.cspell/frigate-dictionary.txt

@@ -191,7 +191,6 @@ ONVIF
 openai
 opencv
 openvino
-overfitting
 OWASP
 paddleocr
 paho

.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
@@ -47,7 +47,7 @@ jobs:
     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 +77,42 @@ 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 +143,7 @@ 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
@@ -136,6 +166,7 @@ jobs:
             *.cache-to=type=registry,ref=${{ steps.setup.outputs.cache-name }}-tensorrt,mode=max
       - name: AMD/ROCm general build
         env:
+          AMDGPU: gfx
           HSA_OVERRIDE: 0
         uses: docker/bake-action@v6
         with:
@@ -154,7 +185,7 @@ jobs:
       - arm64_build
     steps:
       - name: Check out code
-        uses: actions/checkout@v5
+        uses: actions/checkout@v4
         with:
           persist-credentials: false
       - name: Set up QEMU and Buildx
@@ -172,31 +203,6 @@ jobs:
           set: |
             rk.tags=${{ 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 +217,7 @@ 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 }}

.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

@@ -1,31 +1,28 @@
 <p align="center">
-  <img align="center" alt="logo" src="docs/static/img/branding/frigate.png">
+  <img align="center" alt="logo" src="docs/static/img/frigate.png">
 </p>
 
-# Frigate NVR™ - 一个具有实时目标检测的本地 NVR
+# Frigate - 一个具有实时目标检测的本地NVR
 
-[English](https://github.com/blakeblackshear/frigate) | \[简体中文\]
-
-[![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
 
@@ -35,55 +32,39 @@
 
 如果您想通过捐赠支持开发，请使用 [Github Sponsors](https://github.com/sponsors/blakeblackshear)。
 
-## 协议
-
-本项目采用 **MIT 许可证**授权。
-**代码部分**：本代码库中的源代码、配置文件和文档均遵循 [MIT 许可证](LICENSE)。您可以自由使用、修改和分发这些代码，但必须保留原始版权声明。
-
-**商标部分**：“Frigate”名称、“Frigate NVR”品牌以及 Frigate 的 Logo 为 **Frigate 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
-
 <div>
 <img width="800" alt="简单的审查工作流程" src="https://github.com/blakeblackshear/frigate/assets/569905/6fed96e8-3b18-40e5-9ddc-31e6f3c9f2ff">
 </div>
 
 ### 多摄像头可按时间轴查看
-
 <div>
 <img width="800" alt="多摄像头可按时间轴查看" src="https://github.com/blakeblackshear/frigate/assets/569905/d6788a15-0eeb-4427-a8d4-80b93cae3d74">
 </div>
 
 ### 内置遮罩和区域编辑器
-
 <div>
 <img width="800" alt="内置遮罩和区域编辑器" src="https://github.com/blakeblackshear/frigate/assets/569905/d7885fc3-bfe6-452f-b7d0-d957cb3e31f5">
 </div>
 
-## 翻译
 
+## 翻译
 我们使用 [Weblate](https://hosted.weblate.org/projects/frigate-nvr/) 平台提供翻译支持，欢迎参与进来一起完善。
 
-## 非官方中文讨论社区
 
-欢迎加入中文讨论 QQ 群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
+## 非官方中文讨论社区
+欢迎加入中文讨论QQ群：[1043861059](https://qm.qq.com/q/7vQKsTmSz)
 
 Bilibili：https://space.bilibili.com/3546894915602564
 
-## 中文社区赞助商
 
+## 中文社区赞助商
 [![EdgeOne](https://edgeone.ai/media/34fe3a45-492d-4ea4-ae5d-ea1087ca7b4b.png)](https://edgeone.ai/zh?from=github)
 本项目 CDN 加速及安全防护由 Tencent EdgeOne 赞助
-
----
-
-**Copyright © 2025 Frigate LLC.**

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/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

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_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/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.*
@@ -31,7 +27,7 @@ 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.*
@@ -40,15 +36,16 @@ titlecase == 2.4.*
 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 +53,7 @@ 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 +71,3 @@ prometheus-client == 0.21.*
 # TFLite
 tflite_runtime @ https://github.com/frigate-nvr/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_x86_64.whl; platform_machine == 'x86_64'
 tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.17.1/tflite_runtime-2.17.1-cp311-cp311-linux_aarch64.whl; platform_machine == 'aarch64'
-# audio transcription
-sherpa-onnx==1.12.*
-faster-whisper==1.1.*
-librosa==0.11.*
-soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*
-# 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/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/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_tls_settings.py

@@ -26,10 +26,6 @@ try:
 except FileNotFoundError:
     config: dict[str, Any] = {}
 
-tls_config: dict[str, any] = config.get("tls", {"enabled": True})
-networking_config = config.get("networking", {})
-ipv6_config = networking_config.get("ipv6", {"enabled": False})
+tls_config: dict[str, Any] = config.get("tls", {"enabled": True})
 
-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
@@ -32,7 +30,5 @@ ADD https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/librknnrt
 
 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/
 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-$ROCM/lib/ && \
+    cp -dpr libMIOpen*.so* libamd*.so* libhip*.so* libhsa*.so* libmigraphx*.so* librocm*.so* librocblas*.so* libroctracer*.so* librocsolver*.so* librocfft*.so* librocprofiler*.so* libroctx*.so* /opt/rocm-dist/opt/rocm-$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 +44,26 @@ 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
+ENV TF_ROCM_USE_IMMEDIATE_MODE=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

@@ -12,16 +12,13 @@ ARG PIP_BREAK_SYSTEM_PACKAGES
 # 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
+RUN  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 uninstall -y onnxruntime-openvino tensorflow-cpu \
     && pip3 install -U /deps/trt-wheels/*.whl
 
 COPY --from=rootfs / /

docker/tensorrt/Dockerfile.arm64

@@ -112,7 +112,7 @@ 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
@@ -145,8 +145,7 @@ 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/

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)

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

@@ -59,7 +59,6 @@ The default session length for user authentication in Frigate is 24 hours. This
 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.
 
@@ -81,7 +80,7 @@ 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
 
@@ -113,7 +112,7 @@ 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.
+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. 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:
@@ -121,10 +120,10 @@ 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
+    role: x-forwarded-role
 ```
 
-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.
+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.
 
 A default role can be provided. Any value in the mapped `role` header will override the default.
 
@@ -134,34 +133,6 @@ 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).
-
-```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
-```
-
-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.
-
 #### Port Considerations
 
 **Authenticated Port (8971)**
@@ -170,7 +141,6 @@ 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 +186,6 @@ 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.
 
@@ -257,7 +249,6 @@ TP-Link VIGI cameras need some adjustments to the main stream settings on the ca
 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.
@@ -268,7 +259,7 @@ To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's
 go2rtc:
   streams:
     usb_camera:
-      - "ffmpeg:device?video=0&video_size=1024x576#video=h264"
+      - "ffmpeg:device?video=0&video_size=1024x576#video=h264" 
 
 cameras:
   usb_camera:
@@ -285,3 +276,5 @@ cameras:
       width: 1024
       height: 576
 ```
+
+

docs/docs/configuration/cameras.md

@@ -89,35 +89,34 @@ 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.                                                                                            |
+| 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, among others) have been reported to not support autotracking                                                  |
+| Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
+| Dahua DH-SD49825GB-HNR       |      ✅      |      ✅      |                                                                                                                                                 |
+| Dahua DH-P5AE-PV             |      ❌      |      ❌      |                                                                                                                                                 |
+| 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 +137,3 @@ 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

@@ -24,7 +24,7 @@ Frigate needs to first detect a `person` before it can detect and recognize a fa
 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.
 
@@ -34,7 +34,7 @@ All of these features run locally on your system.
 
 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 required. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
 
 ## Configuration
 
@@ -70,12 +70,9 @@ Fine-tune face recognition with these optional parameters at the global level of
 - `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
 
@@ -114,9 +111,9 @@ When choosing images to include in the face training set it is recommended to al
 
 :::
 
-### Understanding the Recent Recognitions Tab
+### Understanding the Train 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.
+The Train tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
 
 Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
 
@@ -140,7 +137,7 @@ Once front-facing images are performing well, start choosing slightly off-angle
 
 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.
+1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Train tab in the Frigate UI's Face Library.
 
    If you are using a Frigate+ or `face` detecting model:
 
@@ -161,8 +158,6 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
 
 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.
@@ -188,7 +183,7 @@ Avoid training on images that already score highly, as this can lead to over-fit
 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.
 

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
 

docs/docs/configuration/genai.md

@@ -9,18 +9,19 @@ 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:
+  front_camera: 
     genai:
       enabled: True # <- enable GenAI for your front camera
       use_snapshot: True
@@ -29,17 +30,14 @@ cameras:
       required_zones:
         - steps
   indoor_camera:
-    objects:
-      genai:
-        enabled: False # <- disable GenAI for your indoor camera
+    genai: 
+      enabled: False # <- disable GenAI for your indoor camera
 ```
 
 By default, descriptions will be generated for all tracked objects and all zones. But you can also optionally specify `objects` and `required_zones` to only generate descriptions for certain tracked objects or zones.
 
 Optionally, you can generate the description using a snapshot (if enabled) by setting `use_snapshot` to `True`. By default, this is set to `False`, which sends the uncompressed images from the `detect` stream collected over the object's lifetime to the model. Once the object lifecycle ends, only a single compressed and cropped thumbnail is saved with the tracked object. Using a snapshot might be useful when you want to _regenerate_ a tracked object's description as it will provide the AI with a higher-quality image (typically downscaled by the AI itself) than the cropped/compressed thumbnail. Using a snapshot otherwise has a trade-off in that only a single image is sent to your provider, which will limit the model's ability to determine object movement or direction.
 
-Generative AI can also be toggled dynamically for a camera via MQTT with the topic `frigate/<camera_name>/object_descriptions/set`. See the [MQTT documentation](/integrations/mqtt/#frigatecamera_nameobjectdescriptionsset).
-
 ## Ollama
 
 :::warning
@@ -68,9 +66,10 @@ 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 +78,7 @@ 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 +93,19 @@ 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 +115,7 @@ 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 +133,19 @@ 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}"
 ```
 
@@ -191,35 +186,32 @@ 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. 
 
 ```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**
 
@@ -18,16 +18,11 @@ 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

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
 
@@ -175,26 +176,16 @@ 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 +219,7 @@ 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 +237,7 @@ docker run -d \
   --name frigate \
   ...
   --gpus=all \
-  ghcr.io/blakeblackshear/frigate:stable-tensorrt
+  ghcr.io/blakeblackshear/frigate:stable
 ```
 
 ### Setup Decoder
@@ -385,8 +376,13 @@ 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 +390,3 @@ 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` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
-LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
+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` or `motorcycle` tracked object.
+- Published via the `frigate/tracked_object_update` MQTT topic with `name` (if known) and `plate`.
 
 ## Model Requirements
 
@@ -30,7 +30,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
 
 ## Minimum System Requirements
 
-License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM 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
 
@@ -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
 
@@ -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:
@@ -306,7 +274,7 @@ 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.
 

docs/docs/configuration/live.md

@@ -174,9 +174,7 @@ For devices that support two way talk, Frigate can be configured to use the feat
 - 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.
 
-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
 
@@ -214,42 +212,6 @@ For restreamed cameras, go2rtc remains active but does not use system resources
 
 Note that disabling a camera through the config file (`enabled: False`) removes all related UI elements, including historical footage access. To retain access while disabling the camera, keep it enabled in the config and use the UI or MQTT to disable it temporarily.
 
-### Live player error messages
-
-When your browser runs into problems playing back your camera streams, it will log short error messages to the browser console. They indicate playback, codec, or network issues on the client/browser side, not something server side with Frigate itself. Below are the common messages you may see and simple actions you can take to try to resolve them.
-
-- **startup**
-
-  - What it means: The player failed to initialize or connect to the live stream (network or startup error).
-  - What to try: Reload the Live view or click _Reset_. Verify `go2rtc` is running and the camera stream is reachable. Try switching to a different stream from the Live UI dropdown (if available) or use a different browser.
-
-  - Possible console messages from the player code:
-
-    - `Error opening MediaSource.`
-    - `Browser reported a network error.`
-    - `Max error count ${errorCount} exceeded.` (the numeric value will vary)
-
-- **mse-decode**
-
-  - What it means: The browser reported a decoding error while trying to play the stream, which usually is a result of a codec incompatibility or corrupted frames.
-  - What to try: Check the browser console for the supported and negotiated codecs. Ensure your camera/restream is using H.264 video and AAC audio (these are the most compatible). If your camera uses a non-standard audio codec, configure `go2rtc` to transcode the stream to AAC. Try another browser (some browsers have stricter MSE/codec support) and, for iPhone, ensure you're on iOS 17.1 or newer.
-
-  - Possible console messages from the player code:
-
-    - `Safari cannot open MediaSource.`
-    - `Safari reported InvalidStateError.`
-    - `Safari reported decoding errors.`
-
-- **stalled**
-
-  - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval — shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
-
-  - Possible console messages from the player code:
-
-    - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
-    - `Media playback has stalled after <n> seconds due to insufficient buffering or a network interruption.` (the seconds value will vary)
-
 ## Live view FAQ
 
 1. **Why don't I have audio in my Live view?**
@@ -266,27 +228,7 @@ 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 +251,3 @@ 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/object_detectors.md

@@ -3,8 +3,6 @@ id: object_detectors
 title: Object Detectors
 ---
 
-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 # Supported Hardware
 
 :::info
@@ -13,20 +11,14 @@ Frigate supports multiple different detectors that work on different types of ha
 
 **Most Hardware**
 
-- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
+- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
-- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
 
-**Apple Silicon**
-
-- [Apple Silicon](#apple-silicon-detector): Apple Silicon can run on M1 and newer Apple Silicon devices.
-
 **Intel**
 
 - [OpenVino](#openvino-detector): OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
@@ -36,19 +28,14 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
 
-**Nvidia Jetson** <CommunityBadge />
-
+**Nvidia Jetson**
 - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
 
-**Rockchip** <CommunityBadge />
+**Rockchip**
 
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
 
-**Synaptics** <CommunityBadge />
-
-- [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
-
 **For Testing**
 
 - [CPU Detector (not recommended for actual use](#cpu-detector-not-recommended): Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.
@@ -65,14 +52,16 @@ This does not affect using hardware for accelerating other tasks such as [semant
 
 # Officially Supported Detectors
 
-Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `memryx`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
+Frigate provides the following builtin detector types: `cpu`, `edgetpu`, `hailo8l`, `onnx`, `openvino`, `rknn`, and `tensorrt`. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
 
 ## Edge TPU Detector
 
-The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
+The Edge TPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
 
