge />
-
-- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs to provide efficient object detection.
-
 :::
 
 ### Hailo-8
@@ -120,7 +86,6 @@ In real-world deployments, even with multiple cameras running concurrently, Frig
 | 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                 |
 
 ### Google Coral TPU
@@ -132,132 +97,61 @@ Frigate supports both the USB and M.2 versions of the Google Coral.
 
 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
-- Intel NPUs
+- 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                    | t-320: 14ms s-320: 24ms t-640: 34ms s-640: 65ms   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
-| Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
-| Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
-| Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
-| Intel Iris XE  | ~ 10 ms                    | t-320: 6 ms t-640: 14 ms s-320: 8 ms s-640: 16 ms | 320: ~ 10 ms 640: ~ 20 ms | 320-n: 33 ms           |                                    |
-| Intel NPU      | ~ 6 ms                     | s-320: 11 ms                                      | 320: ~ 14 ms 640: ~ 34 ms | 320-n: 40 ms           |                                    |
-| Intel Arc A310 | ~ 5 ms                     | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms | 320: ~ 8 ms 640: ~ 14 ms  |                        |                                    |
-| Intel Arc A380 | ~ 6 ms                     |                                                   | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms  |                                    |
-| Intel Arc A750 | ~ 4 ms                     |                                                   | 320: ~ 8 ms               |                        |                                    |
+| Name           | MobileNetV2 Inference Time | 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                    | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
+| Intel N100     | ~ 15 ms                    | 320: ~ 20 ms              |                        |                                    |
+| Intel Iris XE  | ~ 10 ms                    | 320: ~ 18 ms 640: ~ 50 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               |                        |                                    |
 
 ### 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   | RF-DETR 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 | 336: ~ 16 ms 560: ~ 40 ms |
+| RTX 3070 Mobile | ~ 5 ms                |                           |                           |
+| RTX 3070        | 4 - 6 ms              | 320: ~ 6 ms 640: ~ 12 ms  | 336: ~ 14 ms 560: ~ 36 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.
-
-:::warning
-
-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.
-
-:::
-
-| 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                           |
-
-### ROCm - AMD GPU
-
-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 |
+| Name     | YOLOv9 Inference Time | YOLO-NAS Inference Time   |
+| -------- | --------------------- | ------------------------- |
+| AMD 780M | ~ 14 ms               | 320: ~ 30 ms 640: ~ 60 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
 
-Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
+Frigate supports all Jetson boards, from the inexpensive Jetson Nano to the powerful Jetson Orin AGX. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson-orin-agx-orin-nx-orin-nano-xavier-agx-xavier-nx-tx2-tx1-nano) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
 
 Inference speed will vary depending on the YOLO model, jetson platform and jetson nvpmodel (GPU/DLA/EMC clock speed). It is typically 20-40 ms for most models. The DLA is more efficient than the GPU, but not faster, so using the DLA will reduce power consumption but will slightly increase inference time.
 
@@ -278,15 +172,6 @@ Frigate supports hardware video processing on all Rockchip boards. However, hard
 
 The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms for yolo-nas s.
 
-### Synaptics
-
-- **Synaptics** Default model is **mobilenet**
-
-| Name          | Synaptics SL1680 Inference Time |
-| ------------- | ------------------------------- |
-| ssd mobilenet | ~ 25 ms                         |
-| yolov5m       | ~ 118 ms                        |
-
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
@@ -307,4 +192,4 @@ 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/installation.md

@@ -43,7 +43,7 @@ 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
 
@@ -56,7 +56,7 @@ services:
     volumes:
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # Recommended: 1GB of memory
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
         target: /tmp/cache
         tmpfs:
           size: 1000000000
@@ -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
 
@@ -256,37 +185,6 @@ or add these options to your `docker run` command:
 
 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).
-
 ## Docker
 
 Running through Docker with Docker Compose is the recommended install method.
@@ -304,13 +202,12 @@ services:
       - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
       - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
       - /dev/video11:/dev/video11 # For Raspberry Pi 4B
-      - /dev/dri/renderD128:/dev/dri/renderD128 # AMD / Intel GPU, needs to be updated for your hardware
-      - /dev/accel:/dev/accel # Intel NPU
+      - /dev/dri/renderD128:/dev/dri/renderD128 # For intel hwaccel, needs to be updated for your hardware
     volumes:
       - /etc/localtime:/etc/localtime:ro
       - /path/to/your/config:/config
       - /path/to/your/storage:/media/frigate
-      - type: tmpfs # Recommended: 1GB of memory
+      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
         target: /tmp/cache
         tmpfs:
           size: 1000000000

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.

