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5
docs/docs/configuration/camera_specific.md
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@@ -147,7 +147,7 @@ WEB Digest Algorithm - MD5
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 | |
@@ -250,6 +250,7 @@ TP-Link VIGI cameras need some adjustments to the main stream settings on the ca
To use a USB camera (webcam) with Frigate, the recommendation is to use go2rtc's [FFmpeg Device](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg-device) support:
- Preparation outside of Frigate:
- Get USB camera path. Run `v4l2-ctl --list-devices` to get a listing of locally-connected cameras available. (You may need to install `v4l-utils` in a way appropriate for your Linux distribution). In the sample configuration below, we use `video=0` to correlate with a detected device path of `/dev/video0`
- Get USB camera formats & resolutions. Run `ffmpeg -f v4l2 -list_formats all -i /dev/video0` to get an idea of what formats and resolutions the USB Camera supports. In the sample configuration below, we use a width of 1024 and height of 576 in the stream and detection settings based on what was reported back.
- If using Frigate in a container (e.g. Docker on TrueNAS), ensure you have USB Passthrough support enabled, along with a specific Host Device (`/dev/video0`) + Container Device (`/dev/video0`) listed.
@@ -277,5 +278,3 @@ cameras:
width: 1024
height: 576
```

4
docs/docs/configuration/live.md
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@@ -272,3 +272,7 @@ 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.

70
docs/docs/configuration/object_detectors.md
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@@ -35,6 +35,7 @@ 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.
@@ -331,6 +332,12 @@ 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
@@ -592,6 +599,12 @@ 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
@@ -619,6 +632,12 @@ 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
@@ -1303,26 +1322,29 @@ Here are some tips for getting different model types
### Downloading D-FINE Model
To export as ONNX:
1. Clone: https://github.com/Peterande/D-FINE and install all dependencies.
2. Select and download a checkpoint from the [readme](https://github.com/Peterande/D-FINE).
3. Modify line 58 of `tools/deployment/export_onnx.py` and change batch size to 1: `data = torch.rand(1, 3, 640, 640)`
4. Run the export, making sure you select the right config, for your checkpoint.
Example:
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.
```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
# Create output directory and download checkpoint
RUN mkdir -p output
ARG MODEL_SIZE
RUN wget https://github.com/Peterande/storage/releases/download/dfinev1.0/dfine_${MODEL_SIZE}_obj2coco.pth -O output/dfine_${MODEL_SIZE}_obj2coco.pth
# Modify line 58 of export_onnx.py to change batch size to 1
RUN sed -i '58s/data = torch.rand(.*)/data = torch.rand(1, 3, 640, 640)/' tools/deployment/export_onnx.py
RUN python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_${MODEL_SIZE}_obj2coco.yml -r output/dfine_${MODEL_SIZE}_obj2coco.pth
FROM scratch
ARG MODEL_SIZE
COPY --from=build /dfine/output/dfine_${MODEL_SIZE}_obj2coco.onnx /dfine-${MODEL_SIZE}.onnx
EOF
```
python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth
```
:::tip
Model export has only been tested on Linux (or WSL2). Not all dependencies are in `requirements.txt`. Some live in the deployment folder, and some are still missing entirely and must be installed manually.
Make sure you change the batch size to 1 before exporting.
:::
### Download RF-DETR Model
@@ -1374,23 +1396,25 @@ 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
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
```

60
docs/docs/frigate/hardware.md
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@@ -56,6 +56,7 @@ Frigate supports multiple different detectors that work on different types of ha
- Runs best with tiny or small size models
- [Google Coral EdgeTPU](#google-coral-tpu): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
- [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#edge-tpu-detector)
- [MemryX](#memryx-mx3): The MX3 M.2 accelerator module is available in m.2 format allowing for a wide range of compatibility with devices.