 The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
 
+A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
+
 :::tip
 
 See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edge TPU is not detected.
@@ -144,46 +133,6 @@ detectors:
     device: pci
 ```
 
-### EdgeTPU Supported Models
-
-| Model                                 | Notes                                       |
-| ------------------------------------- | ------------------------------------------- |
-| [MobileNet v2](#ssdlite-mobilenet-v2) | Default model                               |
-| [YOLOv9](#yolo-v9)                    | More accurate but slower than default model |
-
-#### SSDLite MobileNet v2
-
-A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
-
-A Tensorflow Lite is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an INT8 precision model.
-
-#### YOLO v9
-
-[YOLOv9](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite) models that are compiled for Tensorflow Lite and properly quantized are supported, but not included by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`. Note that the model may require a custom label file (eg. [use this 17 label file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) for the model linked above.)
-
-<details>
-  <summary>YOLOv9 Setup & Config</summary>
-
-After placing the downloaded files for the tflite model and labels in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  coral:
-    type: edgetpu
-    device: usb
-
-model:
-  model_type: yolo-generic
-  width: 320 # <--- should match the imgsize of the model, typically 320
-  height: 320 # <--- should match the imgsize of the model, typically 320
-  path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
-  labelmap_path: /labelmap/labels-coco-17.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 17 objects.
-
-</details>
-
 ---
 
 ## Hailo-8
@@ -293,55 +242,41 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
 
 ## OpenVINO Detector
 
-The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
+The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
 
-The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
+The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU` and `GPU`. Currently, there is a known issue with using `AUTO`. For backwards compatibility, Frigate will attempt to use `GPU` if `AUTO` is set in your configuration.
 
-OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` or `NPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2025/about-openvino/release-notes-openvino/system-requirements.html)
+OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the `GPU` device with OpenVINO. For detailed system requirements, see [OpenVINO System Requirements](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html)
 
 :::tip
 
-**NPU + GPU Systems:** If you have both NPU and GPU available (Intel Core Ultra processors), use NPU for object detection and GPU for enrichments (semantic search, face recognition, etc.) for best performance and compatibility.
-
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
 detectors:
   ov_0:
     type: openvino
-    device: GPU # or NPU
+    device: GPU
   ov_1:
     type: openvino
-    device: GPU # or NPU
+    device: GPU
 ```
 
 :::
 
-### OpenVINO Supported Models
-
-| Model                                 | GPU | NPU | Notes                                                        |
-| ------------------------------------- | --- | --- | ------------------------------------------------------------ |
-| [YOLOv9](#yolo-v3-v4-v7-v9)           | ✅  | ✅  | Recommended for GPU & NPU                                    |
-| [RF-DETR](#rf-detr)                   | ✅  | ✅  | Requires XE iGPU or Arc                                      |
-| [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
-| [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
-| [YOLOX](#yolox)                       | ✅  | ?   |                                                              |
-| [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
+### Supported Models
 
 #### SSDLite MobileNet v2
 
 An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
 
-<details>
-  <summary>MobileNet v2 Config</summary>
-
 Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:
 
 ```yaml
 detectors:
   ov:
     type: openvino
-    device: GPU # Or NPU
+    device: GPU
 
 model:
   width: 300
@@ -352,8 +287,6 @@ model:
   labelmap_path: /openvino-model/coco_91cl_bkgr.txt
 ```
 
-</details>
-
 #### YOLOX
 
 This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models.
@@ -362,9 +295,6 @@ This detector also supports YOLOX. Frigate does not come with any YOLOX models p
 
 [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
-<details>
-  <summary>YOLO-NAS Setup & Config</summary>
-
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
@@ -385,8 +315,6 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
-</details>
-
 #### YOLO (v3, v4, v7, v9)
 
 YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
@@ -397,22 +325,13 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
 
 :::
 
-<details>
-  <summary>YOLOv Setup & Config</summary>
-
-:::warning
-
-If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
-
-:::
-
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
 detectors:
   ov:
     type: openvino
-    device: GPU # or NPU
+    device: GPU
 
 model:
   model_type: yolo-generic
@@ -426,8 +345,6 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
-</details>
-
 #### RF-DETR
 
 [RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more informatoin on downloading the RF-DETR model for use in Frigate.
@@ -438,9 +355,6 @@ Due to the size and complexity of the RF-DETR model, it is only recommended to b
 
 :::
 
-<details>
-  <summary>RF-DETR Setup & Config</summary>
-
 After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
 
 ```yaml
@@ -451,15 +365,13 @@ detectors:
 
 model:
   model_type: rfdetr
-  width: 320
-  height: 320
+  width: 560
+  height: 560
   input_tensor: nchw
   input_dtype: float
   path: /config/model_cache/rfdetr.onnx
 ```
 
-</details>
-
 #### D-FINE
 
 [D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
@@ -470,9 +382,6 @@ Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to
 
 :::
 
-<details>
-  <summary>D-FINE Setup & Config</summary>
-
 After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
 
 ```yaml
@@ -487,63 +396,7 @@ model:
   height: 640
   input_tensor: nchw
   input_dtype: float
-  path: /config/model_cache/dfine-s.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
-</details>
-
-## Apple Silicon detector
-
-The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
-
-### Setup
-
-1. Setup the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) and run the client
-2. Configure the detector in Frigate and startup Frigate
-
-### Configuration
-
-Using the detector config below will connect to the client:
-
-```yaml
-detectors:
-  apple-silicon:
-    type: zmq
-    endpoint: tcp://host.docker.internal:5555
-```
-
-### Apple Silicon Supported Models
-
-There is no default model provided, the following formats are supported:
-
-#### YOLO (v3, v4, v7, v9)
-
-YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
-
-:::tip
-
-The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well. See [the models section](#downloading-yolo-models) for more information on downloading YOLO models for use in Frigate.
-
-:::
-
-When Frigate is started with the following config it will connect to the detector client and transfer the model automatically:
-
-```yaml
-detectors:
-  apple-silicon:
-    type: zmq
-    endpoint: tcp://host.docker.internal:5555
-
-model:
-  model_type: yolo-generic
-  width: 320 # <--- should match the imgsize set during model export
-  height: 320 # <--- should match the imgsize set during model export
-  input_tensor: nchw
-  input_dtype: float
-  path: /config/model_cache/yolo.onnx
+  path: /config/model_cache/dfine_s_obj2coco.onnx
   labelmap_path: /labelmap/coco-80.txt
 ```
 
@@ -587,13 +440,14 @@ Also AMD/ROCm does not "officially" support integrated GPUs. It still does work
 
 For the rocm frigate build there is some automatic detection:
 
+- gfx90c -> 9.0.0
 - gfx1031 -> 10.3.0
 - gfx1103 -> 11.0.0
 
-If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `10.0.0`, then you should configure it from command line as:
+If you have something else you might need to override the `HSA_OVERRIDE_GFX_VERSION` at Docker launch. Suppose the version you want is `9.0.0`, then you should configure it from command line as:
 
 ```bash
-$ docker run -e HSA_OVERRIDE_GFX_VERSION=10.0.0 \
+$ docker run -e HSA_OVERRIDE_GFX_VERSION=9.0.0 \
     ...
 ```
 
@@ -604,7 +458,7 @@ services:
   frigate:
 
 environment:
-  HSA_OVERRIDE_GFX_VERSION: "10.0.0"
+  HSA_OVERRIDE_GFX_VERSION: "9.0.0"
 ```
 
 Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
@@ -629,18 +483,7 @@ We unset the `HSA_OVERRIDE_GFX_VERSION` to prevent an existing override from mes
 $ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'
 ```
 
-### ROCm Supported Models
-
-:::tip
-
-The AMD GPU kernel is known problematic especially when converting models to mxr format. The recommended approach is:
-
-1. Disable object detection in the config.
-2. Startup Frigate with the onnx detector configured, the main object detection model will be converted to mxr format and cached in the config directory.
-3. Once this is finished as indicated by the logs, enable object detection in the UI and confirm that it is working correctly.
-4. Re-enable object detection in the config.
-
-:::
+### Supported Models
 
 See [ONNX supported models](#supported-models) for supported models, there are some caveats:
 
@@ -683,15 +526,7 @@ detectors:
 
 :::
 
-### ONNX Supported Models
-
-| Model                         | Nvidia GPU | AMD GPU | Notes                                               |
-| ----------------------------- | ---------- | ------- | --------------------------------------------------- |
-| [YOLOv9](#yolo-v3-v4-v7-v9-2) | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
-| [RF-DETR](#rf-detr)           | ✅         | ❌      | Supports CUDA Graphs for optimal Nvidia performance |
-| [YOLO-NAS](#yolo-nas-1)       | ⚠️         | ⚠️      | Not supported by CUDA Graphs                        |
-| [YOLOX](#yolox-1)             | ✅         | ✅      | Supports CUDA Graphs for optimal Nvidia performance |
-| [D-FINE](#d-fine)             | ⚠️         | ❌      | Not supported by CUDA Graphs                        |
+### Supported Models
 
 There is no default model provided, the following formats are supported:
 
@@ -699,15 +534,6 @@ There is no default model provided, the following formats are supported:
 
 [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) models are supported, but not included by default. See [the models section](#downloading-yolo-nas-model) for more information on downloading the YOLO-NAS model for use in Frigate.
 
-<details>
-  <summary>YOLO-NAS Setup & Config</summary>
-
-:::warning
-
-If you are using a Frigate+ YOLO-NAS model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
-
-:::
-
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
@@ -725,8 +551,6 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
-</details>
-
 #### YOLO (v3, v4, v7, v9)
 
 YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) models are supported, but not included by default.
@@ -737,15 +561,6 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv
 
 :::
 
-<details>
-  <summary>YOLOv Setup & Config</summary>
-
-:::warning
-
-If you are using a Frigate+ model, you should not define any of the below `model` parameters in your config except for `path`. See [the Frigate+ model docs](/plus/first_model#step-3-set-your-model-id-in-the-config) for more information on setting up your model.
-
-:::
-
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
@@ -763,17 +578,12 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
-</details>
-
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 #### YOLOx
 
 [YOLOx](https://github.com/Megvii-BaseDetection/YOLOX) models are supported, but not included by default. See [the models section](#downloading-yolo-models) for more information on downloading the YOLOx model for use in Frigate.
 
-<details>
-  <summary>YOLOx Setup & Config</summary>
-
 After placing the downloaded onnx model in your config folder, you can use the following configuration:
 
 ```yaml
@@ -793,15 +603,10 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
-</details>
-
 #### RF-DETR
 
 [RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
 
-<details>
-  <summary>RF-DETR Setup & Config</summary>
-
 After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
 
 ```yaml
@@ -811,22 +616,17 @@ detectors:
 
 model:
   model_type: rfdetr
-  width: 320
-  height: 320
+  width: 560
+  height: 560
   input_tensor: nchw
   input_dtype: float
   path: /config/model_cache/rfdetr.onnx
 ```
 
-</details>
-
 #### D-FINE
 
 [D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
 
-<details>
-  <summary>D-FINE Setup & Config</summary>
-
 After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
 
 ```yaml
@@ -844,8 +644,6 @@ model:
   labelmap_path: /labelmap/coco-80.txt
 ```
 
-</details>
-
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
 ## CPU Detector (not recommended)
@@ -901,196 +699,6 @@ To verify that the integration is working correctly, start Frigate and observe t
 
 # Community Supported Detectors
 
-## MemryX MX3
-
-This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
-
-See the [installation docs](../frigate/installation.md#memryx-mx3) for information on configuring the MemryX hardware.
-
-To configure a MemryX detector, simply set the `type` attribute to `memryx` and follow the configuration guide below.
-
-### Configuration
-
-To configure the MemryX detector, use the following example configuration:
-
-#### Single PCIe MemryX MX3
-
-```yaml
-detectors:
-  memx0:
-    type: memryx
-    device: PCIe:0
-```
-
-#### Multiple PCIe MemryX MX3 Modules
-
-```yaml
-detectors:
-  memx0:
-    type: memryx
-    device: PCIe:0
-
-  memx1:
-    type: memryx
-    device: PCIe:1
-
-  memx2:
-    type: memryx
-    device: PCIe:2
-```
-
-### Supported Models
-
-MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
-
-#### YOLO-NAS
-
-The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded from the [Models Section](#downloading-yolo-nas-model) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
-
-**Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
-
-The input size for **YOLO-NAS** can be set to either **320x320** (default) or **640x640**.
-
-- The default size of **320x320** is optimized for lower CPU usage and faster inference times.
-
-##### Configuration
-
-Below is the recommended configuration for using the **YOLO-NAS** (small) model with the MemryX detector:
-
-```yaml
-detectors:
-  memx0:
-    type: memryx
-    device: PCIe:0
-
-model:
-  model_type: yolonas
-  width: 320 # (Can be set to 640 for higher resolution)
-  height: 320 # (Can be set to 640 for higher resolution)
-  input_tensor: nchw
-  input_dtype: float
-  labelmap_path: /labelmap/coco-80.txt
-  # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-  # path: /config/yolonas.zip
-  # The .zip file must contain:
-  # ├── yolonas.dfp          (a file ending with .dfp)
-  # └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
-```
-
-#### YOLOv9
-
-The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) like in the [Models Section](#yolov9-1) and compiled to DFP with [mx_nc](https://developer.memryx.com/tools/neural_compiler.html#usage).
-
-##### Configuration
-
-Below is the recommended configuration for using the **YOLOv9** (small) model with the MemryX detector:
-
-```yaml
-detectors:
-  memx0:
-    type: memryx
-    device: PCIe:0
-
-model:
-  model_type: yolo-generic
-  width: 320 # (Can be set to 640 for higher resolution)
-  height: 320 # (Can be set to 640 for higher resolution)
-  input_tensor: nchw
-  input_dtype: float
-  labelmap_path: /labelmap/coco-80.txt
-  # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-  # path: /config/yolov9.zip
-  # The .zip file must contain:
-  # ├── yolov9.dfp          (a file ending with .dfp)
-```
-
-#### YOLOX
-
-The model is sourced from the [OpenCV Model Zoo](https://github.com/opencv/opencv_zoo) and precompiled to DFP.
-
-##### Configuration
-
-Below is the recommended configuration for using the **YOLOX** (small) model with the MemryX detector:
-
-```yaml
-detectors:
-  memx0:
-    type: memryx
-    device: PCIe:0
-
-model:
-  model_type: yolox
-  width: 640
-  height: 640
-  input_tensor: nchw
-  input_dtype: float_denorm
-  labelmap_path: /labelmap/coco-80.txt
-  # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-  # path: /config/yolox.zip
-  # The .zip file must contain:
-  # ├── yolox.dfp          (a file ending with .dfp)
-```
-
-#### SSDLite MobileNet v2
-
-The model is sourced from the [OpenMMLab Model Zoo](https://mmdeploy-oss.openmmlab.com/model/mmdet-det/ssdlite-e8679f.onnx) and has been converted to DFP.
-
-##### Configuration
-
-Below is the recommended configuration for using the **SSDLite MobileNet v2** model with the MemryX detector:
-
-```yaml
-detectors:
-  memx0:
-    type: memryx
-    device: PCIe:0
-
-model:
-  model_type: ssd
-  width: 320
-  height: 320
-  input_tensor: nchw
-  input_dtype: float
-  labelmap_path: /labelmap/coco-80.txt
-  # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
-  # path: /config/ssdlite_mobilenet.zip
-  # The .zip file must contain:
-  # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
-  # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
-```
-
-#### Using a Custom Model
-
-To use your own model:
-
-1.  Package your compiled model into a `.zip` file.
-
-2.  The `.zip` must contain the compiled `.dfp` file.
-
-3.  Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
-
-4.  Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
-
-5.  Update the `labelmap_path` to match your custom model's labels.
-
-For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/tutorials/tutorials.html).
-
-```yaml
-# The detector automatically selects the default model if nothing is provided in the config.
-#
-# Optionally, you can specify a local model path as a .zip file to override the default.
-# If a local path is provided and the file exists, it will be used instead of downloading.
-#
-# Example:
-# path: /config/yolonas.zip
-#
-# The .zip file must contain:
-# ├── yolonas.dfp          (a file ending with .dfp)
-# └── yolonas_post.onnx    (optional; only if the model includes a cropped post-processing network)
-```
-
----
-
 ## NVidia TensorRT Detector
 
 Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
@@ -1169,43 +777,8 @@ model:
   labelmap_path: /labelmap/coco-80.txt
   input_tensor: nchw
   input_pixel_format: rgb
-  width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
-  height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
-```
-
-## Synaptics
-
-Hardware accelerated object detection is supported on the following SoCs:
-
-- SL1680
-
-This implementation uses the [Synaptics model conversion](https://synaptics-synap.github.io/doc/v/latest/docs/manual/introduction.html#offline-model-conversion), version v3.1.0.
-
-This implementation is based on sdk `v1.5.0`.
-
-See the [installation docs](../frigate/installation.md#synaptics) for information on configuring the SL-series NPU hardware.
-
-### Configuration
-
-When configuring the Synap detector, you have to specify the model: a local **path**.
-
-#### SSD Mobilenet
-
-A synap model is provided in the container at /mobilenet.synap and is used by this detector type by default. The model comes from [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
-
-Use the model configuration shown below when using the synaptics detector with the default synap model:
-
-```yaml
-detectors: # required
-  synap_npu: # required
-    type: synaptics # required
-
-model: # required
-  path: /synaptics/mobilenet.synap # required
-  width: 224 # required
-  height: 224 # required
-  tensor_format: nhwc # default value (optional. If you change the model, it is required)
-  labelmap_path: /labelmap/coco-80.txt # required
+  width: 320
+  height: 320
 ```
 