docs/docs/frigate/updating.md

@@ -1,119 +0,0 @@
----
-id: updating
-title: Updating
----
-
-# Updating Frigate
-
-The current stable version of Frigate is **0.16.2**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.2).
-
-Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
-
-## Before You Begin
-
-- **Stop Frigate**: For most methods, you’ll need to stop the running Frigate instance before backing up and updating.
-- **Backup Your Configuration**: Always back up your `/config` directory (e.g., `config.yml` and `frigate.db`, the SQLite database) before updating. This ensures you can roll back if something goes wrong.
-- **Check Release Notes**: Carefully review the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases) for breaking changes or configuration updates that might affect your setup.
-
-## Updating with Docker
-
-If you’re running Frigate via Docker (recommended method), follow these steps:
-
-1. **Stop the Container**:
-
-   - If using Docker Compose:
-     ```bash
-     docker compose down frigate
-     ```
-   - If using `docker run`:
-     ```bash
-     docker stop frigate
-     ```
-
-2. **Update and Pull the Latest Image**:
-
-   - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
-       ```yaml
-       services:
-         frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.16.2
-       ```
-     - Then pull the image:
-       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
-       ```
-     - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
-   - If using `docker run`:
-     - Pull the image with the appropriate tag (e.g., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
-       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
-       ```
-
-3. **Start the Container**:
-
-   - If using Docker Compose:
-     ```bash
-     docker compose up -d
-     ```
-   - If using `docker run`, re-run your original command (e.g., from the [Installation](./installation.md#docker) section) with the updated image tag.
-
-4. **Verify the Update**:
-   - Check the container logs to ensure Frigate starts successfully:
-     ```bash
-     docker logs frigate
-     ```
-   - Visit the Frigate Web UI (default: `http://<your-ip>:5000`) to confirm the new version is running. The version number is displayed at the top of the System Metrics page.
-
-### Notes
-
-- If you’ve customized other settings (e.g., `shm-size`), ensure they’re still appropriate after the update.
-- Docker will automatically use the updated image when you restart the container, as long as you pulled the correct version.
-
-## Updating the Home Assistant Addon
-
-For users running Frigate as a Home Assistant Addon:
-
-1. **Check for Updates**:
-
-   - Navigate to **Settings > Add-ons** in Home Assistant.
-   - Find your installed Frigate addon (e.g., "Frigate NVR" or "Frigate NVR (Full Access)").
-   - If an update is available, you’ll see an "Update" button.
-
-2. **Update the Addon**:
-
-   - Click the "Update" button next to the Frigate addon.
-   - Wait for the process to complete. Home Assistant will handle downloading and installing the new version.
-
-3. **Restart the Addon**:
-
-   - After updating, go to the addon’s page and click "Restart" to apply the changes.
-
-4. **Verify the Update**:
-   - Check the addon logs (under the "Log" tab) to ensure Frigate starts without errors.
-   - Access the Frigate Web UI to confirm the new version is running.
-
-### Notes
-
-- Ensure your `/config/frigate.yml` is compatible with the new version by reviewing the [Release notes](https://github.com/blakeblackshear/frigate/releases).
-- If using custom hardware (e.g., Coral or GPU), verify that configurations still work, as addon updates don’t modify your hardware settings.
-
-## Rolling Back
-
-If an update causes issues:
-
-1. Stop Frigate.
-2. Restore your backed-up config file and database.
-3. Revert to the previous image version:
-   - For Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`) in your `docker run` command.
-   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.15.2`), and re-run `docker compose up -d`.
-   - For Home Assistant: Reinstall the previous addon version manually via the repository if needed and restart the addon.
-4. Verify the old version is running again.
-
-## Troubleshooting
-
-- **Container Fails to Start**: Check logs (`docker logs frigate`) for errors.
-- **UI Not Loading**: Ensure ports (e.g., 5000, 8971) are still mapped correctly and the service is running.
-- **Hardware Issues**: Revisit hardware-specific setup (e.g., Coral, GPU) if detection or decoding fails post-update.
-
-Common questions are often answered in the [FAQ](https://github.com/blakeblackshear/frigate/discussions), pinned at the top of the support discussions.

docs/docs/frigate/video_pipeline.md

@@ -15,10 +15,10 @@ At a high level, there are five processing steps that could be applied to a came
 %%{init: {"themeVariables": {"edgeLabelBackground": "transparent"}}}%%
 
