Update HIKVISION camera link in hardware documentation (#21256 )

docs: update OpenVINO D-FINE configuration default device (#21231 )
* docs: remove OpenVINO D-FINE configuration device * docs: change D-FINE model detectors default device
2025-12-26 15:08:15 -05:00 · 2025-12-12 14:25:22 -06:00 · 2025-12-11 06:31:52 -07:00 · 2025-12-10 10:33:10 -07:00 · 2025-12-07 07:38:41 -07:00 · 2025-12-06 09:16:23 -06:00
41 changed files with 579 additions and 267 deletions
--- a/2
+++ b/2
@@ -1,7 +1,7 @@
 default_target: local

 COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
-VERSION = 0.16.1
+VERSION = 0.16.3
 IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
 GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
 BOARDS= #Initialized empty
--- a/README.md
+++ b/README.md
@@ -12,7 +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 such as a [Google Coral](https://coral.ai/products/) or [Hailo](https://hailo.ai/) is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
+Use of a GPU, Integrated GPU, or AI accelerator such as a [Hailo](https://hailo.ai/) is highly recommended. Dedicated hardware 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
--- a/docker/main/build_pysqlite3.sh
+++ b/docker/main/build_pysqlite3.sh
@@ -5,21 +5,27 @@ set -euxo pipefail
 SQLITE3_VERSION="3.46.1"
 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
 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
-  
+
  cd ../
 fi

--- a/docker/main/requirements-wheels.txt
+++ b/docker/main/requirements-wheels.txt
@@ -8,6 +8,7 @@ fastapi == 0.115.*
 uvicorn == 0.30.*
 slowapi == 0.1.*
 joserfc == 1.0.*
+cryptography == 44.0.*
 pathvalidate == 3.2.*
 markupsafe == 3.0.*
 python-multipart == 0.0.12
--- a/docker/main/requirements.txt
+++ b/docker/main/requirements.txt
@@ -1,2 +1 @@
 scikit-build == 0.18.*
-nvidia-pyindex
--- a/docker/tensorrt/Dockerfile.arm64
+++ b/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 -r /requirements-tensorrt-models.txt
+    pip3 wheel --wheel-dir=/trt-model-wheels --no-deps -r /requirements-tensorrt-models.txt

 FROM wget AS jetson-ffmpeg
 ARG DEBIAN_FRONTEND
@@ -145,7 +145,8 @@ COPY --from=trt-wheels /etc/TENSORRT_VER /etc/TENSORRT_VER
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
    --mount=type=bind,from=trt-model-wheels,source=/trt-model-wheels,target=/deps/trt-model-wheels \
    pip3 uninstall -y onnxruntime \
-    && pip3 install -U /deps/trt-wheels/*.whl /deps/trt-model-wheels/*.whl \
+    && pip3 install -U /deps/trt-wheels/*.whl \
+    && pip3 install -U /deps/trt-model-wheels/*.whl \
    && ldconfig

 WORKDIR /opt/frigate/
--- a/docs/docs/configuration/camera_specific.md
+++ b/docs/docs/configuration/camera_specific.md
@@ -144,7 +144,14 @@ WEB Digest Algorithm  - MD5

 ### Reolink Cameras

-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.
+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                              |                                                                         |
+
 Frigate works much better with newer reolink cameras that are setup with the below options:

 If available, recommended settings are:
@@ -157,19 +164,35 @@ According to [this discussion](https://github.com/blakeblackshear/frigate/issues
 Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
 The setup of main stream can be also done via RTSP, but isn't always reliable on all hardware versions. The example configuration is working with the oldest HW version RLN16-410 device with multiple types of cameras.

-:::warning
+<details>
+  <summary>Example Config</summary>

-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.
+:::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).

 :::

 ```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"
@@ -200,25 +223,16 @@ cameras:
          roles:
            - detect
 ```
-
-#### 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"
-```
+</details>

 ### Unifi Protect Cameras

+:::note 
+
+Unifi G5s cameras and newer need a Unifi Protect server to enable rtsps stream, it's not posible to enable it in standalone mode.
+
+:::
+
 Unifi protect cameras require the rtspx stream to be used with go2rtc.
 To utilize a Unifi protect camera, modify the rtsps link to begin with rtspx.
 Additionally, remove the "?enableSrtp" from the end of the Unifi link.
@@ -244,6 +258,10 @@ ffmpeg:

 TP-Link VIGI cameras need some adjustments to the main stream settings on the camera itself to avoid issues. The stream needs to be configured as `H264` with `Smart Coding` set to `off`. Without these settings you may have problems when trying to watch recorded footage. For example Firefox will stop playback after a few seconds and show the following error message: `The media playback was aborted due to a corruption problem or because the media used features your browser did not support.`.

+### Wyze Wireless Cameras
+
+Some community members have found better performance on Wyze cameras by using an alternative firmware known as [Thingino](https://thingino.com/).
+
 ## USB Cameras (aka Webcams)

 To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
@@ -259,7 +277,7 @@ 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:
@@ -276,5 +294,3 @@ cameras:
      width: 1024
      height: 576
 ```
-
-
--- a/docs/docs/configuration/cameras.md
+++ b/docs/docs/configuration/cameras.md
@@ -98,6 +98,7 @@ This list of working and non-working PTZ cameras is based on user feedback.
 | Amcrest IP4M-S2112EW-AI      |      ✅      |      ❌      | FOV relative movement not supported.                                                                                                            |
 | Amcrest IP5M-1190EW          |      ✅      |      ❌      | ONVIF Port: 80. FOV relative movement not supported.                                                                                            |
 | Annke CZ504                  |      ✅      |      ✅      | Annke support provide specific firmware ([V5.7.1 build 250227](https://github.com/pierrepinon/annke_cz504/raw/refs/heads/main/digicap_V5-7-1_build_250227.dav)) to fix issue with ONVIF "TranslationSpaceFov" |
+| Axis Q-6155E                 |      ✅      |      ❌      | ONVIF service port: 80; Camera does not support MoveStatus.
 | Ctronics PTZ                 |      ✅      |      ❌      |                                                                                                                                                 |
 | Dahua                        |      ✅      |      ✅      | Some low-end Dahuas (lite series, among others) have been reported to not support autotracking                                                  |
 | Dahua DH-SD2A500HB           |      ✅      |      ❌      |                                                                                                                                                 |
@@ -107,10 +108,7 @@ This list of working and non-working PTZ cameras is based on user feedback.
 | 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         |      ✅      |      ❌      |                                                                                                                                                 |
+| 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                                                                                                 |
--- a/docs/docs/configuration/face_recognition.md
+++ b/docs/docs/configuration/face_recognition.md
@@ -158,6 +158,8 @@ 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.
--- a/docs/docs/configuration/genai.md
+++ b/docs/docs/configuration/genai.md
@@ -18,10 +18,10 @@ genai:
  enabled: True
  provider: gemini
  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
+  model: gemini-2.0-flash

 cameras:
-  front_camera: 
+  front_camera:
    genai:
      enabled: True # <- enable GenAI for your front camera
      use_snapshot: True
@@ -30,7 +30,7 @@ cameras:
      required_zones:
        - steps
  indoor_camera:
-    genai: 
+    genai:
      enabled: False # <- disable GenAI for your indoor camera
 ```

@@ -78,7 +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). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).