@@ -110,6 +111,7 @@ 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
@@ -142,17 +144,19 @@ 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
@@ -160,7 +164,7 @@ Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA
#### Minimum Hardware Support
12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
Make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
@@ -180,12 +184,13 @@ Inference speeds will vary greatly depending on the GPU and the model used.
✅ - Accelerated with CUDA Graphs
❌ - Not accelerated with CUDA Graphs
| Name | ✅ YOLOv9 Inference Time | ✅ RF-DETR Inference Time | ❌ YOLO-NAS Inference Time |
| --------------- | ------------------------------------- | ------------------------- | --------------------------- |
| RTX 3050 | t-320: 8 ms s-320: 10 ms s-640: 28 ms | Nano-320: ~ 12 ms | 320: ~ 10 ms 640: ~ 16 ms |
| RTX 3070 | t-320: 6 ms s-320: 8 ms s-640: 25 ms | Nano-320: ~ 9 ms | 320: ~ 8 ms 640: ~ 14 ms |
| RTX A4000 | | | 320: ~ 15 ms |
| Tesla P40 | | | 320: ~ 105 ms |
| Name | ✅ YOLOv9 Inference Time | ✅ RF-DETR Inference Time | ❌ YOLO-NAS Inference Time |
| --------- | ------------------------------------- | ------------------------- | -------------------------- |
| GTX 1070 | s-320: 16 ms | | 320: 14 ms |
| RTX 3050 | t-320: 8 ms s-320: 10 ms s-640: 28 ms | Nano-320: ~ 12 ms | 320: ~ 10 ms 640: ~ 16 ms |
| RTX 3070 | t-320: 6 ms s-320: 8 ms s-640: 25 ms | Nano-320: ~ 9 ms | 320: ~ 8 ms 640: ~ 14 ms |
| RTX A4000 | | | 320: ~ 15 ms |
| Tesla P40 | | | 320: ~ 105 ms |
### Apple Silicon
@@ -197,19 +202,20 @@ Apple Silicon can not run within a container, so a ZMQ proxy is utilized to comm
:::
| Name | YOLOv9 Inference Time |
| --------- | ------------------------------------ |
| M4 | s-20: 10 ms |
| M3 Pro | t-320: 6 ms s-320: 8 ms s-640: 20 ms |
| M1 | s-320: 9ms |
| Name | YOLOv9 Inference Time |
| ------ | ------------------------------------ |
| M4 | s-320: 10 ms |
| M3 Pro | t-320: 6 ms s-320: 8 ms s-640: 20 ms |
| M1 | s-320: 9ms |
### ROCm - AMD GPU
With the [ROCm](../configuration/object_detectors.md#amdrocm-gpu-detector) detector Frigate can take advantage of many discrete AMD GPUs.
| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time |
| --------- | ------------------------- | ------------------------- |
| AMD 780M | t-320: 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms |
| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time |
| --------- | --------------------------- | ------------------------- |
| AMD 780M | t-320: ~ 14 ms s-320: 20 ms | 320: ~ 25 ms 640: ~ 50 ms |
| AMD 8700G | | 320: ~ 20 ms 640: ~ 40 ms |
## Community Supported Detectors
@@ -228,7 +234,7 @@ Detailed information is available [in the detector docs](/configuration/object_d
The MX3 is a pipelined architecture, where the maximum frames per second supported (and thus supported number of cameras) cannot be calculated as `1/latency` (1/"Inference Time") and is measured separately. When estimating how many camera streams you may support with your configuration, use the **MX3 Total FPS** column to approximate of the detector's limit, not the Inference Time.
| Model | Input Size | MX3 Inference Time | MX3 Total FPS |
|----------------------|------------|--------------------|---------------|
| -------------------- | ---------- | ------------------ | ------------- |
| YOLO-NAS-Small | 320 | ~ 9 ms | ~ 378 |
| YOLO-NAS-Small | 640 | ~ 21 ms | ~ 138 |
| YOLOv9s | 320 | ~ 16 ms | ~ 382 |

12
docs/docs/frigate/updating.md
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@@ -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 **0.16.1**. 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/v0.16.1).