 flowchart LR
-    Feed(Feed acquisition) --> Decode(Video decoding)
-    Decode --> Motion(Motion detection)
-    Motion --> Object(Object detection)
-    Feed --> Recording(Recording and visualization)
+    Feed(Feed\nacquisition) --> Decode(Video\ndecoding)
+    Decode --> Motion(Motion\ndetection)
+    Motion --> Object(Object\ndetection)
+    Feed --> Recording(Recording\nand\nvisualization)
     Motion --> Recording
     Object --> Recording
 ```

docs/docs/guides/configuring_go2rtc.md

@@ -3,15 +3,17 @@ id: configuring_go2rtc
 title: Configuring go2rtc
 ---
 
+# Configuring go2rtc
+
 Use of the bundled go2rtc is optional. You can still configure FFmpeg to connect directly to your cameras. However, adding go2rtc to your configuration is required for the following features:
 
 - WebRTC or MSE for live viewing with audio, higher resolutions and frame rates than the jsmpeg stream which is limited to the detect stream and does not support audio
 - Live stream support for cameras in Home Assistant Integration
 - RTSP relay for use with other consumers to reduce the number of connections to your camera streams
 
-## Setup a go2rtc stream
+# Setup a go2rtc stream
 
-First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#module-streams), not just rtsp.
+First, you will want to configure go2rtc to connect to your camera stream by adding the stream you want to use for live view in your Frigate config file. Avoid changing any other parts of your config at this step. Note that go2rtc supports [many different stream types](https://github.com/AlexxIT/go2rtc/tree/v1.9.9#module-streams), not just rtsp.
 
 :::tip
 
@@ -47,8 +49,8 @@ After adding this to the config, restart Frigate and try to watch the live strea
 - Check Video Codec:
 
   - If the camera stream works in go2rtc but not in your browser, the video codec might be unsupported.
-  - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#codecs-madness) in go2rtc documentation.
-  - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
+  - If using H265, switch to H264. Refer to [video codec compatibility](https://github.com/AlexxIT/go2rtc/tree/v1.9.9#codecs-madness) in go2rtc documentation.
+  - If unable to switch from H265 to H264, or if the stream format is different (e.g., MJPEG), re-encode the video using [FFmpeg parameters](https://github.com/AlexxIT/go2rtc/tree/v1.9.9#source-ffmpeg). It supports rotating and resizing video feeds and hardware acceleration. Keep in mind that transcoding video from one format to another is a resource intensive task and you may be better off using the built-in jsmpeg view.
     ```yaml
     go2rtc:
       streams:
@@ -109,11 +111,11 @@ section.
 
 :::
 
-### Next steps
+## Next steps
 
 1. If the stream you added to go2rtc is also used by Frigate for the `record` or `detect` role, you can migrate your config to pull from the RTSP restream to reduce the number of connections to your camera as shown [here](/configuration/restream#reduce-connections-to-camera).
-2. You can [set up WebRTC](/configuration/live#webrtc-extra-configuration) if your camera supports two-way talk. Note that WebRTC only supports specific audio formats and may require opening ports on your router.
+2. You may also prefer to [setup WebRTC](/configuration/live#webrtc-extra-configuration) for slightly lower latency than MSE. Note that WebRTC only supports h264 and specific audio formats and may require opening ports on your router.
 
-## Homekit Configuration
+## Important considerations
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to share export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+If you are configuring go2rtc to publish HomeKit camera streams, on pairing the configuration is written to the `/dev/shm/go2rtc.yaml` file inside the container. These changes must be manually copied across to the `go2rtc` section of your Frigate configuration in order to persist through restarts.

docs/docs/guides/reverse_proxy.md

@@ -35,7 +35,6 @@ There are many solutions available to implement reverse proxies and the communit
 * [Apache2](#apache2-reverse-proxy)
 * [Nginx](#nginx-reverse-proxy)
 * [Traefik](#traefik-reverse-proxy)
-* [Caddy](#caddy-reverse-proxy)
 
 ## Apache2 Reverse Proxy
 
@@ -118,8 +117,7 @@ server {
   set $port           8971;
 
   listen 80;
-  listen 443 ssl;
-  http2 on;
+  listen 443 ssl http2;
 
   server_name frigate.domain.com;
 }
@@ -179,33 +177,3 @@ The above configuration will create a "service" in Traefik, automatically adding
 It will also add a router, routing requests to "traefik.example.com" to your local container.
 
 Note that with this approach, you don't need to expose any ports for the Frigate instance since all traffic will be routed over the internal Docker network.
-
-## Caddy Reverse Proxy
-
-This example shows Frigate running under a subdomain with logging and a tls cert (in this case a wildcard domain cert obtained independently of caddy) handled via imports
-
-```caddy
-(logging) {
-        log {
-                output file /var/log/caddy/{args[0]}.log {
-                        roll_size 10MiB
-                        roll_keep 5
-                        roll_keep_for 10d
-                }
-                format json
-                level INFO
-        }
-}
-
-
-(tls) {
-        tls /var/lib/caddy/wildcard.YOUR_DOMAIN.TLD.fullchain.pem /var/lib/caddy/wildcard.YOUR_DOMAIN.TLD.privkey.pem
-}
-
-frigate.YOUR_DOMAIN.TLD {
-        reverse_proxy http://localhost:8971 
-        import tls
-        import logging frigate.YOUR_DOMAIN.TLD
-}
-
-```

docs/docs/integrations/home-assistant.md

@@ -110,14 +110,6 @@ If you run Frigate on a separate device within your local network, Home Assistan
 
 Use `http://<frigate_device_ip>:8971` as the URL for the integration so that authentication is required.
 
-:::tip
-
-The above URL assumes you have [disabled TLS](../configuration/tls).
-By default, TLS is enabled and Frigate will be using a self-signed certificate. HomeAssistant will fail to connect HTTPS to port 8971 since it fails to verify the self-signed certificate.
-Either disable TLS and use HTTP from HomeAssistant, or configure Frigate to be acessible with a valid certificate.
-
-:::
-
 ```yaml
 services:
   frigate:
@@ -185,26 +177,6 @@ For clips to be castable to media devices, audio is required and may need to be
 