 ### Get API Key

@@ -96,7 +96,7 @@ genai:
  enabled: True
  provider: gemini
  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-1.5-flash
+  model: gemini-2.0-flash
 ```

 :::note
@@ -111,7 +111,7 @@ OpenAI does not have a free tier for their API. With the release of gpt-4o, pric

 ### 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`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).

 ### Get API Key

@@ -139,11 +139,11 @@ 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). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`.
+You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).

 ### Create Resource and Get API Key

-To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key 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.
+To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).

 ### Configuration

@@ -151,7 +151,8 @@ To start using Azure OpenAI, you must first [create a resource](https://learn.mi
 genai:
  enabled: True
  provider: azure_openai
-  base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview
+  base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
+  model: gpt-5-mini
  api_key: "{FRIGATE_OPENAI_API_KEY}"
 ```

@@ -202,7 +203,7 @@ genai:
    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 overriden 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:
--- a/docs/docs/configuration/license_plate_recognition.md
+++ b/docs/docs/configuration/license_plate_recognition.md
@@ -30,8 +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 models are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
-
+License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
 ## Configuration

 License plate recognition is disabled by default. Enable it in your config file:
--- a/docs/docs/configuration/live.md
+++ b/docs/docs/configuration/live.md
@@ -15,7 +15,7 @@ The jsmpeg live view will use more browser and client GPU resources. Using go2rt
 | ------ | ------------------------------------- | ---------- | ---------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | jsmpeg | same as `detect -> fps`, capped at 10 | 720p       | no                           | no              | Resolution is configurable, but go2rtc is recommended if you want higher resolutions and better frame rates. jsmpeg is Frigate's default without go2rtc configured. |
 | mse    | native                                | native     | yes (depends on audio codec) | yes             | iPhone requires iOS 17.1+, Firefox is h.264 only. This is Frigate's default when go2rtc is configured.                                                              |
-| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration, doesn't support h.265. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |
+| webrtc | native                                | native     | yes (depends on audio codec) | yes             | Requires extra configuration. Frigate attempts to use WebRTC when MSE fails or when using a camera's two-way talk feature.                   |

 ### Camera Settings Recommendations

@@ -127,7 +127,8 @@ WebRTC works by creating a TCP or UDP connection on port `8555`. However, it req
  ```

 - For access through Tailscale, the Frigate system's Tailscale IP must be added as a WebRTC candidate. Tailscale IPs all start with `100.`, and are reserved within the `100.64.0.0/10` CIDR block.
- Note that WebRTC does not support H.265.
+
+- Note that some browsers may not support H.265 (HEVC). You can check your browser's current version for H.265 compatibility [here](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness). 

 :::tip

@@ -174,7 +175,7 @@ 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-doorbell)
+To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-cameras)

 ### Streaming options on camera group dashboards

@@ -251,3 +252,7 @@ Note that disabling a camera through the config file (`enabled: False`) removes
 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.
--- a/docs/docs/configuration/object_detectors.md
+++ b/docs/docs/configuration/object_detectors.md
@@ -29,6 +29,7 @@ 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**
+
 - [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.

@@ -325,6 +326,12 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv

 :::

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

 ```yaml
@@ -388,7 +395,7 @@ After placing the downloaded onnx model in your config/model_cache folder, you c
 detectors:
  ov:
    type: openvino
-    device: GPU
+    device: CPU

 model:
  model_type: dfine
@@ -424,10 +431,10 @@ When using Docker Compose:
 ```yaml
 services:
  frigate:
---
-devices:
-  - /dev/dri
-  - /dev/kfd
+    ...
+    devices:
+      - /dev/dri
+      - /dev/kfd
 ```

 For reference on recommended settings see [running ROCm/pytorch in Docker](https://rocm.docs.amd.com/projects/install-on-linux/en/develop/how-to/3rd-party/pytorch-install.html#using-docker-with-pytorch-pre-installed).
@@ -455,9 +462,9 @@ When using Docker Compose:
 ```yaml
 services:
  frigate:
-
-environment:
-  HSA_OVERRIDE_GFX_VERSION: "10.0.0"
+    ...
+    environment:
+      HSA_OVERRIDE_GFX_VERSION: "10.0.0"
 ```

 Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.
@@ -533,6 +540,12 @@ 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.

+:::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
@@ -560,6 +573,12 @@ The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv

 :::

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

 ```yaml
@@ -959,40 +978,43 @@ Here are some tips for getting different model types

 ### Downloading D-FINE Model

-To export as ONNX:
+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.

-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:
-
-```
-python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
+```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
 ```

-:::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
+### Downloading RF-DETR Model

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

 ```sh
-docker build . --build-arg MODEL_SIZE=Nano --output . -f- <<'EOF'
+docker build . --build-arg MODEL_SIZE=Nano --rm --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 onnx onnxruntime onnxsim onnx-graphsurgeon
+RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnx==1.19.1 onnxscript
 ARG MODEL_SIZE
-RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export()"
+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
@@ -1030,23 +1052,25 @@ 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` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available sizes are `t`, `s`, `m`, `c`, and `e`).
+YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).