 ## Rockchip platform
@@ -1251,7 +824,7 @@ $ cat /sys/kernel/debug/rknpu/load
 
 :::
 
-### RockChip Supported Models
+### Supported Models
 
 This `config.yml` shows all relevant options to configure the detector and explains them. All values shown are the default values (except for two). Lines that are required at least to use the detector are labeled as required, all other lines are optional.
 
@@ -1321,6 +894,7 @@ model: # required
   width: 320
   height: 320
   input_tensor: nhwc
+  input_dtype: float
   labelmap_path: /labelmap/coco-80.txt
 ```
 
@@ -1377,105 +951,6 @@ Explanation of the paramters:
   - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
 
-## DeGirum
-
-DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
-
-### Configuration
-
-#### AI Server Inference
-
-Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
-
-```yaml
-degirum_detector:
-  container_name: degirum
-  image: degirum/aiserver:latest
-  privileged: true
-  ports:
-    - "8778:8778"
-```
-
-All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
-
-Once completed, changing the `config.yml` file is simple.
-
-```yaml
-degirum_detector:
-  type: degirum
-  location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
-  zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
-  token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
-```
-
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
-
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
-  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
-- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
-- A path to some model.json.
-
-```yaml
-model:
-  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### Local Inference
-
-It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
-
-1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
-2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
-3. Create a DeGirum detector in your `config.yml` file.
-
-```yaml
-degirum_detector:
-  type: degirum
-  location: "@local" # For accessing AI Hub devices and models
-  zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
-  token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server
-```
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### AI Hub Cloud Inference
-
-If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
-
-1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
-2. Get an access token.
-3. Create a DeGirum detector in your `config.yml` file.
-
-```yaml
-degirum_detector:
-  type: degirum
-  location: "@cloud" # For accessing AI Hub devices and models
-  zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
-  token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).
-```
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
 # Models
 
 Some model types are not included in Frigate by default.
@@ -1486,47 +961,45 @@ Here are some tips for getting different model types
 
 ### Downloading D-FINE Model
 
-D-FINE can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=s` in the first line to `s`, `m`, or `l` size.
+To export as ONNX:
+
+1. Clone: https://github.com/Peterande/D-FINE and install all dependencies.
+2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE).
+3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)`
+4. Run the export, making sure you select the right config, for your checkpoint.
+
+Example:
 
-```sh
-docker build . --build-arg MODEL_SIZE=s --output . -f- <<'EOF'
-FROM python:3.11 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /dfine
-RUN git clone https://github.com/Peterande/D-FINE.git .
-RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
-# Create output directory and download checkpoint
-RUN mkdir -p output
-ARG MODEL_SIZE
-RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
-# Modify line 58 of export_onnx.py to change batch size to 1
-RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
-RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
-FROM scratch
-ARG MODEL_SIZE
-COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
-EOF
 ```
+python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
+```
+
+:::tip
+
+Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually.
+
+Make sure you change the batch size to 1 before exporting.
+
+:::
 
 ### Download RF-DETR Model
 
-RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
+To export as ONNX:
 
-```sh
-docker build . --build-arg MODEL_SIZE=Nano --output . -f- <<'EOF'
-FROM python:3.11 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /rfdetr
-RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnxscript
-ARG MODEL_SIZE
-RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
-FROM scratch
-ARG MODEL_SIZE
-COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
-EOF
+1. `pip3 install rfdetr`
+2. `python3`
+3. `from rfdetr import RFDETRBase`
+4. `x = RFDETRBase()`
+5. `x.export()`
+
+#### Additional Configuration
+
+The input tensor resolution can be customized:
+
+```python
+from rfdetr import RFDETRBase
+x = RFDETRBase(resolution=560)  # resolution must be a multiple of 56
+x.export()
 ```
 
 ### Downloading YOLO-NAS Model
@@ -1560,25 +1033,22 @@ python3 yolo_to_onnx.py -m yolov7-320
 
 #### YOLOv9
 
-YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
+YOLOv9 models can be exported using the below code
 