 <a name="api"></a>
 
-## Camera API
-
-To disable a camera dynamically
-
-```
-action: camera.turn_off
-data: {}
-target:
-  entity_id: camera.back_deck_cam  # your Frigate camera entity ID
-```
-
-To enable a camera that has been disabled dynamically
-
-```
-action: camera.turn_on
-data: {}
-target:
-  entity_id: camera.back_deck_cam  # your Frigate camera entity ID
-```
-
 ## Notification API
 
 Many people do not want to expose Frigate to the web, so the integration creates some public API endpoints that can be used for notifications.

docs/docs/integrations/homekit.md

@@ -1,37 +0,0 @@
----
-id: homekit
-title: HomeKit
----
-
-Frigate cameras can be integrated with Apple HomeKit through go2rtc. This allows you to view your camera streams directly in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
-
-## Overview
-
-HomeKit integration is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server to expose your cameras to HomeKit.
-
-## Setup
-
-All HomeKit configuration and pairing should be done through the **go2rtc WebUI**.
-
-### Accessing the go2rtc WebUI
-
-The go2rtc WebUI is available at:
-
-```
-http://<frigate_host>:1984
-```
-
-Replace `<frigate_host>` with the IP address or hostname of your Frigate server.
-
-### Pairing Cameras
-
-1. Navigate to the go2rtc WebUI at `http://<frigate_host>:1984`
-2. Use the `add` section to add a new camera to HomeKit
-3. Follow the on-screen instructions to generate pairing codes for your cameras
-
-## Requirements
-
-- Frigate must be accessible on your local network using host network_mode
-- Your iOS device must be on the same network as Frigate
-- Port 1984 must be accessible for the go2rtc WebUI
-- For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc)

docs/docs/integrations/mqtt.md

@@ -28,14 +28,7 @@ Message published for each changed tracked object. The first message is publishe
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123961.837752,
-    "snapshot": {
-      "frame_time": 1607123965.975463,
-      "box": [415, 489, 528, 700],
-      "area": 12728,
-      "region": [260, 446, 660, 846],
-      "score": 0.77546,
-      "attributes": []
-    },
+    "snapshot_time": 1607123961.837752,
     "label": "person",
     "sub_label": null,
     "top_score": 0.958984375,
@@ -61,7 +54,6 @@ Message published for each changed tracked object. The first message is publishe
     }, // attributes with top score that have been identified on the object at any point
     "current_attributes": [], // detailed data about the current attributes in this frame
     "current_estimated_speed": 0.71, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
-    "average_estimated_speed": 14.3, // average estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
     "velocity_angle": 180, // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
     "recognized_license_plate": "ABC12345", // a recognized license plate for car objects
     "recognized_license_plate_score": 0.933451
@@ -70,14 +62,7 @@ Message published for each changed tracked object. The first message is publishe
     "id": "1607123955.475377-mxklsc",
     "camera": "front_door",
     "frame_time": 1607123962.082975,
-    "snapshot": {
-      "frame_time": 1607123965.975463,
-      "box": [415, 489, 528, 700],
-      "area": 12728,
-      "region": [260, 446, 660, 846],
-      "score": 0.77546,
-      "attributes": []
-    },
+    "snapshot_time": 1607123961.837752,
     "label": "person",
     "sub_label": ["John Smith", 0.79],
     "top_score": 0.958984375,
@@ -110,7 +95,6 @@ Message published for each changed tracked object. The first message is publishe
       }
     ],
     "current_estimated_speed": 0.77, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
-    "average_estimated_speed": 14.31, // average estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
     "velocity_angle": 180, // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
     "recognized_license_plate": "ABC12345", // a recognized license plate for car objects
     "recognized_license_plate_score": 0.933451
@@ -141,7 +125,7 @@ Message published for updates to tracked object metadata, for example:
   "name": "John",
   "score": 0.95,
   "camera": "front_door_cam",
-  "timestamp": 1607123958.748393
+  "timestamp": 1607123958.748393,
 }
 ```
 
@@ -155,53 +139,13 @@ Message published for updates to tracked object metadata, for example:
   "plate": "123ABC",
   "score": 0.95,
   "camera": "driveway_cam",
-  "timestamp": 1607123958.748393
-}
-```
-
-#### Object Classification Update
-
-Message published when [object classification](/configuration/custom_classification/object_classification) reaches consensus on a classification result.
-
-**Sub label type:**
-
-```json
-{
-  "type": "classification",
-  "id": "1607123955.475377-mxklsc",
-  "camera": "front_door_cam",
   "timestamp": 1607123958.748393,
-  "model": "person_classifier",
-  "sub_label": "delivery_person",
-  "score": 0.87
-}
-```
-
-**Attribute type:**
-
-```json
-{
-  "type": "classification",
-  "id": "1607123955.475377-mxklsc",
-  "camera": "front_door_cam",
-  "timestamp": 1607123958.748393,
-  "model": "helmet_detector",
-  "attribute": "yes",
-  "score": 0.92
 }
 ```
 