 ```sh
-docker build . --build-arg MODEL_SIZE=t --output . -f- <<'EOF'
+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 onnxruntime onnx-simplifier>=0.4.1
+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-${MODEL_SIZE}.pt --imgsz 320 --simplify --include onnx
+RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
 FROM scratch
 ARG MODEL_SIZE
-COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /
+ARG IMG_SIZE
+COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
 EOF
 ```
--- a/docs/docs/configuration/pwa.md
+++ b/docs/docs/configuration/pwa.md
@@ -11,7 +11,7 @@ This adds features including the ability to deep link directly into the app.

 In order to install Frigate as a PWA, the following requirements must be met:

- Frigate must be accessed via a secure context (localhost, secure https, etc.)
+- Frigate must be accessed via a secure context (localhost, secure https, VPN, etc.)
 - On Android, Firefox, Chrome, Edge, Opera, and Samsung Internet Browser all support installing PWAs.
 - On iOS 16.4 and later, PWAs can be installed from the Share menu in Safari, Chrome, Edge, Firefox, and Orion.

@@ -22,3 +22,7 @@ Installation varies slightly based on the device that is being used:
 - Desktop: Use the install button typically found in right edge of the address bar
 - Android: Use the `Install as App` button in the more options menu for Chrome, and the `Add app to Home screen` button for Firefox
 - iOS: Use the `Add to Homescreen` button in the share menu
+
+## Usage
+
+Once setup, the Frigate app can be used wherever it has access to Frigate. This means it can be setup as local-only, VPN-only, or fully accessible depending on your needs.
--- a/docs/docs/frigate/hardware.md
+++ b/docs/docs/frigate/hardware.md
@@ -18,7 +18,7 @@ Here are some of the cameras I recommend:
 - <a href="https://amzn.to/4fwoNWA" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T549M-ALED-S3</a> (affiliate link)
 - <a href="https://amzn.to/3YXpcMw" target="_blank" rel="nofollow noopener sponsored">Loryta(Dahua) IPC-T54IR-AS</a> (affiliate link)
 - <a href="https://amzn.to/3AvBHoY" target="_blank" rel="nofollow noopener sponsored">Amcrest IP5M-T1179EW-AI-V3</a> (affiliate link)
- <a href="https://amzn.to/4ltOpaC" target="_blank" rel="nofollow noopener sponsored">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)
+- <a href="https://www.bhphotovideo.com/c/product/1705511-REG/hikvision_colorvu_ds_2cd2387g2p_lsu_sl_8mp_network.html" target="_blank" rel="nofollow noopener">HIKVISION DS-2CD2387G2P-LSU/SL ColorVu 8MP Panoramic Turret IP Camera</a> (affiliate link)

 I may earn a small commission for my endorsement, recommendation, testimonial, or link to any products or services from this website.

@@ -36,9 +36,11 @@ If the EQ13 is out of stock, the link below may take you to a suggested alternat

 :::

-| Name                                                                                                          | Coral Inference Speed | Coral Compatibility | Notes                                                                                     |
-| ------------------------------------------------------------------------------------------------------------- | --------------------- | ------------------- | ----------------------------------------------------------------------------------------- |
-| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | 5-10ms                | USB                 | Dual gigabit NICs for easy isolated camera network. Easily handles several 1080p cameras. |
+| Name                                                                                                          | Capabilities                                                               | Notes                                               |
+| ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------- |
+| Beelink EQ13 (<a href="https://amzn.to/4jn2qVr" target="_blank" rel="nofollow noopener sponsored">Amazon</a>) | Can run object detection on several 1080p cameras with low-medium activity | Dual gigabit NICs for easy isolated camera network. |
+| Intel 1120p ([Amazon](https://www.amazon.com/Beelink-i3-1220P-Computer-Display-Gigabit/dp/B0DDCKT9YP)         | Can handle a large number of 1080p cameras with high activity              |                                                     |
+| Intel 125H  ([Amazon](https://www.amazon.com/MINISFORUM-Pro-125H-Barebone-Computer-HDMI2-1/dp/B0FH21FSZM)     | Can handle a significant number of 1080p cameras with high activity        | Includes NPU for more efficient detection in 0.17+  |

 ## Detectors

@@ -99,14 +101,21 @@ 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

+:::warning
+
+The Coral is no longer recommended for new Frigate installations, except in deployments with particularly low power requirements or hardware incapable of utilizing alternative AI accelerators for object detection. Instead, we suggest using one of the numerous other supported object detectors. Frigate will continue to provide support for the Coral TPU for as long as practicably possible given its still one of the most power-efficient devices for executing object detection models.
+
+:::
+
 Frigate supports both the USB and M.2 versions of the Google Coral.

 - The USB version is compatible with the widest variety of hardware and does not require a driver on the host machine. However, it does lack the automatic throttling features of the other versions.
- The PCIe and M.2 versions require installation of a driver on the host. Follow the instructions for your version from https://coral.ai
+- The PCIe and M.2 versions require installation of a driver on the host. https://github.com/jnicolson/gasket-builder should be used.

 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.

@@ -131,17 +140,19 @@ More information is available [in the detector docs](/configuration/object_detec

 Inference speeds vary greatly depending on the CPU or GPU used, some known examples of GPU inference times are below:

-| 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               |                        |                                    |
+| Name           | MobileNetV2 Inference Time | YOLOv9                                            | YOLO-NAS Inference Time   | RF-DETR Inference Time | Notes                              |
+| -------------- | -------------------------- | ------------------------------------------------- | ------------------------- | ---------------------- | ---------------------------------- |
+| Intel HD 530   | 15 - 35 ms                 |                                                   |                           |                        | Can only run one detector instance |
+| Intel HD 620   | 15 - 25 ms                 |                                                   | 320: ~ 35 ms              |                        |                                    |
+| Intel HD 630   | ~ 15 ms                    |                                                   | 320: ~ 30 ms              |                        |                                    |
+| Intel UHD 730  | ~ 10 ms                    |                                                   | 320: ~ 19 ms 640: ~ 54 ms |                        |                                    |
+| Intel UHD 770  | ~ 15 ms                    | t-320: ~ 16 ms s-320: ~ 20 ms s-640: ~ 40 ms      | 320: ~ 20 ms 640: ~ 46 ms |                        |                                    |
+| Intel N100     | ~ 15 ms                    | s-320: 30 ms                                      | 320: ~ 25 ms              |                        | Can only run one detector instance |
+| Intel N150     | ~ 15 ms                    | t-320: 16 ms s-320: 24 ms                         |                           |                        |                                    |
+| Intel Iris XE  | ~ 10 ms                    | s-320: 12 ms s-640: 30 ms                         | 320: ~ 18 ms 640: ~ 50 ms |                        |                                    |
+| Intel Arc A310 | ~ 5  ms                    | t-320: 7 ms t-640: 11 ms s-320: 8 ms s-640: 15 ms | 320: ~ 8 ms 640: ~ 14 ms  |                        |                                    |
+| Intel Arc A380 | ~ 6 ms                     |                                                   | 320: ~ 10 ms 640: ~ 22 ms | 336: 20 ms 448: 27 ms  |                                    |
+| Intel Arc A750 | ~ 4 ms                     |                                                   | 320: ~ 8 ms               |                        |                                    |

 ### TensorRT - Nvidia GPU

@@ -166,12 +177,13 @@ There are improved capabilities in newer GPU architectures that TensorRT can ben
 Inference speeds will vary greatly depending on the GPU and the model used.
 `tiny` variants are faster than the equivalent non-tiny model, some known examples are below:

-| Name            | YOLOv9 Inference Time | YOLO-NAS Inference Time   | RF-DETR Inference Time |
-| --------------- | --------------------- | ------------------------- | ---------------------- |
-| RTX 3050        | t-320: 15 ms          | 320: ~ 10 ms 640: ~ 16 ms | Nano-320: ~ 12 ms      |
-| RTX 3070        | t-320: 11 ms          | 320: ~ 8 ms 640: ~ 14 ms  | Nano-320: ~ 9 ms       |
-| RTX A4000       |                       | 320: ~ 15 ms              |                        |
-| Tesla P40       |                       | 320: ~ 105 ms             |                        |
+| Name            | YOLOv9 Inference Time     | YOLO-NAS Inference Time   | RF-DETR Inference Time |
+| --------------- | ------------------------- | ------------------------- | ---------------------- |
+| GTX 1070        | s-320: 16 ms              | 320: 14 ms                |                        |
+| RTX 3050        | t-320: 15 ms s-320: 17 ms | 320: ~ 10 ms 640: ~ 16 ms | Nano-320: ~ 12 ms      |
+| RTX 3070        | t-320: 11 ms s-320: 13 ms | 320: ~ 8 ms 640: ~ 14 ms  | Nano-320: ~ 9 ms       |
+| RTX A4000       |                           | 320: ~ 15 ms              |                        |
+| Tesla P40       |                           | 320: ~ 105 ms             |                        |

 ### ROCm - AMD GPU

@@ -179,7 +191,7 @@ With the [rocm](../configuration/object_detectors.md#amdrocm-gpu-detector) detec

 | Name      | YOLOv9 Inference Time | YOLO-NAS Inference Time   |
 | --------- | --------------------- | ------------------------- |
-| AMD 780M  | ~ 14 ms               | 320: ~ 25 ms 640: ~ 50 ms |
+| AMD 780M  | 320: ~ 14 ms          | 320: ~ 25 ms 640: ~ 50 ms |
 | AMD 8700G |                       | 320: ~ 20 ms 640: ~ 40 ms |

 ## Community Supported Detectors
--- a/docs/docs/frigate/installation.md
+++ b/docs/docs/frigate/installation.md
@@ -94,6 +94,10 @@ $ python -c 'print("{:.2f}MB".format(((1280 * 720 * 1.5 * 20 + 270480) / 1048576

 The shm size cannot be set per container for Home Assistant add-ons. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.

+## Extra Steps for Specific Hardware
+
+The following sections contain additional setup steps that are only required if you are using specific hardware. If you are not using any of these hardware types, you can skip to the [Docker](#docker) installation section.
+
 ### Raspberry Pi 3/4

 By default, the Raspberry Pi limits the amount of memory available to the GPU. In order to use ffmpeg hardware acceleration, you must increase the available memory by setting `gpu_mem` to the maximum recommended value in `config.txt` as described in the [official docs](https://www.raspberrypi.org/documentation/computers/config_txt.html#memory-options).
@@ -106,14 +110,107 @@ The Hailo-8 and Hailo-8L AI accelerators are available in both M.2 and HAT form

 #### Installation

-For Raspberry Pi 5 users with the AI Kit, installation is straightforward. Simply follow this [guide](https://www.raspberrypi.com/documentation/accessories/ai-kit.html#ai-kit-installation) to install the driver and software.
+:::warning

-For other installations, follow these steps for installation:
+The Raspberry Pi kernel includes an older version of the Hailo driver that is incompatible with Frigate. You **must** follow the installation steps below to install the correct driver version, and you **must** disable the built-in kernel driver as described in step 1.

-1. Install the driver from the [Hailo GitHub repository](https://github.com/hailo-ai/hailort-drivers). A convenient script for Linux is available to clone the repository, build the driver, and install it.
-2. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/hailo8l/user_installation.sh).
-3. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
-4. Run the script with `./user_installation.sh`
+:::
+
+1. **Disable the built-in Hailo driver (Raspberry Pi only)**:
+
+   :::note
+
+   If you are **not** using a Raspberry Pi, skip this step and proceed directly to step 2.
+
+   :::
+
+   If you are using a Raspberry Pi, you need to blacklist the built-in kernel Hailo driver to prevent conflicts. First, check if the driver is currently loaded:
+
+   ```bash
+   lsmod | grep hailo
+   ```
+
+   If it shows `hailo_pci`, unload it:
+
+   ```bash
+   sudo rmmod hailo_pci
+   ```
+
+   Now blacklist the driver to prevent it from loading on boot:
+
+   ```bash
+   echo "blacklist hailo_pci" | sudo tee /etc/modprobe.d/blacklist-hailo_pci.conf
+   ```
+
+   Update initramfs to ensure the blacklist takes effect:
+
+   ```bash
+   sudo update-initramfs -u
+   ```
+
+   Reboot your Raspberry Pi:
+
+   ```bash
+   sudo reboot
+   ```
+
+   After rebooting, verify the built-in driver is not loaded:
+
+   ```bash
+   lsmod | grep hailo
+   ```
+
+   This command should return no results. If it still shows `hailo_pci`, the blacklist did not take effect properly and you may need to check for other Hailo packages installed via apt that are loading the driver.
+
+2. **Run the installation script**:
+
+   Download the installation script:
+
+   ```bash
+   wget https://raw.githubusercontent.com/blakeblackshear/frigate/dev/docker/hailo8l/user_installation.sh
+   ```
+
+   Make it executable:
+
+   ```bash
+   sudo chmod +x user_installation.sh
+   ```
+
+   Run the script:
+
+   ```bash
+   ./user_installation.sh
+   ```
+
+   The script will:
+
+   - Install necessary build dependencies
+   - Clone and build the Hailo driver from the official repository
+   - Install the driver
+   - Download and install the required firmware
+   - Set up udev rules
+
+3. **Reboot your system**:
+
+   After the script completes successfully, reboot to load the firmware:
+
+   ```bash
+   sudo reboot
+   ```
+
+4. **Verify the installation**:
+
+   After rebooting, verify that the Hailo device is available:
+
+   ```bash
+   ls -l /dev/hailo0
+   ```
+
+   You should see the device listed. You can also verify the driver is loaded:
+
+   ```bash
+   lsmod | grep hailo_pci
+   ```

 #### Setup

@@ -200,7 +297,7 @@ services:
    shm_size: "512mb" # update for your cameras based on calculation above
    devices:
      - /dev/bus/usb:/dev/bus/usb # Passes the USB Coral, needs to be modified for other versions
-      - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
+      - /dev/apex_0:/dev/apex_0 # Passes a PCIe Coral, follow driver instructions here https://github.com/jnicolson/gasket-builder
      - /dev/video11:/dev/video11 # For Raspberry Pi 4B
      - /dev/dri/renderD128:/dev/dri/renderD128 # For intel hwaccel, needs to be updated for your hardware
    volumes:
--- a/docs/docs/frigate/updating.md
+++ b/docs/docs/frigate/updating.md
@@ -5,7 +5,7 @@ title: Updating

 # Updating Frigate

-The current stable version of Frigate is **0.16.0**. 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/v0.16.0).
+The current stable version of Frigate is **0.16.3**. 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/v0.16.3).

 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., `0.16.0` instead of `0.15.2`). For example:
+     - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.3` instead of `0.15.2`). For example:
       ```yaml
       services:
         frigate:
-           image: ghcr.io/blakeblackshear/frigate:0.16.0
+           image: ghcr.io/blakeblackshear/frigate:0.16.3
       ```
     - Then pull the image:
       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.3
       ```
     - **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., `0.16.0`, `0.16.0-tensorrt`, or `stable`):
+     - Pull the image with the appropriate tag (e.g., `0.16.3`, `0.16.3-tensorrt`, or `stable`):
       ```bash
-       docker pull ghcr.io/blakeblackshear/frigate:0.16.0
+       docker pull ghcr.io/blakeblackshear/frigate:0.16.3
       ```

 3. **Start the Container**:
--- a/docs/docs/guides/getting_started.md
+++ b/docs/docs/guides/getting_started.md
@@ -202,7 +202,7 @@ services:
    ...
    devices:
      - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
-      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://coral.ai/docs/m2/get-started/#2a-on-linux
+      - /dev/apex_0:/dev/apex_0 # passes a PCIe Coral, follow driver instructions here https://github.com/jnicolson/gasket-builder
    ...
 ```