 ```sh
-docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
-FROM python:3.11 AS build
-RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
-COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
-WORKDIR /yolov9
-ADD https://github.com/WongKinYiu/yolov9.git .
-RUN uv pip install --system -r requirements.txt
-RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
-ARG MODEL_SIZE
-ARG IMG_SIZE
-ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
-RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
-RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
-FROM scratch
-ARG MODEL_SIZE
-ARG IMG_SIZE
-COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
-EOF
+git clone https://github.com/WongKinYiu/yolov9
+cd yolov9
+
+# setup the virtual environment so installation doesn't affect main system
+# NOTE: Virtual environment must be using Python 3.11 or older.
+python3 -m venv ./
+bin/pip install -r requirements.txt
+bin/pip install onnx onnxruntime onnx-simplifier>=0.4.1
+
+# download the weights
+wget -O yolov9-t.pt "https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt" # download the weights
+
+# prepare and run export script
+sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" ./models/experimental.py
+bin/python3 export.py --weights ./yolov9-t.pt --imgsz 320 --simplify --include onnx
 ```

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

@@ -73,12 +73,6 @@ 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
@@ -88,13 +82,7 @@ proxy:
   #       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
+    role: x-forwarded-role
   # Optional: Url for logging out a user. This sets the location of the logout url in
   # the UI.
   logout_url: /api/logout
@@ -240,13 +228,11 @@ 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 +256,6 @@ 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 +275,6 @@ 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 +339,6 @@ 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 +351,6 @@ 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 +365,12 @@ 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 +438,20 @@ 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 +476,7 @@ 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 +496,7 @@ 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 +513,7 @@ 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
@@ -618,9 +546,6 @@ 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
@@ -639,14 +564,11 @@ face_recognition:
   # 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 +576,6 @@ 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 +598,30 @@ 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 +720,6 @@ 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 +827,33 @@ 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 +903,7 @@ 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/semantic_search.md

@@ -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`:
 
@@ -78,21 +78,17 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
 
 ### 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/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.
 
 :::
 

docs/docs/frigate/hardware.md

@@ -3,8 +3,6 @@ id: hardware
 title: Recommended hardware
 ---
 
-import CommunityBadge from '@site/src/components/CommunityBadge';
-
 ## Cameras
 
 Cameras that output H.264 video and AAC audio will offer the most compatibility with all features of Frigate and Home Assistant. It is also helpful if your camera supports multiple substreams to allow different resolutions to be used for detection, streaming, and recordings without re-encoding.
@@ -58,52 +56,33 @@ 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 +99,6 @@ 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
@@ -132,50 +110,37 @@ 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: ~ 25 ms              |                        | Can only run one detector instance |
+| 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
+### 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.
+ 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.
 
@@ -190,74 +155,33 @@ There are improved capabilities in newer GPU architectures that TensorRT can ben
 [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus)
 
 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: ~ 25 ms 640: ~ 50 ms |
+| AMD 8700G |                       | 320: ~ 20 ms 640: ~ 40 ms |
 
 ## Community Supported Detectors
 
-### MemryX MX3
-
-Frigate supports the MemryX MX3 M.2 AI Acceleration Module on compatible hardware platforms, including both x86 (Intel/AMD) and ARM-based SBCs such as Raspberry Pi 5.
-
-A single MemryX MX3 module is capable of handling multiple camera streams using the default models, making it sufficient for most users. For larger deployments with more cameras or bigger models, multiple MX3 modules can be used. Frigate supports multi-detector configurations, allowing you to connect multiple MX3 modules to scale inference capacity.
-
-Detailed information is available [in the detector docs](/configuration/object_detectors#memryx-mx3).
-
-**Default Model Configuration:**
-
-- Default model is **YOLO-NAS-Small**.
-
-The MX3 is a pipelined architecture, where the maximum frames per second supported (and thus supported number of cameras) cannot be calculated as `1/latency` (1/"Inference Time") and is measured separately. When estimating how many camera streams you may support with your configuration, use the **MX3 Total FPS** column to approximate of the detector's limit, not the Inference Time.
-
-| Model                | Input Size | MX3 Inference Time | MX3 Total FPS |
-| -------------------- | ---------- | ------------------ | ------------- |
-| YOLO-NAS-Small       | 320        | ~ 9 ms             | ~ 378         |
-| YOLO-NAS-Small       | 640        | ~ 21 ms            | ~ 138         |
-| YOLOv9s              | 320        | ~ 16 ms            | ~ 382         |
-| YOLOv9s              | 640        | ~ 41 ms            | ~ 110         |
-| YOLOX-Small          | 640        | ~ 16 ms            | ~ 263         |
-| SSDlite MobileNet v2 | 320        | ~ 5 ms             | ~ 1056        |
-
-Inference speeds may vary depending on the host platform. The above data was measured on an **Intel 13700 CPU**. Platforms like Raspberry Pi, Orange Pi, and other ARM-based SBCs have different levels of processing capability, which may limit total FPS.
-
 ### Nvidia Jetson
 
-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 +202,6 @@ 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.

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:
@@ -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

@@ -5,7 +5,7 @@ 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).
+The current stable version of Frigate is **0.15.0**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.15.0).
 
 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.
 
@@ -33,21 +33,21 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
 2. **Update and Pull the Latest Image**:
 
    - If using Docker Compose:
-     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.15.0` instead of `0.14.1`). For example:
        ```yaml
        services:
          frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.16.2
+           image: ghcr.io/blakeblackshear/frigate:0.15.0
        ```
      - Then pull the image:
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
+       docker pull ghcr.io/blakeblackshear/frigate:0.15.0
        ```
      - **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`):
+     - Pull the image with the appropriate tag (e.g., `0.15.0`, `0.15.0-tensorrt`, or `stable`):
        ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.2
+       docker pull ghcr.io/blakeblackshear/frigate:0.15.0
        ```
 
 3. **Start the Container**:
@@ -105,8 +105,8 @@ 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 Docker: Specify an older tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`) in your `docker run` command.
+   - For Docker Compose: Edit your `docker-compose.yml`, specify the older version tag (e.g., `ghcr.io/blakeblackshear/frigate:0.14.1`), 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.
 

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/integrations/home-assistant.md

@@ -185,26 +185,6 @@ 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

@@ -29,12 +29,12 @@ Message published for each changed tracked object. The first message is publishe
     "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": []
+        "frame_time": 1607123965.975463,
+        "box": [415, 489, 528, 700],
+        "area": 12728,
+        "region": [260, 446, 660, 846],
+        "score": 0.77546,
+        "attributes": [],
     },
     "label": "person",
     "sub_label": null,
@@ -61,7 +61,6 @@ 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
@@ -71,12 +70,12 @@ Message published for each changed tracked object. The first message is publishe
     "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": []
+        "frame_time": 1607123965.975463,
+        "box": [415, 489, 528, 700],
+        "area": 12728,
+        "region": [260, 446, 660, 846],
+        "score": 0.77546,
+        "attributes": [],
     },
     "label": "person",
     "sub_label": ["John Smith", 0.79],
@@ -110,7 +109,6 @@ 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 +139,7 @@ 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 +153,13 @@ 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 +206,6 @@ 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 +224,6 @@ 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 +257,6 @@ 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 +269,6 @@ 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 +391,6 @@ 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/plus/annotating.md

@@ -27,7 +27,7 @@ Suggestions are converted to labels when saving, so you should remove any errant
 
 ## 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. These can't be adjusted, but they can be deleted if you accidentally submit a true positive as a false positive from Frigate.
 ![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/first_model.md

@@ -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/index.md

@@ -11,51 +11,34 @@ Information on how to integrate Frigate+ with Frigate can be found in the [integ
 
 ## 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.
 
-| 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.
+
+:::warning
+
+Using Frigate+ models with `onnx` is only available with Frigate 0.15 and later.
+
+:::
 
 | 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`               |
+| [Intel](/configuration/object_detectors.md#openvino-detector)                    | `openvino`                | `yolonas`              |
+| [NVidia GPU](/configuration/object_detectors#onnx)\*                             | `onnx`                    | `yolonas`              |
+| [AMD ROCm GPU](/configuration/object_detectors#amdrocm-gpu-detector)\*           | `rocm`                    | `yolonas`              |
 
-_\* Requires manual conversion in 0.16. Automatic conversion coming in 0.17._
+_\* Requires Frigate 0.15_
 
 ## Improving your model
 
@@ -70,9 +53,9 @@ Some users may find that Frigate+ models result in more false positives initiall
 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:
 
 - **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.
@@ -83,7 +66,7 @@ Candidate labels are also available for annotation. These labels don't have enou
 
 Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
 
-The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`
+The candidate labels are: `baby`, `royal mail`, `canada post`, `bpost`, `skunk`, `badger`, `possum`, `rodent`, `kangaroo`, `chicken`, `groundhog`, `boar`, `hedgehog`, `school bus`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`
 
 Candidate labels are not available for automatic suggestions.
 

docs/docs/troubleshooting/memory.md

@@ -1,129 +0,0 @@
----
-id: memory
-title: Memory Troubleshooting
----
-
-Frigate includes built-in memory profiling using [memray](https://bloomberg.github.io/memray/) to help diagnose memory issues. This feature allows you to profile specific Frigate modules to identify memory leaks, excessive allocations, or other memory-related problems.
-
-## Enabling Memory Profiling
-
-Memory profiling is controlled via the `FRIGATE_MEMRAY_MODULES` environment variable. Set it to a comma-separated list of module names you want to profile:
-
-```bash
-export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
-```
-
-### Module Names
-
-Frigate processes are named using a module-based naming scheme. Common module names include:
-
-- `frigate.review_segment_manager` - Review segment processing
-- `frigate.recording_manager` - Recording management
-- `frigate.capture` - Camera capture processes (all cameras with this module name)
-- `frigate.process` - Camera processing/tracking (all cameras with this module name)
-- `frigate.output` - Output processing
-- `frigate.audio_manager` - Audio processing
-- `frigate.embeddings` - Embeddings processing
-
-You can also specify the full process name (including camera-specific identifiers) if you want to profile a specific camera:
-
-```bash
-export FRIGATE_MEMRAY_MODULES="frigate.capture:front_door"
-```
-
-When you specify a module name (e.g., `frigate.capture`), all processes with that module prefix will be profiled. For example, `frigate.capture` will profile all camera capture processes.
-
-## How It Works
-
-1. **Binary File Creation**: When profiling is enabled, memray creates a binary file (`.bin`) in `/config/memray_reports/` that is updated continuously in real-time as the process runs.
-
-2. **Automatic HTML Generation**: On normal process exit, Frigate automatically:
-
-   - Stops memray tracking
-   - Generates an HTML flamegraph report
-   - Saves it to `/config/memray_reports/<module_name>.html`
-
-3. **Crash Recovery**: If a process crashes (SIGKILL, segfault, etc.), the binary file is preserved with all data up to the crash point. You can manually generate the HTML report from the binary file.
-
-## Viewing Reports
-
-### Automatic Reports
-
-After a process exits normally, you'll find HTML reports in `/config/memray_reports/`. Open these files in a web browser to view interactive flamegraphs showing memory usage patterns.
-
-### Manual Report Generation
-
-If a process crashes or you want to generate a report from an existing binary file, you can manually create the HTML report:
-
-```bash
-memray flamegraph /config/memray_reports/<module_name>.bin
-```
-
-This will generate an HTML file that you can open in your browser.
-
-## Understanding the Reports
-
-Memray flamegraphs show:
-
-- **Memory allocations over time**: See where memory is being allocated in your code
-- **Call stacks**: Understand the full call chain leading to allocations
-- **Memory hotspots**: Identify functions or code paths that allocate the most memory
-- **Memory leaks**: Spot patterns where memory is allocated but not freed
-
-The interactive HTML reports allow you to:
-
-- Zoom into specific time ranges
-- Filter by function names
-- View detailed allocation information
-- Export data for further analysis
-
-## Best Practices
-
-1. **Profile During Issues**: Enable profiling when you're experiencing memory issues, not all the time, as it adds some overhead.
-
-2. **Profile Specific Modules**: Instead of profiling everything, focus on the modules you suspect are causing issues.
-
-3. **Let Processes Run**: Allow processes to run for a meaningful duration to capture representative memory usage patterns.
-
-4. **Check Binary Files**: If HTML reports aren't generated automatically (e.g., after a crash), check for `.bin` files in `/config/memray_reports/` and generate reports manually.
-
-5. **Compare Reports**: Generate reports at different times to compare memory usage patterns and identify trends.
-
-## Troubleshooting
-
-### No Reports Generated
-
-- Check that the environment variable is set correctly
-- Verify the module name matches exactly (case-sensitive)
-- Check logs for memray-related errors
-- Ensure `/config/memray_reports/` directory exists and is writable
-
-### Process Crashed Before Report Generation
-
-- Look for `.bin` files in `/config/memray_reports/`
-- Manually generate HTML reports using: `memray flamegraph <file>.bin`
-- The binary file contains all data up to the crash point
-
-### Reports Show No Data
-
-- Ensure the process ran long enough to generate meaningful data
-- Check that memray is properly installed (included by default in Frigate)
-- Verify the process actually started and ran (check process logs)
-
-## Example Usage
-
-```bash
-# Enable profiling for review and capture modules
-export FRIGATE_MEMRAY_MODULES="frigate.review_segment_manager,frigate.capture"
-
-# Start Frigate
-# ... let it run for a while ...
-
-# Check for reports
-ls -lh /config/memray_reports/
-
-# If a process crashed, manually generate report
-memray flamegraph /config/memray_reports/frigate_capture_front_door.bin
-```
-
-For more information about memray and interpreting reports, see the [official memray documentation](https://bloomberg.github.io/memray/).

docs/docusaurus.config.ts

@@ -10,7 +10,7 @@ const config: Config = {
   baseUrl: "/",
   onBrokenLinks: "throw",
   onBrokenMarkdownLinks: "warn",
-  favicon: "img/branding/favicon.ico",
+  favicon: "img/favicon.ico",
   organizationName: "blakeblackshear",
   projectName: "frigate",
   themes: [
@@ -116,8 +116,8 @@ const config: Config = {
       title: "Frigate",
       logo: {
         alt: "Frigate",
-        src: "img/branding/logo.svg",
-        srcDark: "img/branding/logo-dark.svg",
+        src: "img/logo.svg",
+        srcDark: "img/logo-dark.svg",
       },
       items: [
         {
@@ -170,7 +170,7 @@ const config: Config = {
           ],
         },
       ],
-      copyright: `Copyright © ${new Date().getFullYear()} Frigate LLC`,
+      copyright: `Copyright © ${new Date().getFullYear()} Blake Blackshear`,
     },
   },
   plugins: [

docs/sidebars.ts

@@ -5,14 +5,13 @@ import frigateHttpApiSidebar from "./docs/integrations/api/sidebar";
 const sidebars: SidebarsConfig = {
   docs: {
     Frigate: [
-      "frigate/index",
-      "frigate/hardware",
-      "frigate/planning_setup",
-      "frigate/installation",
-      "frigate/updating",
-      "frigate/camera_setup",
-      "frigate/video_pipeline",
-      "frigate/glossary",
+      'frigate/index',
+      'frigate/hardware',
+      'frigate/installation',
+      'frigate/updating',
+      'frigate/camera_setup',
+      'frigate/video_pipeline',
+      'frigate/glossary',