 ### `frigate/reviews`
 
-Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated.
-
-An `update` with the same ID will be published when:
-
-- The severity changes from `detection` to `alert`
-- Additional objects are detected
-- An object is recognized via face, lpr, etc.
-
-When the review activity has ended a final `end` message is published.
+Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. When additional objects are detected or when a zone change occurs, it will publish a, `update` message with the same id. When the review activity has ended a final `end` message is published.
 
 ```json
 {
@@ -248,20 +192,6 @@ When the review activity has ended a final `end` message is published.
 }
 ```
 
-### `frigate/triggers`
-
-Message published when a trigger defined in a camera's `semantic_search` configuration fires.
-
-```json
-{
-  "name": "car_trigger",
-  "camera": "driveway",
-  "event_id": "1751565549.853251-b69j73",
-  "type": "thumbnail",
-  "score": 0.85
-}
-```
-
 ### `frigate/stats`
 
 Same data available at `/api/stats` published at a configurable interval.
@@ -280,14 +210,6 @@ Topic with current state of notifications. Published values are `ON` and `OFF`.
 
 ## Frigate Camera Topics
 
-### `frigate/<camera_name>/<role>/status`
-
-Publishes the current health status of each role that is enabled (`audio`, `detect`, `record`). Possible values are:
-
-- `online`: Stream is running and being processed
-- `offline`: Stream is offline and is being restarted
-- `disabled`: Camera is currently disabled
-
 ### `frigate/<camera_name>/<object_name>`
 
 Publishes the count of objects for the camera for use as a sensor in Home Assistant.
@@ -321,8 +243,6 @@ The height and crop of snapshots can be configured in the config.
 
 Publishes "ON" when a type of audio is detected and "OFF" when it is not for the camera for use as a sensor in Home Assistant.
 
-`all` can be used as the audio_type for the status of all audio types.
-
 ### `frigate/<camera_name>/audio/dBFS`
 
 Publishes the dBFS value for audio detected on this camera.
@@ -335,17 +255,6 @@ Publishes the rms value for audio detected on this camera.
 
 **NOTE:** Requires audio detection to be enabled
 
-### `frigate/<camera_name>/audio/transcription`
-
-Publishes transcribed text for audio detected on this camera.
-
-**NOTE:** Requires audio detection and transcription to be enabled
-
-### `frigate/<camera_name>/classification/<model_name>`
-
-Publishes the current state detected by a state classification model for the camera. The topic name includes the model name as configured in your classification settings.
-The published value is the detected state class name (e.g., `open`, `closed`, `on`, `off`). The state is only published when it changes, helping to reduce unnecessary MQTT traffic.
-
 ### `frigate/<camera_name>/enabled/set`
 
 Topic to turn Frigate's processing of a camera on and off. Expected values are `ON` and `OFF`.
@@ -468,22 +377,6 @@ Topic to turn review detections for a camera on or off. Expected values are `ON`
 
 Topic with current state of review detections for a camera. Published values are `ON` and `OFF`.
 
-### `frigate/<camera_name>/object_descriptions/set`
-
-Topic to turn generative AI object descriptions for a camera on or off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/object_descriptions/state`
-
-Topic with current state of generative AI object descriptions for a camera. Published values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/review_descriptions/set`
-
-Topic to turn generative AI review descriptions for a camera on or off. Expected values are `ON` and `OFF`.
-
-### `frigate/<camera_name>/review_descriptions/state`
-
-Topic with current state of generative AI review descriptions for a camera. Published values are `ON` and `OFF`.
-
 ### `frigate/<camera_name>/birdseye/set`
 
 Topic to turn Birdseye for a camera on and off. Expected values are `ON` and `OFF`. Birdseye mode

docs/docs/integrations/plus.md

@@ -43,7 +43,7 @@ Snapshots must be enabled to be able to submit examples to Frigate+
 
 ### Annotate and verify
 
-You can view all of your submitted images at [https://plus.frigate.video](https://plus.frigate.video). Annotations can be added by clicking an image. For more detailed information about labeling, see the documentation on [annotating](../plus/annotating.md).
+You can view all of your submitted images at [https://plus.frigate.video](https://plus.frigate.video). Annotations can be added by clicking an image. For more detailed information about labeling, see the documentation on [improving your model](../plus/improving_model.md).
 
 ![Annotate](/img/annotate.png)
 

docs/docs/integrations/third_party_extensions.md

@@ -13,10 +13,6 @@ Please use your own knowledge to assess and vet them before you install anything
 
 :::
 
-## [Advanced Camera Card (formerly known as Frigate Card](https://card.camera/#/README)
-
-The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant dashboard card with deep Frigate integration.
-
 ## [Double Take](https://github.com/skrashevich/double-take)
 
 [Double Take](https://github.com/skrashevich/double-take) provides an unified UI and API for processing and training images for facial recognition.
@@ -27,14 +23,6 @@ This is a fork (with fixed errors and new features) of [original Double Take](ht
 
 [Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
 
-## [Frigate Snap-Sync](https://github.com/thequantumphysicist/frigate-snap-sync/)
-
-[Frigate Snap-Sync](https://github.com/thequantumphysicist/frigate-snap-sync/) is a program that works in tandem with Frigate. It responds to Frigate when a snapshot or a review is made (and more can be added), and uploads them to one or more remote server(s) of your choice.
-
 ## [Frigate telegram](https://github.com/OldTyT/frigate-telegram)
 