--- a/docs/docs/integrations/home-assistant.md
+++ b/docs/docs/integrations/home-assistant.md
@@ -185,6 +185,26 @@ 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.
--- a/docs/docs/integrations/mqtt.md
+++ b/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,
@@ -61,6 +61,7 @@ Message published for each changed tracked object. The first message is publishe
    }, // attributes with top score that have been identified on the object at any point
    "current_attributes": [], // detailed data about the current attributes in this frame
    "current_estimated_speed": 0.71, // current estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
+    "average_estimated_speed": 14.3, // average estimated speed (mph or kph) for objects moving through zones with speed estimation enabled
    "velocity_angle": 180, // direction of travel relative to the frame for objects moving through zones with speed estimation enabled
    "recognized_license_plate": "ABC12345", // a recognized license plate for car objects
    "recognized_license_plate_score": 0.933451
@@ -70,12 +71,12 @@ 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],
@@ -109,6 +110,7 @@ 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
@@ -139,7 +141,7 @@ 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
 }
 ```

@@ -153,13 +155,20 @@ Message published for updates to tracked object metadata, for example:
  "plate": "123ABC",
  "score": 0.95,
  "camera": "driveway_cam",
-  "timestamp": 1607123958.748393,
+  "timestamp": 1607123958.748393
 }
 ```