     ],
     Guides: [
       "guides/getting_started",
@@ -28,7 +27,7 @@ const sidebars: SidebarsConfig = {
         {
           type: "link",
           label: "Go2RTC Configuration Reference",
-          href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.10#configuration",
+          href: "https://github.com/AlexxIT/go2rtc/tree/v1.9.9#configuration",
         } as PropSidebarItemLink,
       ],
       Detectors: [
@@ -37,36 +36,10 @@ const sidebars: SidebarsConfig = {
       ],
       Enrichments: [
         "configuration/semantic_search",
+        "configuration/genai",
         "configuration/face_recognition",
         "configuration/license_plate_recognition",
         "configuration/bird_classification",
-        {
-          type: "category",
-          label: "Custom Classification",
-          link: {
-            type: "generated-index",
-            title: "Custom Classification",
-            description: "Configuration for custom classification models",
-          },
-          items: [
-            "configuration/custom_classification/state_classification",
-            "configuration/custom_classification/object_classification",
-          ],
-        },
-        {
-          type: "category",
-          label: "Generative AI",
-          link: {
-            type: "generated-index",
-            title: "Generative AI",
-            description: "Generative AI Features",
-          },
-          items: [
-            "configuration/genai/genai_config",
-            "configuration/genai/genai_review",
-            "configuration/genai/genai_objects",
-          ],
-        },
       ],
       Cameras: [
         "configuration/cameras",
@@ -116,22 +89,20 @@ const sidebars: SidebarsConfig = {
         items: frigateHttpApiSidebar,
       },
       "integrations/mqtt",
-      "integrations/homekit",
       "configuration/metrics",
       "integrations/third_party_extensions",
     ],
-    "Frigate+": [
-      "plus/index",
-      "plus/annotating",
-      "plus/first_model",
-      "plus/faq",
+    'Frigate+': [
+      'plus/index',
+      'plus/annotating',
+      'plus/first_model',
+      'plus/faq',
     ],
     Troubleshooting: [
       "troubleshooting/faqs",
       "troubleshooting/recordings",
       "troubleshooting/gpu",
       "troubleshooting/edgetpu",
-      "troubleshooting/memory",
     ],
     Development: [
       "development/contributing",

docs/src/components/CommunityBadge/index.jsx

@@ -1,23 +0,0 @@
-import React from "react";
-
-export default function CommunityBadge() {
-  return (
-    <span
-      title="This detector is maintained by community members who provide code, maintenance, and support. See the contributing boards documentation for more information."
-      style={{
-        display: "inline-block",
-        backgroundColor: "#f1f3f5",
-        color: "#24292f",
-        fontSize: "11px",
-        fontWeight: 600,
-        padding: "2px 6px",
-        borderRadius: "3px",
-        border: "1px solid #d1d9e0",
-        marginLeft: "4px",
-        cursor: "help",
-      }}
-    >
-      Community Supported
-    </span>
-  );
-}

docs/src/components/LanguageAlert/styles.module.css

@@ -1,18 +1,13 @@
 .alert {
-  padding: 12px;
-  background: #fff8e6;
-  border-bottom: 1px solid #ffd166;
-  text-align: center;
-  font-size: 15px;
-}
-
-[data-theme="dark"] .alert {
-  background: #3b2f0b;
-  border-bottom: 1px solid #665c22;
-}
-
-.alert a {
-  color: #1890ff;
-  font-weight: 500;
-  margin-left: 6px;
-}
+    padding: 12px;
+    background: #fff8e6;
+    border-bottom: 1px solid #ffd166;
+    text-align: center;
+    font-size: 15px;
+  }
+  
+  .alert a {
+    color: #1890ff;
+    font-weight: 500;
+    margin-left: 6px;
+  }

docs/static/img/branding/LICENSE.md

@@ -1,30 +0,0 @@
-# COPYRIGHT AND TRADEMARK NOTICE
-
-The images, logos, and icons contained in this directory (the "Brand Assets") are
-proprietary to Frigate LLC and are NOT covered by the MIT License governing the
-rest of this repository.
-
-1. TRADEMARK STATUS
-   The "Frigate" name and the accompanying logo are common law trademarks™ of
-   Frigate LLC. Frigate LLC reserves all rights to these marks.
-
-2. LIMITED PERMISSION FOR USE
-   Permission is hereby granted to display these Brand Assets strictly for the
-   following purposes:
-   a. To execute the software interface on a local machine.
-   b. To identify the software in documentation or reviews (nominative use).
-
-3. RESTRICTIONS
-   You may NOT:
-   a. Use these Brand Assets to represent a derivative work (fork) as an official
-   product of Frigate LLC.
-   b. Use these Brand Assets in a way that implies endorsement, sponsorship, or
-   commercial affiliation with Frigate LLC.
-   c. Modify or alter the Brand Assets.
-
-If you fork this repository with the intent to distribute a modified or competing
-version of the software, you must replace these Brand Assets with your own
-original content.
-
-ALL RIGHTS RESERVED.
-Copyright (c) 2025 Frigate LLC.

frigate/__main__.py

@@ -1,6 +1,5 @@
 import argparse
 import faulthandler
-import multiprocessing as mp
 import signal
 import sys
 import threading
@@ -16,17 +15,12 @@ from frigate.util.config import find_config_file
 
 
 def main() -> None:
-    manager = mp.Manager()
     faulthandler.enable()
 
     # Setup the logging thread
-    setup_logging(manager)
+    setup_logging()
 
     threading.current_thread().name = "frigate"
-    stop_event = mp.Event()
-
-    # send stop event on SIGINT
-    signal.signal(signal.SIGINT, lambda sig, frame: stop_event.set())
 
     # Make sure we exit cleanly on SIGTERM.
     signal.signal(signal.SIGTERM, lambda sig, frame: sys.exit())
@@ -99,14 +93,7 @@ def main() -> None:
         print("*************************************************************")
         print("***    End Config Validation Errors                       ***")
         print("*************************************************************")
-
-        # attempt to start Frigate in recovery mode
-        try:
-            config = FrigateConfig.load(install=True, safe_load=True)
-            print("Starting Frigate in safe mode.")
-        except ValidationError:
-            print("Unable to start Frigate in safe mode.")
-            sys.exit(1)
+        sys.exit(1)
     if args.validate_config:
         print("*************************************************************")
         print("*** Your config file is valid.                            ***")
@@ -114,23 +101,8 @@ def main() -> None:
         sys.exit(0)
 
     # Run the main application.
-    FrigateApp(config, manager, stop_event).start()
+    FrigateApp(config).start()
 
 
 if __name__ == "__main__":
-    mp.set_forkserver_preload(
-        [
-            # Standard library and core dependencies
-            "sqlite3",
-            # Third-party libraries commonly used in Frigate
-            "numpy",
-            "cv2",
-            "peewee",
-            "zmq",
-            "ruamel.yaml",
-            # Frigate core modules
-            "frigate.camera.maintainer",
-        ]
-    )
-    mp.set_start_method("forkserver", force=True)
     main()

frigate/api/app.py

@@ -6,48 +6,43 @@ import json
 import logging
 import os
 import traceback
-import urllib
 from datetime import datetime, timedelta
 from functools import reduce
 from io import StringIO
 from pathlib import Path as FilePath
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import aiofiles
+import requests
 import ruamel.yaml
 from fastapi import APIRouter, Body, Path, Request, Response
 from fastapi.encoders import jsonable_encoder
 from fastapi.params import Depends
 from fastapi.responses import JSONResponse, PlainTextResponse, StreamingResponse
 from markupsafe import escape
-from peewee import SQL, fn, operator
+from peewee import operator
 from pydantic import ValidationError
 
-from frigate.api.auth import allow_any_authenticated, allow_public, require_role
+from frigate.api.auth import require_role
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import AppConfigSetBody
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
-from frigate.config.camera.updater import (
-    CameraConfigUpdateEnum,
-    CameraConfigUpdateTopic,
-)
 from frigate.models import Event, Timeline
 from frigate.stats.prometheus import get_metrics, update_metrics
 from frigate.util.builtin import (
     clean_camera_user_pass,
-    flatten_config_data,
-    process_config_query_string,
-    update_yaml_file_bulk,
+    get_tz_modifiers,
+    update_yaml_from_url,
 )
 from frigate.util.config import find_config_file
 from frigate.util.services import (
+    ffprobe_stream,
     get_nvidia_driver_info,
     process_logs,
     restart_frigate,
     vainfo_hwaccel,
 )
-from frigate.util.time import get_tz_modifiers
 from frigate.version import VERSION
 
 logger = logging.getLogger(__name__)
@@ -56,33 +51,66 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.app])
 
 
-@router.get(
-    "/", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
-)
+@router.get("/", response_class=PlainTextResponse)
 def is_healthy():
     return "Frigate is running. Alive and healthy!"
 
 
-@router.get("/config/schema.json", dependencies=[Depends(allow_public())])
+@router.get("/config/schema.json")
 def config_schema(request: Request):
     return Response(
         content=request.app.frigate_config.schema_json(), media_type="application/json"
     )
 
 
-@router.get(
-    "/version", response_class=PlainTextResponse, dependencies=[Depends(allow_public())]
-)
+@router.get("/go2rtc/streams")
+def go2rtc_streams():
+    r = requests.get("http://127.0.0.1:1984/api/streams")
+    if not r.ok:
+        logger.error("Failed to fetch streams from go2rtc")
+        return JSONResponse(
+            content=({"success": False, "message": "Error fetching stream data"}),
+            status_code=500,
+        )
+    stream_data = r.json()
+    for data in stream_data.values():
+        for producer in data.get("producers") or []:
+            producer["url"] = clean_camera_user_pass(producer.get("url", ""))
+    return JSONResponse(content=stream_data)
+
+
+@router.get("/go2rtc/streams/{camera_name}")
+def go2rtc_camera_stream(request: Request, camera_name: str):
+    r = requests.get(
+        f"http://127.0.0.1:1984/api/streams?src={camera_name}&video=all&audio=all&microphone"
+    )
+    if not r.ok:
+        camera_config = request.app.frigate_config.cameras.get(camera_name)
+
+        if camera_config and camera_config.enabled:
+            logger.error("Failed to fetch streams from go2rtc")
+
+        return JSONResponse(
+            content=({"success": False, "message": "Error fetching stream data"}),
+            status_code=500,
+        )
+    stream_data = r.json()
+    for producer in stream_data.get("producers", []):
+        producer["url"] = clean_camera_user_pass(producer.get("url", ""))
+    return JSONResponse(content=stream_data)
+
+
+@router.get("/version", response_class=PlainTextResponse)
 def version():
     return VERSION
 
 
-@router.get("/stats", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/stats")
 def stats(request: Request):
     return JSONResponse(content=request.app.stats_emitter.get_latest_stats())
 
 
-@router.get("/stats/history", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/stats/history")
 def stats_history(request: Request, keys: str = None):
     if keys:
         keys = keys.split(",")
@@ -90,24 +118,17 @@ def stats_history(request: Request, keys: str = None):
     return JSONResponse(content=request.app.stats_emitter.get_stats_history(keys))
 
 
-@router.get("/metrics", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/metrics")
 def metrics(request: Request):
     """Expose Prometheus metrics endpoint and update metrics with latest stats"""
     # Retrieve the latest statistics and update the Prometheus metrics
     stats = request.app.stats_emitter.get_latest_stats()
-    # query DB for count of events by camera, label
-    event_counts: List[Dict[str, Any]] = (
-        Event.select(Event.camera, Event.label, fn.Count())
-        .group_by(Event.camera, Event.label)
-        .dicts()
-    )
-
-    update_metrics(stats=stats, event_counts=event_counts)
+    update_metrics(stats)
     content, content_type = get_metrics()
     return Response(content=content, media_type=content_type)
 
 
-@router.get("/config", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/config")
 def config(request: Request):
     config_obj: FrigateConfig = request.app.frigate_config
     config: dict[str, dict[str, Any]] = config_obj.model_dump(
@@ -183,37 +204,7 @@ def config(request: Request):
     return JSONResponse(content=config)
 
 
-@router.get("/config/raw_paths", dependencies=[Depends(require_role(["admin"]))])
-def config_raw_paths(request: Request):
-    """Admin-only endpoint that returns camera paths and go2rtc streams without credential masking."""
-    config_obj: FrigateConfig = request.app.frigate_config
-
-    raw_paths = {"cameras": {}, "go2rtc": {"streams": {}}}
-
-    # Extract raw camera ffmpeg input paths
-    for camera_name, camera in config_obj.cameras.items():
-        raw_paths["cameras"][camera_name] = {
-            "ffmpeg": {
-                "inputs": [
-                    {"path": input.path, "roles": input.roles}
-                    for input in camera.ffmpeg.inputs
-                ]
-            }
-        }
-
-    # Extract raw go2rtc stream URLs
-    go2rtc_config = config_obj.go2rtc.model_dump(
-        mode="json", warnings="none", exclude_none=True
-    )
-    for stream_name, stream in go2rtc_config.get("streams", {}).items():
-        if stream is None:
-            continue
-        raw_paths["go2rtc"]["streams"][stream_name] = stream
-
-    return JSONResponse(content=raw_paths)
-
-
-@router.get("/config/raw", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/config/raw")
 def config_raw():
     config_file = find_config_file()
 
@@ -363,37 +354,14 @@ def config_set(request: Request, body: AppConfigSetBody):
 
     with open(config_file, "r") as f:
         old_raw_config = f.read()
+        f.close()
 
     try:
-        updates = {}
-
-        # process query string parameters (takes precedence over body.config_data)
-        parsed_url = urllib.parse.urlparse(str(request.url))
-        query_string = urllib.parse.parse_qs(parsed_url.query, keep_blank_values=True)
-
-        # Filter out empty keys but keep blank values for non-empty keys
-        query_string = {k: v for k, v in query_string.items() if k}
-
-        if query_string:
-            updates = process_config_query_string(query_string)
-        elif body.config_data:
-            updates = flatten_config_data(body.config_data)
-
-        if not updates:
-            return JSONResponse(
-                content=(
-                    {"success": False, "message": "No configuration data provided"}
-                ),
-                status_code=400,
-            )
-
-        # apply all updates in a single operation
-        update_yaml_file_bulk(config_file, updates)
-
-        # validate the updated config
+        update_yaml_from_url(config_file, str(request.url))
         with open(config_file, "r") as f:
             new_raw_config = f.read()
-
+            f.close()
+        # Validate the config schema
         try:
             config = FrigateConfig.parse(new_raw_config)
         except Exception:
@@ -417,34 +385,8 @@ def config_set(request: Request, body: AppConfigSetBody):
             status_code=500,
         )
 
-    if body.requires_restart == 0 or body.update_topic:
-        old_config: FrigateConfig = request.app.frigate_config
+    if body.requires_restart == 0:
         request.app.frigate_config = config
-
-        if body.update_topic:
-            if body.update_topic.startswith("config/cameras/"):
-                _, _, camera, field = body.update_topic.split("/")
-
-                if field == "add":
-                    settings = config.cameras[camera]
-                elif field == "remove":
-                    settings = old_config.cameras[camera]
-                else:
-                    settings = config.get_nested_object(body.update_topic)
-
-                request.app.config_publisher.publish_update(
-                    CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
-                    settings,
-                )
-            else:
-                # Generic handling for global config updates
-                settings = config.get_nested_object(body.update_topic)
-
-                # Publish None for removal, actual config for add/update
-                request.app.config_publisher.publisher.publish(
-                    body.update_topic, settings
-                )
-
     return JSONResponse(
         content=(
             {
@@ -456,7 +398,67 @@ def config_set(request: Request, body: AppConfigSetBody):
     )
 
 
-@router.get("/vainfo", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/ffprobe")
+def ffprobe(request: Request, paths: str = ""):
+    path_param = paths
+
+    if not path_param:
+        return JSONResponse(
+            content=({"success": False, "message": "Path needs to be provided."}),
+            status_code=404,
+        )
+
+    if path_param.startswith("camera"):
+        camera = path_param[7:]
+
+        if camera not in request.app.frigate_config.cameras.keys():
+            return JSONResponse(
+                content=(
+                    {"success": False, "message": f"{camera} is not a valid camera."}
+                ),
+                status_code=404,
+            )
+
+        if not request.app.frigate_config.cameras[camera].enabled:
+            return JSONResponse(
+                content=({"success": False, "message": f"{camera} is not enabled."}),
+                status_code=404,
+            )
+
+        paths = map(
+            lambda input: input.path,
+            request.app.frigate_config.cameras[camera].ffmpeg.inputs,
+        )
+    elif "," in clean_camera_user_pass(path_param):
+        paths = path_param.split(",")
+    else:
+        paths = [path_param]
+
+    # user has multiple streams
+    output = []
+
+    for path in paths:
+        ffprobe = ffprobe_stream(request.app.frigate_config.ffmpeg, path.strip())
+        output.append(
+            {
+                "return_code": ffprobe.returncode,
+                "stderr": (
+                    ffprobe.stderr.decode("unicode_escape").strip()
+                    if ffprobe.returncode != 0
+                    else ""
+                ),
+                "stdout": (
+                    json.loads(ffprobe.stdout.decode("unicode_escape").strip())