 [Frigate telegram](https://github.com/OldTyT/frigate-telegram) makes it possible to send events from Frigate to Telegram. Events are sent as a message with a text description, video, and thumbnail.
-
-## [Periscope](https://github.com/maksz42/periscope)
-
-[Periscope](https://github.com/maksz42/periscope) is a lightweight Android app that turns old devices into live viewers for Frigate. It works on Android 2.2 and above, including Android TV. It supports authentication and HTTPS.

docs/docs/plus/first_model.md

@@ -9,11 +9,11 @@ Before requesting your first model, you will need to upload and verify at least
 
 It is recommended to submit **both** true positives and false positives. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
 
-For more detailed recommendations, you can refer to the docs on [annotating](./annotating.md).
+For more detailed recommendations, you can refer to the docs on [improving your model](./improving_model.md).
 
 ## Step 2: Submit a model request
 
-Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). If you are unsure which type to request, you can test the base model for each version from the "Base Models" tab. Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
+Once you have an initial set of verified images, you can request a model on the Models page. For guidance on choosing a model type, refer to [this part of the documentation](./index.md#available-model-types). Each model request requires 1 of the 12 trainings that you receive with your annual subscription. This model will support all [label types available](./index.md#available-label-types) even if you do not submit any examples for those labels. Model creation can take up to 36 hours.
 ![Plus Models Page](/img/plus/plus-models.jpg)
 
 ## Step 3: Set your model id in the config
@@ -34,12 +34,6 @@ Model IDs are not secret values and can be shared freely. Access to your model i
 
 :::
 
-:::tip
-
-When setting the plus model id, all other fields should be removed as these are configured automatically with the Frigate+ model config
-
-:::
-
 ## Step 4: Adjust your object filters for higher scores
 
 Frigate+ models generally have much higher scores than the default model provided in Frigate. You will likely need to increase your `threshold` and `min_score` values. Here is an example of how these values can be refined, but you should expect these to evolve as your model improves. For more information about how `threshold` and `min_score` are related, see the docs on [object filters](../configuration/object_filters.md#object-scores).

docs/docs/plus/improving_model.md

@@ -1,9 +1,17 @@
 ---
-id: annotating
-title: Annotating your images
+id: improving_model
+title: Improving your model
 ---
 
-For the best results, follow these guidelines. You may also want to review the documentation on [improving your model](./index.md#improving-your-model).
+You may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
+
+- **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
+- **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
+- **Submit diverse images**. For the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. As circumstances change, you may need to submit new examples to address new types of false positives. For example, the change from summer days to snowy winter days or other changes such as a new grill or patio furniture may require additional examples and training.
+
+## Properly labeling images
+
+For the best results, follow the following guidelines.
 
 **Label every object in the image**: It is important that you label all objects in each image before verifying. If you don't label a car for example, the model will be taught that part of the image is _not_ a car and it will start to get confused. You can exclude labels that you don't want detected on any of your cameras.
 
@@ -17,17 +25,9 @@ For the best results, follow these guidelines. You may also want to review the d
 
 ![Fedex Logo](/img/plus/fedex-logo.jpg)
 
-## AI suggested labels
-
-If you have an active Frigate+ subscription, new uploads will be scanned for the objects configured for you camera and you will see suggested labels as light blue boxes when annotating in Frigate+. These suggestions are processed via a queue and typically complete within a minute after uploading, but processing times can be longer.
-
-![Suggestions](/img/plus/suggestions.webp)
-
-Suggestions are converted to labels when saving, so you should remove any errant suggestions. There is already some logic designed to avoid duplicate labels, but you may still occasionally see some duplicate suggestions. You should keep the most accurate bounding box and delete any duplicates so that you have just one label per object remaining.
-
 ## False positive labels
 
-False positives will be shown with a red box and the label will have a strike through. These can't be adjusted, but they can be deleted if you accidentally submit a true positive as a false positive from Frigate.
+False positives will be shown with a read box and the label will have a strike through.
 ![false positive](/img/plus/false-positive.jpg)
 
 Misidentified objects should have a correct label added. For example, if a person was mistakenly detected as a cat, you should submit it as a false positive in Frigate and add a label for the person. The boxes will overlap.
@@ -42,7 +42,6 @@ Misidentified objects should have a correct label added. For example, if a perso
 | `w`               | Add box                       |
 | `d`               | Toggle difficult              |
 | `s`               | Switch to the next label      |
-| `Shift + s`       | Switch to the previous label  |
 | `tab`             | Select next largest box       |
 | `del`             | Delete current box            |
 | `esc`             | Deselect/Cancel               |

docs/docs/plus/index.md

@@ -3,90 +3,61 @@ id: index
 title: Models
 ---
 
-<a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate NVR analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a base model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
+<a href="https://frigate.video/plus" target="_blank" rel="nofollow">Frigate+</a> offers models trained on images submitted by Frigate+ users from their security cameras and is specifically designed for the way Frigate analyzes video footage. These models offer higher accuracy with less resources. The images you upload are used to fine tune a baseline model trained from images uploaded by all Frigate+ users. This fine tuning process results in a model that is optimized for accuracy in your specific conditions.
 