 ### `frigate/reviews`

-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.
+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.

 ```json
 {
--- a/docs/docs/plus/annotating.md
+++ b/docs/docs/plus/annotating.md
@@ -42,6 +42,7 @@ 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               |
--- a/docs/docs/plus/first_model.md
+++ b/docs/docs/plus/first_model.md
@@ -34,6 +34,12 @@ 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).
--- a/docs/docs/plus/index.md
+++ b/docs/docs/plus/index.md
@@ -11,34 +11,51 @@ Information on how to integrate Frigate+ with Frigate can be found in the [integ

 ## Available model 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).
+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).

 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. |
+| 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.

 ## Supported detector types

-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.
-
-:::
+Currently, Frigate+ models support CPU (`cpu`), Google Coral (`edgetpu`), OpenVino (`openvino`), ONNX (`onnx`), Hailo (`hailo8l`), and Rockchip\* (`rknn`) detectors.

 | Hardware                                                                         | Recommended Detector Type | Recommended Model Type |
 | -------------------------------------------------------------------------------- | ------------------------- | ---------------------- |
 | [CPU](/configuration/object_detectors.md#cpu-detector-not-recommended)           | `cpu`                     | `mobiledet`            |
 | [Coral (all form factors)](/configuration/object_detectors.md#edge-tpu-detector) | `edgetpu`                 | `mobiledet`            |
-| [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`              |
+| [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`               |

-_\* Requires Frigate 0.15_
+_\* Requires manual conversion in 0.16. Automatic conversion coming in 0.17._

 ## Improving your model

--- a/docs/docs/troubleshooting/edgetpu.md
+++ b/docs/docs/troubleshooting/edgetpu.md
@@ -68,8 +68,7 @@ The USB Coral can become stuck and need to be restarted, this can happen for a n

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

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

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

--- a/frigate/api/auth.py
+++ b/frigate/api/auth.py
@@ -447,8 +447,14 @@ def create_user(
    return JSONResponse(content={"username": body.username})


-@router.delete("/users/{username}")
-def delete_user(username: str):
+@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
+        )
+
    User.delete_by_id(username)
    return JSONResponse(content={"success": True})

--- a/frigate/api/export.py
+++ b/frigate/api/export.py
@@ -8,6 +8,7 @@ from pathlib import Path
 import psutil
 from fastapi import APIRouter, Depends, Request
 from fastapi.responses import JSONResponse
+from pathvalidate import sanitize_filepath
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict

@@ -15,7 +16,7 @@ from frigate.api.auth import require_role
 from frigate.api.defs.request.export_recordings_body import ExportRecordingsBody
 from frigate.api.defs.request.export_rename_body import ExportRenameBody
 from frigate.api.defs.tags import Tags
-from frigate.const import EXPORT_DIR
+from frigate.const import CLIPS_DIR, EXPORT_DIR
 from frigate.models import Export, Previews, Recordings
 from frigate.record.export import (
    PlaybackFactorEnum,
@@ -54,7 +55,14 @@ def export_recording(
    playback_factor = body.playback
    playback_source = body.source
    friendly_name = body.name
-    existing_image = body.image_path
+    existing_image = sanitize_filepath(body.image_path) if body.image_path else None
+
+    # Ensure that existing_image is a valid path
+    if existing_image and not existing_image.startswith(CLIPS_DIR):
+        return JSONResponse(
+            content=({"success": False, "message": "Invalid image path"}),
+            status_code=400,
+        )

    if playback_source == "recordings":
        recordings_count = (
--- a/frigate/api/media.py
+++ b/frigate/api/media.py
@@ -1598,7 +1598,7 @@ def label_thumbnail(request: Request, camera_name: str, label: str):
    try:
        event_id = event_query.scalar()

-        return event_thumbnail(request, event_id, 60)
+        return event_thumbnail(request, event_id, Extension.jpg, 60)
    except DoesNotExist:
        frame = np.zeros((175, 175, 3), np.uint8)
        ret, jpg = cv2.imencode(".jpg", frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
--- a/frigate/data_processing/real_time/bird.py
+++ b/frigate/data_processing/real_time/bird.py
@@ -41,10 +41,13 @@ class BirdRealTimeProcessor(RealTimeProcessorApi):
        self.detected_birds: dict[str, float] = {}
        self.labelmap: dict[int, str] = {}

+        GITHUB_RAW_ENDPOINT = os.environ.get(
+            "GITHUB_RAW_ENDPOINT", "https://raw.githubusercontent.com"
+        )
        download_path = os.path.join(MODEL_CACHE_DIR, "bird")
        self.model_files = {
-            "bird.tflite": "https://raw.githubusercontent.com/google-coral/test_data/master/mobilenet_v2_1.0_224_inat_bird_quant.tflite",
-            "birdmap.txt": "https://raw.githubusercontent.com/google-coral/test_data/master/inat_bird_labels.txt",
+            "bird.tflite": f"{GITHUB_RAW_ENDPOINT}/google-coral/test_data/master/mobilenet_v2_1.0_224_inat_bird_quant.tflite",
+            "birdmap.txt": f"{GITHUB_RAW_ENDPOINT}/google-coral/test_data/master/inat_bird_labels.txt",
        }

        if not all(
--- a/frigate/data_processing/real_time/face.py
+++ b/frigate/data_processing/real_time/face.py
@@ -60,10 +60,12 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
        self.faces_per_second = EventsPerSecond()
        self.inference_speed = InferenceSpeed(self.metrics.face_rec_speed)

+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
+
        download_path = os.path.join(MODEL_CACHE_DIR, "facedet")
        self.model_files = {
-            "facedet.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facedet.onnx",
-            "landmarkdet.yaml": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/landmarkdet.yaml",
+            "facedet.onnx": f"{GITHUB_ENDPOINT}/NickM-27/facenet-onnx/releases/download/v1.0/facedet.onnx",
+            "landmarkdet.yaml": f"{GITHUB_ENDPOINT}/NickM-27/facenet-onnx/releases/download/v1.0/landmarkdet.yaml",
        }

        if not all(
--- a/frigate/detectors/detector_config.py
+++ b/frigate/detectors/detector_config.py
@@ -161,6 +161,10 @@ class ModelConfig(BaseModel):
        if model_info.get("inputDataType"):
            self.input_dtype = model_info["inputDataType"]

+        # RKNN always uses NHWC
+        if detector == "rknn":
+            self.input_tensor = InputTensorEnum.nhwc
+
        # generate list of attribute labels
        self.attributes_map = {
            **model_info.get("attributes", DEFAULT_ATTRIBUTE_LABEL_MAP),
--- a/frigate/detectors/plugins/rknn.py
+++ b/frigate/detectors/plugins/rknn.py
@@ -139,8 +139,9 @@ class Rknn(DetectionApi):
        if not os.path.isdir(model_cache_dir):
            os.mkdir(model_cache_dir)

+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        urllib.request.urlretrieve(
-            f"https://github.com/MarcA711/rknn-models/releases/download/v2.3.2-2/{filename}",
+            f"{GITHUB_ENDPOINT}/MarcA711/rknn-models/releases/download/v2.3.2-2/{filename}",
            model_cache_dir + filename,
        )

--- a/frigate/embeddings/onnx/face_embedding.py
+++ b/frigate/embeddings/onnx/face_embedding.py
@@ -24,11 +24,12 @@ FACENET_INPUT_SIZE = 160

 class FaceNetEmbedding(BaseEmbedding):
    def __init__(self):
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        super().__init__(
            model_name="facedet",
            model_file="facenet.tflite",
            download_urls={
-                "facenet.tflite": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.tflite",
+                "facenet.tflite": f"{GITHUB_ENDPOINT}/NickM-27/facenet-onnx/releases/download/v1.0/facenet.tflite",
            },
        )
        self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
@@ -110,11 +111,12 @@ class FaceNetEmbedding(BaseEmbedding):

 class ArcfaceEmbedding(BaseEmbedding):
    def __init__(self):
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        super().__init__(
            model_name="facedet",