+                    if ffprobe.returncode == 0
+                    else ""
+                ),
+            }
+        )
+
+    return JSONResponse(content=output)
+
+
+@router.get("/vainfo")
 def vainfo():
     vainfo = vainfo_hwaccel()
     return JSONResponse(
@@ -476,16 +478,12 @@ def vainfo():
     )
 
 
-@router.get("/nvinfo", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/nvinfo")
 def nvinfo():
     return JSONResponse(content=get_nvidia_driver_info())
 
 
-@router.get(
-    "/logs/{service}",
-    tags=[Tags.logs],
-    dependencies=[Depends(allow_any_authenticated())],
-)
+@router.get("/logs/{service}", tags=[Tags.logs])
 async def logs(
     service: str = Path(enum=["frigate", "nginx", "go2rtc"]),
     download: Optional[str] = None,
@@ -593,7 +591,7 @@ def restart():
     )
 
 
-@router.get("/labels", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/labels")
 def get_labels(camera: str = ""):
     try:
         if camera:
@@ -611,7 +609,7 @@ def get_labels(camera: str = ""):
     return JSONResponse(content=labels)
 
 
-@router.get("/sub_labels", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/sub_labels")
 def get_sub_labels(split_joined: Optional[int] = None):
     try:
         events = Event.select(Event.sub_label).distinct()
@@ -642,7 +640,7 @@ def get_sub_labels(split_joined: Optional[int] = None):
     return JSONResponse(content=sub_labels)
 
 
-@router.get("/plus/models", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/plus/models")
 def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
     if not request.app.frigate_config.plus_api.is_active():
         return JSONResponse(
@@ -684,19 +682,10 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
     return JSONResponse(content=validModels)
 
 
-@router.get(
-    "/recognized_license_plates", dependencies=[Depends(allow_any_authenticated())]
-)
+@router.get("/recognized_license_plates")
 def get_recognized_license_plates(split_joined: Optional[int] = None):
     try:
-        query = (
-            Event.select(
-                SQL("json_extract(data, '$.recognized_license_plate') AS plate")
-            )
-            .where(SQL("json_extract(data, '$.recognized_license_plate') IS NOT NULL"))
-            .distinct()
-        )
-        recognized_license_plates = [row[0] for row in query.tuples()]
+        events = Event.select(Event.data).distinct()
     except Exception:
         return JSONResponse(
             content=(
@@ -705,6 +694,14 @@ def get_recognized_license_plates(split_joined: Optional[int] = None):
             status_code=404,
         )
 
+    recognized_license_plates = []
+    for e in events:
+        if e.data is not None and "recognized_license_plate" in e.data:
+            recognized_license_plates.append(e.data["recognized_license_plate"])
+
+    while None in recognized_license_plates:
+        recognized_license_plates.remove(None)
+
     if split_joined:
         original_recognized_license_plates = recognized_license_plates.copy()
         for recognized_license_plate in original_recognized_license_plates:
@@ -720,7 +717,7 @@ def get_recognized_license_plates(split_joined: Optional[int] = None):
     return JSONResponse(content=recognized_license_plates)
 
 
-@router.get("/timeline", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/timeline")
 def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = None):
     clauses = []
 
@@ -737,11 +734,7 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
         clauses.append((Timeline.camera == camera))
 
     if source_id:
-        source_ids = [sid.strip() for sid in source_id.split(",")]
-        if len(source_ids) == 1:
-            clauses.append((Timeline.source_id == source_ids[0]))
-        else:
-            clauses.append((Timeline.source_id.in_(source_ids)))
+        clauses.append((Timeline.source_id == source_id))
 
     if len(clauses) == 0:
         clauses.append((True))
@@ -757,7 +750,7 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
     return JSONResponse(content=[t for t in timeline])
 
 
-@router.get("/timeline/hourly", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/timeline/hourly")
 def hourly_timeline(params: AppTimelineHourlyQueryParameters = Depends()):
     """Get hourly summary for timeline."""
     cameras = params.cameras

frigate/api/auth.py

@@ -11,7 +11,7 @@ import secrets
 import time
 from datetime import datetime
 from pathlib import Path
-from typing import List, Optional
+from typing import List
 
 from fastapi import APIRouter, Depends, HTTPException, Request, Response
 from fastapi.responses import JSONResponse, RedirectResponse
@@ -32,192 +32,8 @@ from frigate.models import User
 
 logger = logging.getLogger(__name__)
 
-
-def require_admin_by_default():
-    """
-    Global admin requirement dependency for all endpoints by default.
-
-    This is set as the default dependency on the FastAPI app to ensure all
-    endpoints require admin access unless explicitly overridden with
-    allow_public(), allow_any_authenticated(), or require_role().
-
-    Port 5000 (internal) always has admin role set by the /auth endpoint,
-    so this check passes automatically for internal requests.
-
-    Certain paths are exempted from the global admin check because they must
-    be accessible before authentication (login, auth) or they have their own
-    route-level authorization dependencies that handle access control.
-    """
-    # Paths that have route-level auth dependencies and should bypass global admin check
-    # These paths still have authorization - it's handled by their route-level dependencies
-    EXEMPT_PATHS = {
-        # Public auth endpoints (allow_public)
-        "/auth",
-        "/auth/first_time_login",
-        "/login",
-        # Authenticated user endpoints (allow_any_authenticated)
-        "/logout",
-        "/profile",
-        # Public info endpoints (allow_public)
-        "/",
-        "/version",
-        "/config/schema.json",
-        # Authenticated user endpoints (allow_any_authenticated)
-        "/metrics",
-        "/stats",
-        "/stats/history",
-        "/config",
-        "/config/raw",
-        "/vainfo",
-        "/nvinfo",
-        "/labels",
-        "/sub_labels",
-        "/plus/models",
-        "/recognized_license_plates",
-        "/timeline",
-        "/timeline/hourly",
-        "/recordings/storage",
-        "/recordings/summary",
-        "/recordings/unavailable",
-        "/go2rtc/streams",
-        "/event_ids",
-        "/events",
-        "/exports",
-    }
-
-    # Path prefixes that should be exempt (for paths with parameters)
-    EXEMPT_PREFIXES = (
-        "/logs/",  # /logs/{service}
-        "/review",  # /review, /review/{id}, /review/summary, /review_ids, etc.
-        "/reviews/",  # /reviews/viewed, /reviews/delete
-        "/events/",  # /events/{id}/thumbnail, /events/summary, etc. (camera-scoped)
-        "/export/",  # /export/{camera}/start/..., /export/{id}/rename, /export/{id}
-        "/go2rtc/streams/",  # /go2rtc/streams/{camera}
-        "/users/",  # /users/{username}/password (has own auth)
-        "/preview/",  # /preview/{file}/thumbnail.jpg
-        "/exports/",  # /exports/{export_id}
-        "/vod/",  # /vod/{camera_name}/...
-        "/notifications/",  # /notifications/pubkey, /notifications/register
-    )
-
-    async def admin_checker(request: Request):
-        path = request.url.path
-
-        # Check exact path matches
-        if path in EXEMPT_PATHS:
-            return
-
-        # Check prefix matches for parameterized paths
-        if path.startswith(EXEMPT_PREFIXES):
-            return
-
-        # Dynamic camera path exemption:
-        # Any path whose first segment matches a configured camera name should
-        # bypass the global admin requirement. These endpoints enforce access
-        # via route-level dependencies (e.g. require_camera_access) to ensure
-        # per-camera authorization. This allows non-admin authenticated users
-        # (e.g. viewer role) to access camera-specific resources without
-        # needing admin privileges.
-        try:
-            if path.startswith("/"):
-                first_segment = path.split("/", 2)[1]
-                if (
-                    first_segment
-                    and first_segment in request.app.frigate_config.cameras
-                ):
-                    return
-        except Exception:
-            pass
-
-        # For all other paths, require admin role
-        # Port 5000 (internal) requests have admin role set automatically
-        role = request.headers.get("remote-role")
-        if role == "admin":
-            return
-
-        raise HTTPException(
-            status_code=403,
-            detail="Access denied. A user with the admin role is required.",
-        )
-
-    return admin_checker
-
-
-def _is_authenticated(request: Request) -> bool:
-    """
-    Helper to determine if a request is from an authenticated user.
-
-    Returns True if the request has a valid authenticated user (not anonymous).
-    Port 5000 internal requests are considered anonymous despite having admin role.
-    """
-    username = request.headers.get("remote-user")
-    return username is not None and username != "anonymous"
-
-
-def allow_public():
-    """
-    Override dependency to allow unauthenticated access to an endpoint.
-
-    Use this for endpoints that should be publicly accessible without
-    authentication, such as login page, health checks, or pre-auth info.
-
-    Example:
-        @router.get("/public-endpoint", dependencies=[Depends(allow_public())])
-    """
-
-    async def public_checker(request: Request):
-        return  # Always allow
-
-    return public_checker
-
-
-def allow_any_authenticated():
-    """
-    Override dependency to allow any authenticated user (bypass admin requirement).
-
-    Allows:
-    - Port 5000 internal requests (have admin role despite anonymous user)
-    - Any authenticated user with a real username (not "anonymous")
-
-    Rejects:
-    - Port 8971 requests with anonymous user (auth disabled, no proxy auth)
-
-    Example:
-        @router.get("/authenticated-endpoint", dependencies=[Depends(allow_any_authenticated())])
-    """
-
-    async def auth_checker(request: Request):
-        # Port 5000 requests have admin role and should be allowed
-        role = request.headers.get("remote-role")
-        if role == "admin":
-            return
-
-        # Otherwise require a real authenticated user (not anonymous)
-        if not _is_authenticated(request):
-            raise HTTPException(status_code=401, detail="Authentication required")
-        return
-
-    return auth_checker
-
-
 router = APIRouter(tags=[Tags.auth])
-
-
-@router.get("/auth/first_time_login", dependencies=[Depends(allow_public())])
-def first_time_login(request: Request):
-    """Return whether the admin first-time login help flag is set in config.
-
-    This endpoint is intentionally unauthenticated so the login page can
-    query it before a user is authenticated.
-    """
-    auth_config = request.app.frigate_config.auth
-
-    return JSONResponse(
-        content={
-            "admin_first_time_login": auth_config.admin_first_time_login
-            or auth_config.reset_admin_password
-        }
-    )
+VALID_ROLES = ["admin", "viewer"]
 
 
 class RateLimiter:
@@ -255,7 +71,7 @@ def get_remote_addr(request: Request):
             )
             if trusted_proxy.version == 4:
                 ipv4 = ip.ipv4_mapped if ip.version == 6 else ip
-                if ipv4 is not None and ipv4 in trusted_proxy:
+                if ipv4 in trusted_proxy:
                     trusted = True
                     logger.debug(f"Trusted: {str(ip)} by {str(trusted_proxy)}")
                     break
@@ -388,7 +204,6 @@ async def get_current_user(request: Request):
 def require_role(required_roles: List[str]):
     async def role_checker(request: Request):
         proxy_config: ProxyConfig = request.app.frigate_config.proxy
-        config_roles = list(request.app.frigate_config.auth.roles.keys())
 
         # Get role from header (could be comma-separated)
         role_header = request.headers.get("remote-role")
@@ -402,125 +217,21 @@ def require_role(required_roles: List[str]):
         if not roles:
             raise HTTPException(status_code=403, detail="Role not provided")
 
-        # enforce config roles
-        valid_roles = [r for r in roles if r in config_roles]
-        if not valid_roles:
+        # Check if any role matches required_roles
+        if not any(role in required_roles for role in roles):
             raise HTTPException(
                 status_code=403,
-                detail=f"No valid roles found in {roles}. Required: {', '.join(required_roles)}. Available: {', '.join(config_roles)}",
+                detail=f"Role {', '.join(roles)} not authorized. Required: {', '.join(required_roles)}",
             )
 
-        if not any(role in required_roles for role in valid_roles):
-            raise HTTPException(
-                status_code=403,
-                detail=f"Role {', '.join(valid_roles)} not authorized. Required: {', '.join(required_roles)}",
-            )
-
-        return next(
-            (role for role in valid_roles if role in required_roles), valid_roles[0]
-        )
+        # Return the first matching role
+        return next((role for role in roles if role in required_roles), roles[0])
 
     return role_checker
 
 
-def resolve_role(
-    headers: dict, proxy_config: ProxyConfig, config_roles: set[str]
-) -> str:
-    """
-    Determine the effective role for a request based on proxy headers and configuration.
-
-    Order of resolution:
-      1. If a role header is defined in proxy_config.header_map.role:
-         - If a role_map is configured, treat the header as group claims
-           (split by proxy_config.separator) and map to roles.
-         - If no role_map is configured, treat the header as role names directly.
-      2. If no valid role is found, return proxy_config.default_role if it's valid in config_roles, else 'viewer'.
-
-    Args:
-        headers (dict): Incoming request headers (case-insensitive).
-        proxy_config (ProxyConfig): Proxy configuration.
-        config_roles (set[str]): Set of valid roles from config.
-
-    Returns:
-        str: Resolved role (one of config_roles or validated default).
-    """
-    default_role = proxy_config.default_role
-    role_header = proxy_config.header_map.role
-
-    # Validate default_role against config; fallback to 'viewer' if invalid
-    validated_default = default_role if default_role in config_roles else "viewer"
-    if not config_roles:
-        validated_default = "viewer"  # Edge case: no roles defined
-
-    if not role_header:
-        logger.debug(
-            "No role header configured in proxy_config.header_map. Returning validated default role '%s'.",
-            validated_default,
-        )
-        return validated_default
-
-    raw_value = headers.get(role_header, "")
-    logger.debug("Raw role header value from '%s': %r", role_header, raw_value)
-
-    if not raw_value:
-        logger.debug(
-            "Role header missing or empty. Returning validated default role '%s'.",
-            validated_default,
-        )
-        return validated_default
-
-    # role_map configured, treat header as group claims
-    if proxy_config.header_map.role_map:
-        groups = [
-            g.strip() for g in raw_value.split(proxy_config.separator) if g.strip()
-        ]
-        logger.debug("Parsed groups from role header: %s", groups)
-
-        matched_roles = {
-            role_name
-            for role_name, required_groups in proxy_config.header_map.role_map.items()
-            if any(group in groups for group in required_groups)
-        }
-        logger.debug("Matched roles from role_map: %s", matched_roles)
-
-        if matched_roles:
-            resolved = next(
-                (r for r in config_roles if r in matched_roles), validated_default
-            )
-            logger.debug("Resolved role (with role_map) to '%s'.", resolved)
-            return resolved
-
-        logger.debug(
-            "No role_map match for groups '%s'. Using validated default role '%s'.",
-            raw_value,
-            validated_default,
-        )
-        return validated_default
-
-    # no role_map, treat as role names directly
-    roles_from_header = [
-        r.strip().lower() for r in raw_value.split(proxy_config.separator) if r.strip()
-    ]
-    logger.debug("Parsed roles directly from header: %s", roles_from_header)
-
-    resolved = next(
-        (r for r in config_roles if r in roles_from_header),
-        validated_default,
-    )
-    if resolved == validated_default and roles_from_header:
-        logger.debug(
-            "Provided proxy role header values '%s' did not contain a valid role. Using validated default role '%s'.",
-            raw_value,
-            validated_default,
-        )
-    else:
-        logger.debug("Resolved role (direct header) to '%s'.", resolved)
-
-    return resolved
-
-
 # Endpoints
-@router.get("/auth", dependencies=[Depends(allow_public())])
+@router.get("/auth")
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
@@ -555,11 +266,22 @@ def auth(request: Request):
             else "anonymous"
         )
 
-        # parse header and resolve a valid role
-        config_roles_set = set(auth_config.roles.keys())
-        role = resolve_role(request.headers, proxy_config, config_roles_set)
+        role_header = proxy_config.header_map.role
+        role = (
+            request.headers.get(role_header, default=proxy_config.default_role)
+            if role_header
+            else proxy_config.default_role
+        )
+
+        # if comma-separated with "admin", use "admin",
+        # if comma-separated with "viewer", use "viewer",
+        # else use default role
+
+        roles = [r.strip() for r in role.split(proxy_config.separator)] if role else []
+        success_response.headers["remote-role"] = next(
+            (r for r in VALID_ROLES if r in roles), proxy_config.default_role
+        )
 
-        success_response.headers["remote-role"] = role
         return success_response
 
     # now apply authentication
@@ -646,21 +368,15 @@ def auth(request: Request):
         return fail_response
 
 
-@router.get("/profile", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/profile")
 def profile(request: Request):
     username = request.headers.get("remote-user", "anonymous")
     role = request.headers.get("remote-role", "viewer")
 