-With a subscription, 12 model trainings to fine tune your model per year are included. In addition, you will have access to any base models published while your subscription is active. If you cancel your subscription, you will retain access to any trained and base models in your account. An active subscription is required to submit model requests or purchase additional trainings. New base models are published quarterly with target dates of January 15th, April 15th, July 15th, and October 15th.
+:::info
+
+The baseline model isn't directly available after subscribing. This may change in the future, but for now you will need to submit a model request with the minimum number of images.
+
+:::
+
+With a subscription, 12 model trainings per year are included. If you cancel your subscription, you will retain access to any trained models. An active subscription is required to submit model requests or purchase additional trainings.
 
 Information on how to integrate Frigate+ with Frigate can be found in the [integration docs](../integrations/plus.md).
 
 ## Available model types
 
-There are three model types offered in Frigate+, `mobiledet`, `yolonas`, and `yolov9`. All of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
+There are two model types offered in Frigate+, `mobiledet` and `yolonas`. Both of these models are object detection models and are trained to detect the same set of labels [listed below](#available-label-types).
 
-Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types). You can test model types for compatibility and speed on your hardware by using the base models.
+Not all model types are supported by all detectors, so it's important to choose a model type to match your detector as shown in the table under [supported detector types](#supported-detector-types).
 
-| Model Type  | Description                                                                                                                                                                                                                        |
-| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                                                                                                                  |
-| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs.                                                                                       |
-| `yolov9`    | A leading SOTA (state of the art) object detection model with similar performance to yolonas, but on a wider range of hardware options. Runs on Intel, NVidia GPUs, AMD GPUs, Hailo, MemryX\*, Apple Silicon\*, and Rockchip NPUs. |
-
-_\* Support coming in 0.17_
-
-### YOLOv9 Details
-
-YOLOv9 models are available in `s` and `t` sizes. When requesting a `yolov9` model, you will be prompted to choose a size. If you are unsure what size to choose, you should perform some tests with the base models to find the performance level that suits you. The `s` size is most similar to the current `yolonas` models in terms of inference times and accuracy, and a good place to start is the `320x320` resolution model for `yolov9s`.
-
-:::info
-
-When switching to YOLOv9, you may need to adjust your thresholds for some objects.
-
-:::
-
-#### Hailo Support
-
-If you have a Hailo device, you will need to specify the hardware you have when submitting a model request because they are not cross compatible. Please test using the available base models before submitting your model request.
-
-#### Rockchip (RKNN) Support
-
-For 0.16, YOLOv9 onnx models will need to be manually converted. First, you will need to configure Frigate to use the model id for your YOLOv9 onnx model so it downloads the model to your `model_cache` directory. From there, you can follow the [documentation](/configuration/object_detectors.md#converting-your-own-onnx-model-to-rknn-format) to convert it. Automatic conversion is coming in 0.17.
+| Model Type  | Description                                                                                                                                  |
+| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
+| `mobiledet` | Based on the same architecture as the default model included with Frigate. Runs on Google Coral devices and CPUs.                            |
+| `yolonas`   | A newer architecture that offers slightly higher accuracy and improved detection of small objects. Runs on Intel, NVidia GPUs, and AMD GPUs. |
 
 ## Supported detector types
 
-Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo8l`), and Rockchip\* (`rknn`) detectors.
+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), and ONNX (`onnx`) detectors.
 
-| Hardware                                                                         | Recommended Detector Type | Recommended Model Type |
-| -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
-| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)           | `cpu`                     | `mobiledet`            |
-| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu`                 | `mobiledet`            |
-| [Intel](/configuration/object_detectors.md#openvino-detector)                    | `openvino`                | `yolov9`               |
-| [NVidia GPU](/configuration/object_detectors#onnx)                               | `onnx`                    | `yolov9`               |
-| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector)             | `onnx`                    | `yolov9`               |
-| [Hailo8/Hailo8L/Hailo8R](/configuration/object_detectors#hailo-8)                | `hailo8l`                 | `yolov9`               |
-| [Rockchip NPU](/configuration/object_detectors#rockchip-platform)\*              | `rknn`                    | `yolov9`               |
+:::warning
 
-_\* Requires manual conversion in 0.16. Automatic conversion coming in 0.17._
+Using Frigate+ models with `onnx` is only available with Frigate 0.15 and later.
 