            model_file="arcface.onnx",
            download_urls={
-                "arcface.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/arcface.onnx",
+                "arcface.onnx": f"{GITHUB_ENDPOINT}/NickM-27/facenet-onnx/releases/download/v1.0/arcface.onnx",
            },
        )
        self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
--- a/frigate/embeddings/onnx/lpr_embedding.py
+++ b/frigate/embeddings/onnx/lpr_embedding.py
@@ -34,11 +34,12 @@ class PaddleOCRDetection(BaseEmbedding):
        model_file = (
            "detection-large.onnx" if model_size == "large" else "detection-small.onnx"
        )
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        super().__init__(
            model_name="paddleocr-onnx",
            model_file=model_file,
            download_urls={
-                model_file: f"https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/{model_file}"
+                model_file: f"{GITHUB_ENDPOINT}/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/{model_file}"
            },
        )
        self.requestor = requestor
@@ -94,11 +95,12 @@ class PaddleOCRClassification(BaseEmbedding):
        requestor: InterProcessRequestor,
        device: str = "AUTO",
    ):
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        super().__init__(
            model_name="paddleocr-onnx",
            model_file="classification.onnx",
            download_urls={
-                "classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
+                "classification.onnx": f"{GITHUB_ENDPOINT}/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
            },
        )
        self.requestor = requestor
@@ -154,11 +156,12 @@ class PaddleOCRRecognition(BaseEmbedding):
        requestor: InterProcessRequestor,
        device: str = "AUTO",
    ):
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        super().__init__(
            model_name="paddleocr-onnx",
            model_file="recognition.onnx",
            download_urls={
-                "recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
+                "recognition.onnx": f"{GITHUB_ENDPOINT}/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
            },
        )
        self.requestor = requestor
@@ -214,11 +217,12 @@ class LicensePlateDetector(BaseEmbedding):
        requestor: InterProcessRequestor,
        device: str = "AUTO",
    ):
+        GITHUB_ENDPOINT = os.environ.get("GITHUB_ENDPOINT", "https://github.com")
        super().__init__(
            model_name="yolov9_license_plate",
            model_file="yolov9-256-license-plates.onnx",
            download_urls={
-                "yolov9-256-license-plates.onnx": "https://github.com/hawkeye217/yolov9-license-plates/raw/refs/heads/master/models/yolov9-256-license-plates.onnx"
+                "yolov9-256-license-plates.onnx": f"{GITHUB_ENDPOINT}/hawkeye217/yolov9-license-plates/raw/refs/heads/master/models/yolov9-256-license-plates.onnx"
            },
        )

--- a/frigate/util/services.py
+++ b/frigate/util/services.py
@@ -301,7 +301,7 @@ def get_intel_gpu_stats(intel_gpu_device: Optional[str]) -> Optional[dict[str, s
        "-o",
        "-",
        "-s",
-        "1",
+        "1000",  # Intel changed this from seconds to milliseconds in 2024+ versions
    ]

    if intel_gpu_device:
--- a/web/public/robots.txt
+++ b/web/public/robots.txt
@@ -0,0 +1,2 @@
+User-agent: *
+Disallow: /
--- a/web/src/components/player/HlsVideoPlayer.tsx
+++ b/web/src/components/player/HlsVideoPlayer.tsx
@@ -6,7 +6,7 @@ import {
  useState,
 } from "react";
 import Hls from "hls.js";
-import { isAndroid, isDesktop, isMobile } from "react-device-detect";
+import { isDesktop, isMobile } from "react-device-detect";
 import { TransformComponent, TransformWrapper } from "react-zoom-pan-pinch";
 import VideoControls from "./VideoControls";
 import { VideoResolutionType } from "@/types/live";
@@ -21,7 +21,7 @@ import { ASPECT_VERTICAL_LAYOUT, RecordingPlayerError } from "@/types/record";
 import { useTranslation } from "react-i18next";

 // Android native hls does not seek correctly
-const USE_NATIVE_HLS = !isAndroid;
+const USE_NATIVE_HLS = false;
 const HLS_MIME_TYPE = "application/vnd.apple.mpegurl" as const;
 const unsupportedErrorCodes = [
  MediaError.MEDIA_ERR_SRC_NOT_SUPPORTED,
@@ -139,7 +139,7 @@ export default function HlsVideoPlayer({
      if (hlsRef.current) {
        hlsRef.current.destroy();
      }
-    }
+    };
  }, [videoRef, hlsRef, useHlsCompat, currentSource]);

  // state handling
--- a/web/src/components/player/dynamic/DynamicVideoController.ts
+++ b/web/src/components/player/dynamic/DynamicVideoController.ts
@@ -2,7 +2,10 @@ import { Recording } from "@/types/record";
 import { DynamicPlayback } from "@/types/playback";
 import { PreviewController } from "../PreviewPlayer";
 import { TimeRange, ObjectLifecycleSequence } from "@/types/timeline";
-import { calculateInpointOffset } from "@/utils/videoUtil";
+import {
+  calculateInpointOffset,
+  calculateSeekPosition,
+} from "@/utils/videoUtil";

 type PlayerMode = "playback" | "scrubbing";

@@ -68,38 +71,20 @@ export class DynamicVideoController {
      return;
    }

-    if (
-      this.recordings.length == 0 ||
-      time < this.recordings[0].start_time ||
-      time > this.recordings[this.recordings.length - 1].end_time
-    ) {
-      this.setNoRecording(true);
-      return;
-    }
-
    if (this.playerMode != "playback") {
      this.playerMode = "playback";
    }

-    let seekSeconds = 0;
-    (this.recordings || []).every((segment) => {
-      // if the next segment is past the desired time, stop calculating
-      if (segment.start_time > time) {
-        return false;
-      }
+    const seekSeconds = calculateSeekPosition(
+      time,
+      this.recordings,
+      this.inpointOffset,
+    );

-      if (segment.end_time < time) {
-        seekSeconds += segment.end_time - segment.start_time;
-        return true;
-      }
-
-      seekSeconds +=
-        segment.end_time - segment.start_time - (segment.end_time - time);
-      return true;
-    });
-
-    // adjust for HLS inpoint offset
-    seekSeconds -= this.inpointOffset;
+    if (seekSeconds === undefined) {
+      this.setNoRecording(true);
+      return;
+    }

    if (seekSeconds != 0) {
      this.playerController.currentTime = seekSeconds;
--- a/web/src/components/player/dynamic/DynamicVideoPlayer.tsx
+++ b/web/src/components/player/dynamic/DynamicVideoPlayer.tsx
@@ -13,7 +13,10 @@ import { VideoResolutionType } from "@/types/live";
 import axios from "axios";
 import { cn } from "@/lib/utils";
 import { useTranslation } from "react-i18next";
-import { calculateInpointOffset } from "@/utils/videoUtil";
+import {
+  calculateInpointOffset,
+  calculateSeekPosition,
+} from "@/utils/videoUtil";
 import { isFirefox } from "react-device-detect";

 /**
@@ -99,10 +102,10 @@ export default function DynamicVideoPlayer({
  const [isLoading, setIsLoading] = useState(false);
  const [isBuffering, setIsBuffering] = useState(false);
  const [loadingTimeout, setLoadingTimeout] = useState<NodeJS.Timeout>();
-  const [source, setSource] = useState<HlsSource>({
-    playlist: `${apiHost}vod/${camera}/start/${timeRange.after}/end/${timeRange.before}/master.m3u8`,
-    startPosition: startTimestamp ? timeRange.after - startTimestamp : 0,
-  });
+
+  // Don't set source until recordings load - we need accurate startPosition
+  // to avoid hls.js clamping to video end when startPosition exceeds duration
+  const [source, setSource] = useState<HlsSource | undefined>(undefined);

  // start at correct time

@@ -174,7 +177,7 @@ export default function DynamicVideoPlayer({
  );

  useEffect(() => {
-    if (!controller || !recordings?.length) {
+    if (!recordings?.length) {
      if (recordings?.length == 0) {
        setNoRecording(true);
      }
@@ -182,10 +185,6 @@ export default function DynamicVideoPlayer({
      return;
    }

-    if (playerRef.current) {
-      playerRef.current.autoplay = !isScrubbing;
-    }
-
    let startPosition = undefined;

    if (startTimestamp) {
@@ -193,14 +192,12 @@ export default function DynamicVideoPlayer({
        recordingParams.after,
        (recordings || [])[0],
      );
-      const idealStartPosition = Math.max(
-        0,
-        startTimestamp - timeRange.after - inpointOffset,
-      );

-      if (idealStartPosition >= recordings[0].start_time - timeRange.after) {