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-    roles_dict = request.app.frigate_config.auth.roles
-    allowed_cameras = User.get_allowed_cameras(role, roles_dict, all_camera_names)
-
-    return JSONResponse(
-        content={"username": username, "role": role, "allowed_cameras": allowed_cameras}
-    )
+    return JSONResponse(content={"username": username, "role": role})
 
 
-@router.get("/logout", dependencies=[Depends(allow_any_authenticated())])
+@router.get("/logout")
 def logout(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     response = RedirectResponse("/login", status_code=303)
@@ -671,7 +387,7 @@ def logout(request: Request):
 limiter = Limiter(key_func=get_remote_addr)
 
 
-@router.post("/login", dependencies=[Depends(allow_public())])
+@router.post("/login")
 @limiter.limit(limit_value=rateLimiter.get_limit)
 def login(request: Request, body: AppPostLoginBody):
     JWT_COOKIE_NAME = request.app.frigate_config.auth.cookie_name
@@ -688,23 +404,14 @@ def login(request: Request, body: AppPostLoginBody):
     password_hash = db_user.password_hash
     if verify_password(password, password_hash):
         role = getattr(db_user, "role", "viewer")
-        config_roles_set = set(request.app.frigate_config.auth.roles.keys())
-        if role not in config_roles_set:
-            logger.warning(
-                f"User {db_user.username} has an invalid role {role}, falling back to 'viewer'."
-            )
-            role = "viewer"
+        if role not in VALID_ROLES:
+            role = "viewer"  # Enforce valid roles
         expiration = int(time.time()) + JWT_SESSION_LENGTH
         encoded_jwt = create_encoded_jwt(user, role, expiration, request.app.jwt_token)
         response = Response("", 200)
         set_jwt_cookie(
             response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
         )
-        # Clear admin_first_time_login flag after successful admin login so the
-        # UI stops showing the first-time login documentation link.
-        if role == "admin":
-            request.app.frigate_config.auth.admin_first_time_login = False
-
         return response
     return JSONResponse(content={"message": "Login failed"}, status_code=401)
 
@@ -723,17 +430,11 @@ def create_user(
     body: AppPostUsersBody,
 ):
     HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
-    config_roles = list(request.app.frigate_config.auth.roles.keys())
 
     if not re.match("^[A-Za-z0-9._]+$", body.username):
         return JSONResponse(content={"message": "Invalid username"}, status_code=400)
 
-    if body.role not in config_roles:
-        return JSONResponse(
-            content={"message": f"Role must be one of: {', '.join(config_roles)}"},
-            status_code=400,
-        )
-    role = body.role or "viewer"
+    role = body.role if body.role in VALID_ROLES else "viewer"
     password_hash = hash_password(body.password, iterations=HASH_ITERATIONS)
     User.insert(
         {
@@ -746,21 +447,13 @@ def create_user(
     return JSONResponse(content={"username": body.username})
 
 
-@router.delete("/users/{username}", dependencies=[Depends(require_role(["admin"]))])
-def delete_user(request: Request, username: str):
-    # Prevent deletion of the built-in admin user
-    if username == "admin":
-        return JSONResponse(
-            content={"message": "Cannot delete admin user"}, status_code=403
-        )
-
+@router.delete("/users/{username}")
+def delete_user(username: str):
     User.delete_by_id(username)
     return JSONResponse(content={"success": True})
 
 
-@router.put(
-    "/users/{username}/password", dependencies=[Depends(allow_any_authenticated())]
-)
+@router.put("/users/{username}/password")
 async def update_password(
     request: Request,
     username: str,
@@ -812,52 +505,10 @@ async def update_role(
         return JSONResponse(
             content={"message": "Cannot modify admin user's role"}, status_code=403
         )
-    config_roles = list(request.app.frigate_config.auth.roles.keys())
-    if body.role not in config_roles:
+    if body.role not in VALID_ROLES:
         return JSONResponse(
-            content={"message": f"Role must be one of: {', '.join(config_roles)}"},
-            status_code=400,
+            content={"message": "Role must be 'admin' or 'viewer'"}, status_code=400
         )
 
     User.set_by_id(username, {User.role: body.role})
     return JSONResponse(content={"success": True})
-
-
-async def require_camera_access(
-    camera_name: Optional[str] = None,
-    request: Request = None,
-):
-    """Dependency to enforce camera access based on user role."""
-    if camera_name is None:
-        return  # For lists, filter later
-
-    current_user = await get_current_user(request)
-    if isinstance(current_user, JSONResponse):
-        return current_user
-
-    role = current_user["role"]
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-    roles_dict = request.app.frigate_config.auth.roles
-    allowed_cameras = User.get_allowed_cameras(role, roles_dict, all_camera_names)
-
-    # Admin or full access bypasses
-    if role == "admin" or not roles_dict.get(role):
-        return
-
-    if camera_name not in allowed_cameras:
-        raise HTTPException(
-            status_code=403,
-            detail=f"Access denied to camera '{camera_name}'. Allowed: {allowed_cameras}",
-        )
-
-
-async def get_allowed_cameras_for_filter(request: Request):
-    """Dependency to get allowed_cameras for filtering lists."""
-    current_user = await get_current_user(request)
-    if isinstance(current_user, JSONResponse):
-        return []  # Unauthorized: no cameras
-
-    role = current_user["role"]
-    all_camera_names = set(request.app.frigate_config.cameras.keys())
-    roles_dict = request.app.frigate_config.auth.roles
-    return User.get_allowed_cameras(role, roles_dict, all_camera_names)

frigate/api/classification.py

@@ -3,9 +3,7 @@
 import datetime
 import logging
 import os
-import random
 import shutil
-import string
 from typing import Any
 
 import cv2
@@ -16,46 +14,20 @@ from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
 from frigate.api.auth import require_role
-from frigate.api.defs.request.classification_body import (
-    AudioTranscriptionBody,
-    DeleteFaceImagesBody,
-    GenerateObjectExamplesBody,
-    GenerateStateExamplesBody,
-    RenameFaceBody,
-)
-from frigate.api.defs.response.classification_response import (
-    FaceRecognitionResponse,
-    FacesResponse,
-)
-from frigate.api.defs.response.generic_response import GenericResponse
+from frigate.api.defs.request.classification_body import RenameFaceBody
 from frigate.api.defs.tags import Tags
-from frigate.config import FrigateConfig
 from frigate.config.camera import DetectConfig
-from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
+from frigate.const import FACE_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event
-from frigate.util.classification import (
-    collect_object_classification_examples,
-    collect_state_classification_examples,
-    get_dataset_image_count,
-    read_training_metadata,
-)
-from frigate.util.file import get_event_snapshot
+from frigate.util.path import get_event_snapshot
 
 logger = logging.getLogger(__name__)
 
-router = APIRouter(tags=[Tags.classification])
+router = APIRouter(tags=[Tags.events])
 
 
-@router.get(
-    "/faces",
-    response_model=FacesResponse,
-    summary="Get all registered faces",
-    description="""Returns a dictionary mapping face names to lists of image filenames.
-    Each key represents a registered face name, and the value is a list of image
-    files associated with that face. Supported image formats include .webp, .png,
-    .jpg, and .jpeg.""",
-)
+@router.get("/faces")
 def get_faces():
     face_dict: dict[str, list[str]] = {}
 
@@ -79,15 +51,7 @@ def get_faces():
     return JSONResponse(status_code=200, content=face_dict)
 
 
-@router.post(
-    "/faces/reprocess",
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Reprocess a face training image",
-    description="""Reprocesses a face training image to update the prediction.
-    Requires face recognition to be enabled in the configuration. The training file
-    must exist in the faces/train directory. Returns a success response or an error
-    message if face recognition is not enabled or the training file is invalid.""",
-)
+@router.post("/faces/reprocess", dependencies=[Depends(require_role(["admin"]))])
 def reclassify_face(request: Request, body: dict = None):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
@@ -114,32 +78,13 @@ def reclassify_face(request: Request, body: dict = None):
     context: EmbeddingsContext = request.app.embeddings
     response = context.reprocess_face(training_file)
 
-    if not isinstance(response, dict):
-        return JSONResponse(
-            status_code=500,
-            content={
-                "success": False,
-                "message": "Could not process request.",
-            },
-        )
-
     return JSONResponse(
-        status_code=200 if response.get("success", True) else 400,
         content=response,
+        status_code=200,
     )
 
 
-@router.post(
-    "/faces/train/{name}/classify",
-    response_model=GenericResponse,
-    summary="Classify and save a face training image",
-    description="""Adds a training image to a specific face name for face recognition.
-    Accepts either a training file from the train directory or an event_id to extract
-    the face from. The image is saved to the face's directory and the face classifier
-    is cleared to incorporate the new training data. Returns a success message with
-    the new filename or an error if face recognition is not enabled, the file/event
-    is invalid, or the face cannot be extracted.""",
-)
+@router.post("/faces/train/{name}/classify")
 def train_face(request: Request, name: str, body: dict = None):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
@@ -178,7 +123,8 @@ def train_face(request: Request, name: str, body: dict = None):
     new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
     new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
 
-    os.makedirs(new_file_folder, exist_ok=True)
+    if not os.path.exists(new_file_folder):
+        os.mkdir(new_file_folder)
 
     if training_file_name:
         shutil.move(training_file, os.path.join(new_file_folder, new_name))
@@ -242,16 +188,7 @@ def train_face(request: Request, name: str, body: dict = None):
     )
 
 
-@router.post(
-    "/faces/{name}/create",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Create a new face name",
-    description="""Creates a new folder for a face name in the faces directory.
-    This is used to organize face training images. The face name is sanitized and
-    spaces are replaced with underscores. Returns a success message or an error if
-    face recognition is not enabled.""",
-)
+@router.post("/faces/{name}/create", dependencies=[Depends(require_role(["admin"]))])
 async def create_face(request: Request, name: str):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
@@ -268,16 +205,7 @@ async def create_face(request: Request, name: str):
     )
 
 
-@router.post(
-    "/faces/{name}/register",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Register a face image",
-    description="""Registers a face image for a specific face name by uploading an image file.
-    The uploaded image is processed and added to the face recognition system. Returns a
-    success response with details about the registration, or an error if face recognition
-    is not enabled or the image cannot be processed.""",
-)
+@router.post("/faces/{name}/register", dependencies=[Depends(require_role(["admin"]))])
 async def register_face(request: Request, name: str, file: UploadFile):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
@@ -286,7 +214,7 @@ async def register_face(request: Request, name: str, file: UploadFile):
         )
 
     context: EmbeddingsContext = request.app.embeddings
-    result = None if context is None else context.register_face(name, await file.read())
+    result = context.register_face(name, await file.read())
 
     if not isinstance(result, dict):
         return JSONResponse(
@@ -303,14 +231,7 @@ async def register_face(request: Request, name: str, file: UploadFile):
     )
 
 
-@router.post(
-    "/faces/recognize",
-    response_model=FaceRecognitionResponse,
-    summary="Recognize a face from an uploaded image",
-    description="""Recognizes a face from an uploaded image file by comparing it against
-    registered faces in the system. Returns the recognized face name and confidence score,
-    or an error if face recognition is not enabled or the image cannot be processed.""",
-)
+@router.post("/faces/recognize")
 async def recognize_face(request: Request, file: UploadFile):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
@@ -336,38 +257,28 @@ async def recognize_face(request: Request, file: UploadFile):
     )
 
 
-@router.post(
-    "/faces/{name}/delete",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete face images",
-    description="""Deletes specific face images for a given face name. The image IDs must belong
-    to the specified face folder. To delete an entire face folder, all image IDs in that
-    folder must be sent. Returns a success message or an error if face recognition is not enabled.""",
-)
-def deregister_faces(request: Request, name: str, body: DeleteFaceImagesBody):
+@router.post("/faces/{name}/delete", dependencies=[Depends(require_role(["admin"]))])
+def deregister_faces(request: Request, name: str, body: dict = None):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
             status_code=400,
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    json: dict[str, Any] = body or {}
+    list_of_ids = json.get("ids", "")
+
     context: EmbeddingsContext = request.app.embeddings
-    context.delete_face_ids(name, map(lambda file: sanitize_filename(file), body.ids))
+    context.delete_face_ids(
+        name, map(lambda file: sanitize_filename(file), list_of_ids)
+    )
     return JSONResponse(
         content=({"success": True, "message": "Successfully deleted faces."}),
         status_code=200,
     )
 
 
-@router.put(
-    "/faces/{old_name}/rename",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Rename a face name",
-    description="""Renames a face name in the system. The old name must exist and the new
-    name must be valid. Returns a success message or an error if face recognition is not enabled.""",
-)
+@router.put("/faces/{old_name}/rename", dependencies=[Depends(require_role(["admin"]))])
 def rename_face(request: Request, old_name: str, body: RenameFaceBody):
     if not request.app.frigate_config.face_recognition.enabled:
         return JSONResponse(
@@ -396,14 +307,7 @@ def rename_face(request: Request, old_name: str, body: RenameFaceBody):
         )
 
 
-@router.put(
-    "/lpr/reprocess",
-    summary="Reprocess a license plate",
-    description="""Reprocesses a license plate image to update the plate.
-    Requires license plate recognition to be enabled in the configuration. The event_id
-    must exist in the database. Returns a success message or an error if license plate
-    recognition is not enabled or the event_id is invalid.""",
-)
+@router.put("/lpr/reprocess")
 def reprocess_license_plate(request: Request, event_id: str):
     if not request.app.frigate_config.lpr.enabled:
         message = "License plate recognition is not enabled."
@@ -436,14 +340,7 @@ def reprocess_license_plate(request: Request, event_id: str):
     )
 
 
-@router.put(
-    "/reindex",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Reindex embeddings",
-    description="""Reindexes the embeddings for all tracked objects.
-    Requires semantic search to be enabled in the configuration. Returns a success message or an error if semantic search is not enabled.""",
-)
+@router.put("/reindex", dependencies=[Depends(require_role(["admin"]))])
 def reindex_embeddings(request: Request):
     if not request.app.frigate_config.semantic_search.enabled:
         message = (
@@ -487,543 +384,3 @@ def reindex_embeddings(request: Request):
             },
             status_code=500,
         )
-
-
-@router.put(
-    "/audio/transcribe",
-    response_model=GenericResponse,
-    summary="Transcribe audio",
-    description="""Transcribes audio from a specific event.
-    Requires audio transcription to be enabled in the configuration. The event_id
-    must exist in the database. Returns a success message or an error if audio transcription is not enabled or the event_id is invalid.""",
-)
-def transcribe_audio(request: Request, body: AudioTranscriptionBody):
-    event_id = body.event_id
-
-    try:
-        event = Event.get(Event.id == event_id)
-    except DoesNotExist:
-        message = f"Event {event_id} not found"
-        logger.error(message)
-        return JSONResponse(
-            content=({"success": False, "message": message}), status_code=404
-        )
-
-    if not request.app.frigate_config.cameras[event.camera].audio_transcription.enabled:
-        message = f"Audio transcription is not enabled for {event.camera}."
-        logger.error(message)
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": message,
-                }
-            ),
-            status_code=400,
-        )
-
-    context: EmbeddingsContext = request.app.embeddings
-    response = context.transcribe_audio(model_to_dict(event))
-
-    if response == "started":
-        return JSONResponse(
-            content={
-                "success": True,
-                "message": "Audio transcription has started.",
-            },
-            status_code=202,  # 202 Accepted
-        )
-    elif response == "in_progress":
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "Audio transcription for a speech event is currently in progress. Try again later.",
-            },
-            status_code=409,  # 409 Conflict
-        )
-    else:
-        logger.debug(f"Failed to transcribe audio, response: {response}")
-        return JSONResponse(
-            content={
-                "success": False,
-                "message": "Failed to transcribe audio.",
-            },
-            status_code=500,
-        )
-
-
-# custom classification training
-
-
-@router.get(
-    "/classification/{name}/dataset",
-    summary="Get classification dataset",
-    description="""Gets the dataset for a specific classification model.
-    The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
-)
-def get_classification_dataset(name: str):