-## Improving your model
+:::
 
-Some users may find that Frigate+ models result in more false positives initially, but by submitting true and false positives, the model will improve. With all the new images now being submitted by subscribers, future base models will improve as more and more examples are incorporated. Note that only images with at least one verified label will be used when training your model. Submitting an image from Frigate as a true or false positive will not verify the image. You still must verify the image in Frigate+ in order for it to be used in training.
+| Hardware                                                                                                                     | Recommended Detector Type | Recommended Model Type |
+| ---------------------------------------------------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
+| [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)                                                       | `cpu`                     | `mobiledet`            |
+| [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector)                                             | `edgetpu`                 | `mobiledet`            |
+| [Intel](/configuration/object_detectors.md#openvino-detector)                                                                | `openvino`                | `yolonas`              |
+| [NVidia GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#onnx)\*                   | `onnx`                    | `yolonas`              |
+| [AMD ROCm GPU](https://deploy-preview-13787--frigate-docs.netlify.app/configuration/object_detectors#amdrocm-gpu-detector)\* | `onnx`                    | `yolonas`              |
 
-- **Submit both true positives and false positives**. This will help the model differentiate between what is and isn't correct. You should aim for a target of 80% true positive submissions and 20% false positives across all of your images. If you are experiencing false positives in a specific area, submitting true positives for any object type near that area in similar lighting conditions will help teach the model what that area looks like when no objects are present.
-- **Lower your thresholds a little in order to generate more false/true positives near the threshold value**. For example, if you have some false positives that are scoring at 68% and some true positives scoring at 72%, you can try lowering your threshold to 65% and submitting both true and false positives within that range. This will help the model learn and widen the gap between true and false positive scores.
-- **Submit diverse images**. For the best results, you should provide at least 100 verified images per camera. Keep in mind that varying conditions should be included. You will want images from cloudy days, sunny days, dawn, dusk, and night. As circumstances change, you may need to submit new examples to address new types of false positives. For example, the change from summer days to snowy winter days or other changes such as a new grill or patio furniture may require additional examples and training.
+_\* Requires Frigate 0.15_
 
 ## Available label types
 
-Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
+Frigate+ models support a more relevant set of objects for security cameras. Currently, the following objects are supported:
 
 - **People**: `person`, `face`
-- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
-- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
-- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `license_plate`
+- **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`
 - **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
 
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
 
-### Candidate labels
-
-Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
-
-Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
-
-The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`
-
-Candidate labels are not available for automatic suggestions.
-
 ### Label attributes
 
 Frigate has special handling for some labels when using Frigate+ models. `face`, `license_plate`, and delivery logos such as `amazon`, `ups`, and `fedex` are considered attribute labels which are not tracked like regular objects and do not generate review items directly. In addition, the `threshold` filter will have no effect on these labels. You should adjust the `min_score` and other filter values as needed.

docs/docs/troubleshooting/edgetpu.md

@@ -46,17 +46,6 @@ Some users have reported that this older device runs an older kernel causing iss
 6. Open the control panel - info scree. The coral TPU will now be recognised as a USB Device - google inc
 7. Start the frigate container. Everything should work now!
 
-### QNAP NAS
-
-QNAP NAS devices, such as the TS-253A, may use connected Coral TPU devices if [QuMagie](https://www.qnap.com/en/software/qumagie) is installed along with its QNAP AI Core extension. If any of the features—`facial recognition`, `object recognition`, or `similar photo recognition`—are enabled, Container Station applications such as `Frigate` or `CodeProject.AI Server` will be unable to initialize the TPU device in use.
-To allow the Coral TPU device to be discovered, the you must either:
-
-1. [Disable the AI recognition features in QuMagie](https://docs.qnap.com/application/qumagie/2.x/en-us/configuring-qnap-ai-core-settings-FB13CE03.html),
-2. Remove the QNAP AI Core extension or
-3. Manually start the QNAP AI Core extension after Frigate has fully started (not recommended).
-
-It is also recommended to restart the NAS once the changes have been made.
-
 ## USB Coral Detection Appears to be Stuck
 
 The USB Coral can become stuck and need to be restarted, this can happen for a number of reasons depending on hardware and software setup. Some common reasons are:
@@ -66,10 +55,10 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n
 
 ## PCIe Coral Not Detected
 
-The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run.
+The most common reason for the PCIe Coral not being detected is that the driver has not been installed. This process varies based on what OS and kernel that is being run. 
 
 - In most cases [the Coral docs](https://coral.ai/docs/m2/get-started/#2-install-the-pcie-driver-and-edge-tpu-runtime) show how to install the driver for the PCIe based Coral.
-- For some newer Linux distros (for example, Ubuntu 22.04+), https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
+- For Ubuntu 22.04+ https://github.com/jnicolson/gasket-builder can be used to build and install the latest version of the driver.
 
 ## Attempting to load TPU as pci & Fatal Python error: Illegal instruction
 

docs/docs/troubleshooting/gpu.md

@@ -1,13 +0,0 @@
----
-id: gpu
-title: Troubleshooting GPU
----
-
-## OpenVINO
-
-### Can't get OPTIMIZATION_CAPABILITIES property as no supported devices found.
-
-Some users have reported issues using some Intel iGPUs with OpenVINO, where the GPU would not be detected. This error can be caused by various problems, so it is important to ensure the configuration is setup correctly. Some solutions users have noted:
-
-- In some cases users have noted that an HDMI dummy plug was necessary to be plugged into the motherboard's HDMI port.
-- When mixing an Intel iGPU with Nvidia GPU, the devices can be mixed up between `/dev/dri/renderD128` and `/dev/dri/renderD129` so it is important to confirm the correct device, or map the entire `/dev/dri` directory into the Frigate container.