-        startPosition = idealStartPosition;
-      }
+      startPosition = calculateSeekPosition(
+        startTimestamp,
+        recordings,
+        inpointOffset,
+      );
    }

    setSource({
@@ -208,6 +205,18 @@ export default function DynamicVideoPlayer({
      startPosition,
    });

+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [recordings]);
+
+  useEffect(() => {
+    if (!controller || !recordings?.length) {
+      return;
+    }
+
+    if (playerRef.current) {
+      playerRef.current.autoplay = !isScrubbing;
+    }
+
    setLoadingTimeout(setTimeout(() => setIsLoading(true), 1000));

    controller.newPlayback({
@@ -215,7 +224,7 @@ export default function DynamicVideoPlayer({
      timeRange,
    });

-    // we only want this to change when recordings update
+    // we only want this to change when controller or recordings update
    // eslint-disable-next-line react-hooks/exhaustive-deps
  }, [controller, recordings]);

@@ -253,38 +262,40 @@ export default function DynamicVideoPlayer({

  return (
    <>
-      <HlsVideoPlayer
-        videoRef={playerRef}
-        containerRef={containerRef}
-        visible={!(isScrubbing || isLoading)}
-        currentSource={source}
-        hotKeys={hotKeys}
-        supportsFullscreen={supportsFullscreen}
-        fullscreen={fullscreen}
-        inpointOffset={inpointOffset}
-        onTimeUpdate={onTimeUpdate}
-        onPlayerLoaded={onPlayerLoaded}
-        onClipEnded={onValidateClipEnd}
-        onPlaying={() => {
-          if (isScrubbing) {
-            playerRef.current?.pause();
-          }
+      {source && (
+        <HlsVideoPlayer
+          videoRef={playerRef}
+          containerRef={containerRef}
+          visible={!(isScrubbing || isLoading)}
+          currentSource={source}
+          hotKeys={hotKeys}
+          supportsFullscreen={supportsFullscreen}
+          fullscreen={fullscreen}
+          inpointOffset={inpointOffset}
+          onTimeUpdate={onTimeUpdate}
+          onPlayerLoaded={onPlayerLoaded}
+          onClipEnded={onValidateClipEnd}
+          onPlaying={() => {
+            if (isScrubbing) {
+              playerRef.current?.pause();
+            }

-          if (loadingTimeout) {
-            clearTimeout(loadingTimeout);
-          }
+            if (loadingTimeout) {
+              clearTimeout(loadingTimeout);
+            }

-          setNoRecording(false);
-        }}
-        setFullResolution={setFullResolution}
-        onUploadFrame={onUploadFrameToPlus}
-        toggleFullscreen={toggleFullscreen}
-        onError={(error) => {
-          if (error == "stalled" && !isScrubbing) {
-            setIsBuffering(true);
-          }
-        }}
-      />
+            setNoRecording(false);
+          }}
+          setFullResolution={setFullResolution}
+          onUploadFrame={onUploadFrameToPlus}
+          toggleFullscreen={toggleFullscreen}
+          onError={(error) => {
+            if (error == "stalled" && !isScrubbing) {
+              setIsBuffering(true);
+            }
+          }}
+        />
+      )}
      <PreviewPlayer
        className={cn(
          className,
--- a/web/src/hooks/use-camera-live-mode.ts
+++ b/web/src/hooks/use-camera-live-mode.ts
@@ -33,29 +33,43 @@ export default function useCameraLiveMode(

  const streamsFetcher = useCallback(async (key: string) => {
    const streamNames = key.split(",");
-    const metadata: { [key: string]: LiveStreamMetadata } = {};

-    await Promise.all(
-      streamNames.map(async (streamName) => {
-        try {
-          const response = await fetch(`/api/go2rtc/streams/${streamName}`);
-          if (response.ok) {
-            const data = await response.json();
-            metadata[streamName] = data;
-          }
-        } catch (error) {
-          // eslint-disable-next-line no-console
-          console.error(`Failed to fetch metadata for ${streamName}:`, error);
+    const metadataPromises = streamNames.map(async (streamName) => {
+      try {
+        const response = await fetch(`/api/go2rtc/streams/${streamName}`, {
+          priority: "low",
+        });
+
+        if (response.ok) {
+          const data = await response.json();
+          return { streamName, data };
        }
-      }),
-    );
+        return { streamName, data: null };
+      } catch (error) {
+        // eslint-disable-next-line no-console
+        console.error(`Failed to fetch metadata for ${streamName}:`, error);
+        return { streamName, data: null };
+      }
+    });
+
+    const results = await Promise.allSettled(metadataPromises);
+
+    const metadata: { [key: string]: LiveStreamMetadata } = {};
+    results.forEach((result) => {
+      if (result.status === "fulfilled" && result.value.data) {
+        metadata[result.value.streamName] = result.value.data;
+      }
+    });

    return metadata;
  }, []);

  const { data: allStreamMetadata = {} } = useSWR<{
    [key: string]: LiveStreamMetadata;
-  }>(restreamedStreamsKey, streamsFetcher, { revalidateOnFocus: false });
+  }>(restreamedStreamsKey, streamsFetcher, {
+    revalidateOnFocus: false,
+    dedupingInterval: 10000,
+  });

  const [preferredLiveModes, setPreferredLiveModes] = useState<{
    [key: string]: LivePlayerMode;
--- a/web/src/utils/videoUtil.ts
+++ b/web/src/utils/videoUtil.ts
@@ -24,3 +24,57 @@ export function calculateInpointOffset(

  return 0;
 }
+
+/**
+ * Calculates the video player time (in seconds) for a given timestamp
+ * by iterating through recording segments and summing their durations.
+ * This accounts for the fact that the video is a concatenation of segments,
+ * not a single continuous stream.
+ *
+ * @param timestamp - The target timestamp to seek to
+ * @param recordings - Array of recording segments
+ * @param inpointOffset - HLS inpoint offset to subtract from the result
+ * @returns The calculated seek position in seconds, or undefined if timestamp is out of range
+ */
+export function calculateSeekPosition(
+  timestamp: number,
+  recordings: Recording[],
+  inpointOffset: number = 0,
+): number | undefined {
+  if (!recordings || recordings.length === 0) {
+    return undefined;
+  }
+
+  // Check if timestamp is within the recordings range
+  if (
+    timestamp < recordings[0].start_time ||
+    timestamp > recordings[recordings.length - 1].end_time
+  ) {
+    return undefined;
+  }
+
+  let seekSeconds = 0;
+
+  (recordings || []).every((segment) => {
+    // if the next segment is past the desired time, stop calculating
+    if (segment.start_time > timestamp) {
+      return false;
+    }
+
+    if (segment.end_time < timestamp) {
+      // Add the full duration of this segment
+      seekSeconds += segment.end_time - segment.start_time;
+      return true;
+    }
+
+    // We're in this segment - calculate position within it
+    seekSeconds +=
+      segment.end_time - segment.start_time - (segment.end_time - timestamp);
+    return true;
+  });
+
+  // Adjust for HLS inpoint offset
+  seekSeconds -= inpointOffset;
+
+  return seekSeconds >= 0 ? seekSeconds : undefined;
+}
--- a/web/src/views/settings/FrigatePlusSettingsView.tsx
+++ b/web/src/views/settings/FrigatePlusSettingsView.tsx
@@ -390,7 +390,6 @@ export default function FrigatePlusSettingsView({
                                      className="cursor-pointer"
                                      value={id}
                                      disabled={
-                                        model.type != config.model.model_type ||
                                        !model.supportedDetectors.includes(
                                          Object.values(config.detectors)[0]
                                            .type,
Author	SHA1	Message	Date
Nicolas Mowen	1d5c2466a8	Update HIKVISION camera link in hardware documentation (#21256 )	2025-12-12 14:25:22 -06:00
GuoQing Liu	0a293aebab	docs: update OpenVINO D-FINE configuration default device (#21231 ) * docs: remove OpenVINO D-FINE configuration device * docs: change D-FINE model detectors default device	2025-12-11 06:31:52 -07:00
User873902	1de7519d1a	Update camera_specific.md for Wyze Cameras (Thingino) (#21221 ) * Update camera_specific.md Wyze Cameras alternative firmware considerations. * Update docs/docs/configuration/camera_specific.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/camera_specific.md * Update camera_specific.md Moved Wyze Camera section --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>	2025-12-10 10:33:10 -07:00