-    dataset_dict: dict[str, list[str]] = {}
-
-    dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "dataset")
-
-    if not os.path.exists(dataset_dir):
-        return JSONResponse(
-            status_code=200, content={"categories": {}, "training_metadata": None}
-        )
-
-    for category_name in os.listdir(dataset_dir):
-        category_dir = os.path.join(dataset_dir, category_name)
-
-        if not os.path.isdir(category_dir):
-            continue
-
-        dataset_dict[category_name] = []
-
-        for file in filter(
-            lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
-            os.listdir(category_dir),
-        ):
-            dataset_dict[category_name].append(file)
-
-    # Get training metadata
-    metadata = read_training_metadata(sanitize_filename(name))
-    current_image_count = get_dataset_image_count(sanitize_filename(name))
-
-    if metadata is None:
-        training_metadata = {
-            "has_trained": False,
-            "last_training_date": None,
-            "last_training_image_count": 0,
-            "current_image_count": current_image_count,
-            "new_images_count": current_image_count,
-            "dataset_changed": current_image_count > 0,
-        }
-    else:
-        last_training_count = metadata.get("last_training_image_count", 0)
-        # Dataset has changed if count is different (either added or deleted images)
-        dataset_changed = current_image_count != last_training_count
-        # Only show positive count for new images (ignore deletions in the count display)
-        new_images_count = max(0, current_image_count - last_training_count)
-        training_metadata = {
-            "has_trained": True,
-            "last_training_date": metadata.get("last_training_date"),
-            "last_training_image_count": last_training_count,
-            "current_image_count": current_image_count,
-            "new_images_count": new_images_count,
-            "dataset_changed": dataset_changed,
-        }
-
-    return JSONResponse(
-        status_code=200,
-        content={
-            "categories": dataset_dict,
-            "training_metadata": training_metadata,
-        },
-    )
-
-
-@router.get(
-    "/classification/{name}/train",
-    summary="Get classification train images",
-    description="""Gets the train images for a specific classification model.
-    The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
-)
-def get_classification_images(name: str):
-    train_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
-
-    if not os.path.exists(train_dir):
-        return JSONResponse(status_code=200, content=[])
-
-    return JSONResponse(
-        status_code=200,
-        content=list(
-            filter(
-                lambda f: (f.lower().endswith((".webp", ".png", ".jpg", ".jpeg"))),
-                os.listdir(train_dir),
-            )
-        ),
-    )
-
-
-@router.post(
-    "/classification/{name}/train",
-    response_model=GenericResponse,
-    summary="Train a classification model",
-    description="""Trains a specific classification model.
-    The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
-)
-async def train_configured_model(request: Request, name: str):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
-
-    context: EmbeddingsContext = request.app.embeddings
-    context.start_classification_training(name)
-    return JSONResponse(
-        content={"success": True, "message": "Started classification model training."},
-        status_code=200,
-    )
-
-
-@router.post(
-    "/classification/{name}/dataset/{category}/delete",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete classification dataset images",
-    description="""Deletes specific dataset images for a given classification model and category.
-    The image IDs must belong to the specified category. Returns a success message or an error if the name or category is invalid.""",
-)
-def delete_classification_dataset_images(
-    request: Request, name: str, category: str, body: dict = None
-):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
-
-    json: dict[str, Any] = body or {}
-    list_of_ids = json.get("ids", "")
-    folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
-
-    for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
-
-        if os.path.isfile(file_path):
-            os.unlink(file_path)
-
-    if os.path.exists(folder) and not os.listdir(folder):
-        os.rmdir(folder)
-
-    return JSONResponse(
-        content=({"success": True, "message": "Successfully deleted images."}),
-        status_code=200,
-    )
-
-
-@router.put(
-    "/classification/{name}/dataset/{old_category}/rename",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Rename a classification category",
-    description="""Renames a classification category for a given classification model.
-    The old category must exist and the new name must be valid. Returns a success message or an error if the name is invalid.""",
-)
-def rename_classification_category(
-    request: Request, name: str, old_category: str, body: dict = None
-):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
-
-    json: dict[str, Any] = body or {}
-    new_category = sanitize_filename(json.get("new_category", ""))
-
-    if not new_category:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": "New category name is required.",
-                }
-            ),
-            status_code=400,
-        )
-
-    old_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(old_category)
-    )
-    new_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", new_category
-    )
-
-    if not os.path.exists(old_folder):
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"Category {old_category} does not exist.",
-                }
-            ),
-            status_code=404,
-        )
-
-    if os.path.exists(new_folder):
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"Category {new_category} already exists.",
-                }
-            ),
-            status_code=400,
-        )
-
-    try:
-        os.rename(old_folder, new_folder)
-        return JSONResponse(
-            content=(
-                {
-                    "success": True,
-                    "message": f"Successfully renamed category to {new_category}.",
-                }
-            ),
-            status_code=200,
-        )
-    except Exception as e:
-        logger.error(f"Error renaming category: {e}")
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": "Failed to rename category",
-                }
-            ),
-            status_code=500,
-        )
-
-
-@router.post(
-    "/classification/{name}/dataset/categorize",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Categorize a classification image",
-    description="""Categorizes a specific classification image for a given classification model and category.
-    The image must exist in the specified category. Returns a success message or an error if the name or category is invalid.""",
-)
-def categorize_classification_image(request: Request, name: str, body: dict = None):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
-
-    json: dict[str, Any] = body or {}
-    category = sanitize_filename(json.get("category", ""))
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "train", training_file_name
-    )
-
-    if training_file_name and not os.path.isfile(training_file):
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"Invalid filename or no file exists: {training_file_name}",
-                }
-            ),
-            status_code=404,
-        )
-
-    random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
-    timestamp = datetime.datetime.now().timestamp()
-    new_name = f"{category}-{timestamp}-{random_id}.png"
-    new_file_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", category
-    )
-
-    os.makedirs(new_file_folder, exist_ok=True)
-
-    # use opencv because webp images can not be used to train
-    img = cv2.imread(training_file)
-    cv2.imwrite(os.path.join(new_file_folder, new_name), img)
-    os.unlink(training_file)
-
-    return JSONResponse(
-        content=({"success": True, "message": "Successfully categorized image."}),
-        status_code=200,
-    )
-
-
-@router.post(
-    "/classification/{name}/dataset/{category}/create",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Create an empty classification category folder",
-    description="""Creates an empty folder for a classification category.
-    This is used to create folders for categories that don't have images yet.
-    Returns a success message or an error if the name is invalid.""",
-)
-def create_classification_category(request: Request, name: str, category: str):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
-
-    category_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
-
-    os.makedirs(category_folder, exist_ok=True)
-
-    return JSONResponse(
-        content=(
-            {
-                "success": True,
-                "message": f"Successfully created category folder: {category}",
-            }
-        ),
-        status_code=200,
-    )
-
-
-@router.post(
-    "/classification/{name}/train/delete",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete classification train images",
-    description="""Deletes specific train images for a given classification model.
-    The image IDs must belong to the specified train folder. Returns a success message or an error if the name is invalid.""",
-)
-def delete_classification_train_images(request: Request, name: str, body: dict = None):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
-
-    json: dict[str, Any] = body or {}
-    list_of_ids = json.get("ids", "")
-    folder = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
-
-    for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
-
-        if os.path.isfile(file_path):
-            os.unlink(file_path)
-
-    return JSONResponse(
-        content=({"success": True, "message": "Successfully deleted images."}),
-        status_code=200,
-    )
-
-
-@router.post(
-    "/classification/generate_examples/state",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Generate state classification examples",
-)
-async def generate_state_examples(request: Request, body: GenerateStateExamplesBody):
-    """Generate examples for state classification."""
-    model_name = sanitize_filename(body.model_name)
-    cameras_normalized = {
-        camera_name: tuple(crop)
-        for camera_name, crop in body.cameras.items()
-        if camera_name in request.app.frigate_config.cameras
-    }
-
-    collect_state_classification_examples(model_name, cameras_normalized)
-
-    return JSONResponse(
-        content={"success": True, "message": "Example generation completed"},
-        status_code=200,
-    )
-
-
-@router.post(
-    "/classification/generate_examples/object",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Generate object classification examples",
-)
-async def generate_object_examples(request: Request, body: GenerateObjectExamplesBody):
-    """Generate examples for object classification."""
-    model_name = sanitize_filename(body.model_name)
-    collect_object_classification_examples(model_name, body.label)
-
-    return JSONResponse(
-        content={"success": True, "message": "Example generation completed"},
-        status_code=200,
-    )
-
-
-@router.delete(
-    "/classification/{name}",
-    response_model=GenericResponse,
-    dependencies=[Depends(require_role(["admin"]))],
-    summary="Delete a classification model",
-    description="""Deletes a specific classification model and all its associated data.
-    Works even if the model is not in the config (e.g., partially created during wizard).
-    Returns a success message.""",
-)
-def delete_classification_model(request: Request, name: str):
-    sanitized_name = sanitize_filename(name)
-
-    # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
-    if os.path.exists(data_dir):
-        try:
-            shutil.rmtree(data_dir)
-            logger.info(f"Deleted classification data directory for {name}")
-        except Exception as e:
-            logger.debug(f"Failed to delete data directory for {name}: {e}")
-
-    # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
-    if os.path.exists(model_dir):
-        try:
-            shutil.rmtree(model_dir)
-            logger.info(f"Deleted classification model directory for {name}")
-        except Exception as e:
-            logger.debug(f"Failed to delete model directory for {name}: {e}")
-
-    return JSONResponse(
-        content=(
-            {
-                "success": True,
-                "message": f"Successfully deleted classification model {name}.",
-            }
-        ),
-        status_code=200,
-    )

frigate/api/defs/query/events_query_parameters.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 DEFAULT_TIME_RANGE = "00:00,24:00"
 
@@ -21,14 +21,7 @@ class EventsQueryParams(BaseModel):
     has_clip: Optional[int] = None
     has_snapshot: Optional[int] = None
     in_progress: Optional[int] = None
-    include_thumbnails: Optional[int] = Field(
-        1,
-        description=(
-            "Deprecated. Thumbnail data is no longer included in the response. "
-            "Use the /api/events/:event_id/thumbnail.:extension endpoint instead."
-        ),
-        deprecated=True,
-    )
+    include_thumbnails: Optional[int] = 1
     favorites: Optional[int] = None
     min_score: Optional[float] = None
     max_score: Optional[float] = None
@@ -47,14 +40,7 @@ class EventsSearchQueryParams(BaseModel):
     query: Optional[str] = None
     event_id: Optional[str] = None
     search_type: Optional[str] = "thumbnail"
-    include_thumbnails: Optional[int] = Field(
-        1,
-        description=(
-            "Deprecated. Thumbnail data is no longer included in the response. "
-            "Use the /api/events/:event_id/thumbnail.:extension endpoint instead."
-        ),
-        deprecated=True,
-    )
+    include_thumbnails: Optional[int] = 1
     limit: Optional[int] = 50
     cameras: Optional[str] = "all"
     labels: Optional[str] = "all"

frigate/api/defs/query/media_query_parameters.py

@@ -1,8 +1,7 @@
 from enum import Enum
-from typing import Optional, Union
+from typing import Optional
 
 from pydantic import BaseModel
-from pydantic.json_schema import SkipJsonSchema
 
 
 class Extension(str, Enum):
@@ -11,11 +10,6 @@ class Extension(str, Enum):
     jpg = "jpg"
     jpeg = "jpeg"
 
-    def get_mime_type(self) -> str:
-        if self in (Extension.jpg, Extension.jpeg):
-            return "image/jpeg"
-        return f"image/{self.value}"
-
 
 class MediaLatestFrameQueryParams(BaseModel):
     bbox: Optional[int] = None
@@ -23,7 +17,6 @@ class MediaLatestFrameQueryParams(BaseModel):
     zones: Optional[int] = None
     mask: Optional[int] = None
     motion: Optional[int] = None
-    paths: Optional[int] = None
     regions: Optional[int] = None
     quality: Optional[int] = 70
     height: Optional[int] = None
@@ -53,10 +46,3 @@ class MediaMjpegFeedQueryParams(BaseModel):
 class MediaRecordingsSummaryQueryParams(BaseModel):
     timezone: str = "utc"
     cameras: Optional[str] = "all"
-
-
-class MediaRecordingsAvailabilityQueryParams(BaseModel):
-    cameras: str = "all"
-    before: Union[float, SkipJsonSchema[None]] = None
-    after: Union[float, SkipJsonSchema[None]] = None
-    scale: int = 30

frigate/api/defs/query/regenerate_query_parameters.py

@@ -1,13 +1,9 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 from frigate.events.types import RegenerateDescriptionEnum
 
 
 class RegenerateQueryParameters(BaseModel):
     source: Optional[RegenerateDescriptionEnum] = RegenerateDescriptionEnum.thumbnails
-    force: Optional[bool] = Field(
-        default=False,
-        description="Force (re)generating the description even if GenAI is disabled for this camera.",
-    )

frigate/api/defs/request/app_body.py

@@ -1,12 +1,10 @@
-from typing import Any, Dict, Optional
+from typing import Optional
 
 from pydantic import BaseModel
 
 
 class AppConfigSetBody(BaseModel):
     requires_restart: int = 1
-    update_topic: str | None = None
-    config_data: Optional[Dict[str, Any]] = None
 
 
 class AppPutPasswordBody(BaseModel):

frigate/api/defs/request/classification_body.py

@@ -1,31 +1,5 @@
-from typing import Dict, List, Tuple
-
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
 
 
 class RenameFaceBody(BaseModel):
-    new_name: str = Field(description="New name for the face")
-
-
-class AudioTranscriptionBody(BaseModel):
-    event_id: str = Field(description="ID of the event to transcribe audio for")
-
-
-class DeleteFaceImagesBody(BaseModel):
-    ids: List[str] = Field(
-        description="List of image filenames to delete from the face folder"
-    )
-
-
-class GenerateStateExamplesBody(BaseModel):
-    model_name: str = Field(description="Name of the classification model")
-    cameras: Dict[str, Tuple[float, float, float, float]] = Field(
-        description="Dictionary mapping camera names to normalized crop coordinates in [x1, y1, x2, y2] format (values 0-1)"
-    )
-
-
-class GenerateObjectExamplesBody(BaseModel):
-    model_name: str = Field(description="Name of the classification model")
-    label: str = Field(
-        description="Object label to collect examples for (e.g., 'person', 'car')"
-    )
+    new_name: str

frigate/api/defs/request/events_body.py

@@ -2,8 +2,6 @@ from typing import List, Optional, Union
 
 from pydantic import BaseModel, Field
 
-from frigate.config.classification import TriggerType
-
 
 class EventsSubLabelBody(BaseModel):
     subLabel: str = Field(title="Sub label", max_length=100)
@@ -29,6 +27,7 @@ class EventsDescriptionBody(BaseModel):
 
 
 class EventsCreateBody(BaseModel):
+    source_type: Optional[str] = "api"
     sub_label: Optional[str] = None
     score: Optional[float] = 0
     duration: Optional[int] = 30
@@ -46,9 +45,3 @@ class EventsDeleteBody(BaseModel):
 
 class SubmitPlusBody(BaseModel):
     include_annotation: int = Field(default=1)
-
-
-class TriggerEmbeddingBody(BaseModel):
-    type: TriggerType
-    data: str
-    threshold: float = Field(default=0.5, ge=0.0, le=1.0)

frigate/api/defs/request/review_body.py

@@ -4,5 +4,3 @@ from pydantic import BaseModel, conlist, constr
 class ReviewModifyMultipleBody(BaseModel):
     # List of string with at least one element and each element with at least one char
     ids: conlist(constr(min_length=1), min_length=1)
-    # Whether to mark items as reviewed (True) or unreviewed (False)
-    reviewed: bool = True