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., `0.16.1` instead of `0.15.2`). For example:
```yaml
services:
frigate:
image: ghcr.io/blakeblackshear/frigate:0.16.0
image: ghcr.io/blakeblackshear/frigate:0.16.1
```
- Then pull the image:
```bash
docker pull ghcr.io/blakeblackshear/frigate:0.16.0
docker pull ghcr.io/blakeblackshear/frigate:0.16.1
```
- **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., `0.16.1`, `0.16.1-tensorrt`, or `stable`):
```bash
docker pull ghcr.io/blakeblackshear/frigate:0.16.0
docker pull ghcr.io/blakeblackshear/frigate:0.16.1
```
3. **Start the Container**:

20
docs/docs/integrations/home-assistant.md
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@@ -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.

26
docs/docs/integrations/mqtt.md
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@@ -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

6
docs/docs/plus/first_model.md
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@@ -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).

49
docs/docs/plus/index.md
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@@ -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

2
frigate/api/media.py
View File

@@ -1707,7 +1707,7 @@ 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])

7
frigate/data_processing/real_time/bird.py
View File

@@ -42,10 +42,13 @@ 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(

6
frigate/data_processing/real_time/face.py
View File

@@ -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(

4
frigate/detectors/detector_config.py
View File

@@ -161,6 +161,10 @@ class ModelConfig(BaseModel):
if model_info.get("inputDataType"):
self.input_dtype = InputDTypeEnum(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),

3
frigate/detectors/plugins/rknn.py
View File

@@ -165,8 +165,9 @@ 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,
)

6
frigate/embeddings/onnx/face_embedding.py
View File

@@ -27,11 +27,12 @@ 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)
@@ -114,11 +115,12 @@ class FaceNetEmbedding(BaseEmbedding):
class ArcfaceEmbedding(BaseEmbedding):
def __init__(self, config: FaceRecognitionConfig):
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.config = config

14
frigate/embeddings/onnx/lpr_embedding.py
View File

@@ -37,11 +37,12 @@ class PaddleOCRDetection(BaseEmbedding):
if model_size == "large"
else "detection_v5-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/{'v3' if model_size == 'large' else 'v5'}/{model_file}"
model_file: f"{GITHUB_ENDPOINT}/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/{'v3' if model_size == 'large' else 'v5'}/{model_file}"
},
)
self.requestor = requestor
@@ -97,11 +98,12 @@ 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
@@ -157,12 +159,13 @@ 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_v4.onnx",
download_urls={
"recognition_v4.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/v4/recognition_v4.onnx",
"ppocr_keys_v1.txt": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/v4/ppocr_keys_v1.txt",
"recognition_v4.onnx": f"{GITHUB_ENDPOINT}/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/v4/recognition_v4.onnx",
"ppocr_keys_v1.txt": f"{GITHUB_ENDPOINT}/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/v4/ppocr_keys_v1.txt",
},
)
self.requestor = requestor
@@ -218,11 +221,12 @@ 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"
},
)

2
frigate/util/services.py
View File

@@ -303,7 +303,7 @@ 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:

2
web/public/robots.txt Normal file
View File

@@ -0,0 +1,2 @@
User-agent: *
Disallow: /

2
web/src/components/player/HlsVideoPlayer.tsx
View File

@@ -139,7 +139,7 @@ export default function HlsVideoPlayer({
if (hlsRef.current) {
hlsRef.current.destroy();
}
}
};
}, [videoRef, hlsRef, useHlsCompat, currentSource]);
// state handling

44
web/src/hooks/use-camera-live-mode.ts
View File

@@ -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;

1
web/src/views/settings/FrigatePlusSettingsView.tsx
View File

@@ -391,7 +391,6 @@ 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,