GuoQing Liu	c3f596327e	docs: fix the missing quotes in the Reolink example within the documentation (#21178 )	2025-12-07 07:38:41 -07:00
Nicolas Mowen	90344540b3	Fix jetson build (#21173 )	2025-12-06 09:16:23 -06:00
Josh Hawkins	7167cf57c5	pin cryptography version to fix vapid issues (#21126 )	2025-12-02 07:20:50 -07:00
Josh Hawkins	e47e82f4be	Pin onnx in rfdetr model generation command (#21127 ) * pin onnx in rfdetr model generation command * Apply suggestion from @NickM-27 Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>	2025-12-02 08:15:12 -06:00
munit85	a43d294bd1	Add Axis Q-6155E camera configuration details (#21105 ) * Add Axis Q-6155E camera configuration details Added Axis Q-6155E camera details with ONVIF service port information. * Update Axis Q-6155E ONVIF autotracking support details Added the reason for autotracking not working	2025-12-01 10:47:01 -07:00
Josh Hawkins	9f95a5f31f	version bump in docs (#21111 )	2025-12-01 07:21:27 -07:00
Josh Hawkins	592c245dcd	Fixes (#21061 ) * require admin role to delete users * explicitly prevent deletion of admin user * Recordings playback fixes * Remove nvidia pyindex * Update version --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>	2025-11-26 07:27:16 -06:00
h-leth	914ff4f1e5	add comment about unifi g5 and newer cams (#21003 )	2025-11-22 12:41:13 -06:00
Josh Hawkins	9589c5fc24	Fix rf-detr heading (#20963 ) The link earlier in the file was referencing "#downloading-rf-detr-model"	2025-11-18 18:15:38 -07:00
Nicolas Mowen	3620ef27db	Update hailo installation instructions (#20847 ) * Update hailo docs installation * Adjust section separation	2025-11-08 13:21:15 -06:00
GuoQing Liu	5cf2ae0121	docs: remove webrtc not support H.265 tips (#20769 )	2025-11-05 06:23:45 -06:00
Nicolas Mowen	17d2bc240a	Update recommended hardware to list more models (#20777 ) * Update recommended hardware to list more models * Update hardware.md with new Intel models and links	2025-11-04 10:56:28 -06:00
Nicolas Mowen	6fd7f862f5	Update coral docs / links (#20674 ) * Revise GPU and AI accelerator recommendations Updated hardware recommendations for AI acceleration. * Revise PCIe Coral driver installation instructions Updated instructions for PCIe Coral driver installation. * Revise Coral driver installation instructions Updated driver installation instructions for PCIe and M.2 versions of Google Coral. * Change PCIe Coral driver link in getting_started.md Updated the link for PCIe Coral driver instructions. * Change PCIe Coral driver link in installation guide Updated the link for PCIe Coral driver instructions. * Update Coral TPU recommendation in hardware documentation Added a warning about the Coral TPU's recommendation status for new Frigate installations and suggested alternatives.	2025-10-26 06:56:01 -05:00
Nicolas Mowen	5d038b5c75	Update PWA requirements and add usage section (#20562 ) Added VPN as a secure context option for PWA installation and included a usage section.	2025-10-26 05:39:09 -06:00
Nicolas Mowen	c5fe354552	Improve Reolink Camera Documentation (#20605 ) * Improve Reolink Camera Documentation * Update Reolink configuration link in live.md	2025-10-21 16:20:41 -06:00
Josh Hawkins	5dc8a85f2f	Update Azure OpenAI genai docs (#20549 ) * Update azure openai genai docs * tweak url	2025-10-18 06:44:26 -06:00
Nicolas Mowen	0302db1c43	Fix model exports (#20540 )	2025-10-17 07:16:30 -05:00
Nicolas Mowen	a4764563a5	Fix YOLOv9 export script (#20514 )	2025-10-16 07:56:37 -05:00
Josh Hawkins	942a61ddfb	version bump in docs (#20501 )	2025-10-15 05:53:31 -06:00
Nicolas Mowen	4d582062fb	Ensure that a user must provide an image in an expected location (#20491 ) * Ensure that a user must provide an image in an expected location * Use const	2025-10-14 16:29:20 -05:00
Nicolas Mowen	e0a8445bac	Improve rf-detr export (#20485 )	2025-10-14 08:32:44 -05:00
Josh Hawkins	2a271c0f5e	Update GenAI docs for Gemini model deprecation (#20462 )	2025-10-13 10:00:21 -06:00
Nicolas Mowen	925bf78811	Update review topic description (#20445 )	2025-10-12 07:28:08 -05:00
Sean Kelly	59102794e8	Add keyboard shortcut for switching to previous label (#20426 ) * Add keyboard shortcut for switching to previous label * Update docs/docs/plus/annotating.md Co-authored-by: Blake Blackshear <blake.blackshear@gmail.com> --------- Co-authored-by: Blake Blackshear <blake.blackshear@gmail.com>	2025-10-11 10:43:41 -06:00
mpking828	20e5e3bdc0	Update camera_specific.md to fix 2 way audio example for Reolink (#20343 ) Update camera_specific.md to fix 2 way audio example for Reolink	2025-10-03 08:49:51 -06:00
AmirHossein_Omidi	b94ebda9e5	Update license_plate_recognition.md (#20306 ) * Update license_plate_recognition.md Add PaddleOCR description for license plate recognition in Frigate docs * Update docs/docs/configuration/license_plate_recognition.md Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Update docs/docs/configuration/license_plate_recognition.md Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>	2025-10-01 08:18:47 -05:00
Nicolas Mowen	8cdaef307a	Update face rec docs (#20256 ) * Update face rec docs * clarify Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>	2025-09-28 11:31:59 -05:00
Josh Hawkins	4914029a50	Add average_estimated_speed to mqtt docs (#20101 )	2025-09-16 11:03:36 -06:00
GuoQing Liu	bafdab9d67	feat: add robots.txt (#20093 )	2025-09-16 06:14:27 -06:00
GuoQing Liu	b08db4913f	feat: add github mirror download endpoint (#20007 ) * feat: add github mirror download endpoint * fix: fix face_embedding endpoint line * fix: fix github raw endpoint Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>	2025-09-14 06:51:56 -06:00
Nicolas Mowen	7c7ff49b90	Improve d-fine model export docs (#20020 )	2025-09-11 06:17:08 -05:00
Nicolas Mowen	037c4d1cc0	Don't block UI while pulling the stream live info (#19998 )	2025-09-09 17:53:26 -05:00
laviddichterman	1613499218	Update object_detectors.md to document configuring image size in YOLO 9 (#19951 ) * Update object_detectors.md for v16 * add configurability to IMG_SIZE for YOLOv9 export * remove TensorRT detector as it's no longer supported in v16 * Revert removing NVIDIA TensorRT detector docs Added documentation for NVidia TensorRT Detector, including model generation, configuration parameters, and example usage. * Dumb copy/paste * Enhance YOLOv9 export instructions in documentation Updated YOLOv9 export command to include IMG_SIZE parameter and clarified model size options.	2025-09-09 14:27:30 -06:00
Nicolas Mowen	205fdf3ae3	Fixes (#19984 ) * Always handle RKNN as NHWC in Frigate+ model loading * Correct Intel stats * Update inference time docs * Update version * Adjust inference speeds	2025-09-09 06:17:56 -06:00
Nicolas Mowen	f46f8a2160	More inference speed updates (#19974 )	2025-09-08 10:39:33 -06:00
Josh Hawkins	880902cdd7	Add specific notes for frigate+ models in object detector docs (#19971 )	2025-09-08 09:29:03 -05:00
Nicolas Mowen	c5ed95ec52	More inference speed updates (#19947 ) * More inference speed updates * Update hardware.md * Update hardware.md * Update index.md * More inference speeds * Update home-assistant.md * Update object_detectors.md * Update first_model.md	2025-09-08 07:43:04 -05:00
Josh Hawkins	751de141d5	Fix model selection type in Frigate+ settings pane (#19952 ) * model type does not need to match config model type As long as a model is supported by a detector, it should be available in the list * fix missing semicolon the web linter was complaining	2025-09-07 19:19:40 -06:00
Nicolas Mowen	0eb441fe50	Update inference times for yolov9 (#19946 )	2025-09-07 14:59:48 -05:00
Josh Hawkins	7566aecb0b	Add note about Apple Silicon support in 0.17 (#19944 )	2025-09-07 14:12:49 -05:00
Blake Blackshear	60714a733e	update docs for Frigate+ yolov9 (#19938 ) * update docs for Frigate+ yolov9 * footnote memryx suport * tweaks	2025-09-07 06:01:10 -05:00
Josh Hawkins	d7f7cd7be1	best thumbnail endpoint should pass correct extension param (#19930 )	2025-09-05 06:33:57 -05:00
GuoQing Liu	6591210050	docs: fix reolink camera table display (#19926 )	2025-09-05 06:01:26 -05:00
Nicolas Mowen	7e7b3288a8	Update live FAQ for camera distortion (#19907 ) * Add item to FAQ about stream distortion * Update updating docs * Update link	2025-09-04 07:44:33 -05:00
Nicolas Mowen	fe3eb24dfe	Update Reolink support docs (#19887 )	2025-09-02 15:21:18 -05:00