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Update object_detectors.md for v16

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* add configurability to IMG_SIZE for YOLOv9 export
* remove TensorRT detector as it's no longer supported in v16
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laviddichterman 2025-09-07 16:36:25 -07:00 committed by GitHub
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docs/docs/configuration/object_detectors.md
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@ -698,88 +698,6 @@ To verify that the integration is working correctly, start Frigate and observe t
# Community Supported Detectors
## NVidia TensorRT Detector
Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
### Generate Models
The model used for TensorRT must be preprocessed on the same hardware platform that they will run on. This means that each user must run additional setup to generate a model file for the TensorRT library. A script is included that will build several common models.
The Frigate image will generate model files during startup if the specified model is not found. Processed models are stored in the `/config/model_cache` folder. Typically the `/config` path is mapped to a directory on the host already and the `model_cache` does not need to be mapped separately unless the user wants to store it in a different location on the host.
By default, no models will be generated, but this can be overridden by specifying the `YOLO_MODELS` environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. Models will only be generated if the corresponding `{model}.trt` file is not present in the `model_cache` folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.
If you have a Jetson device with DLAs (Xavier or Orin), you can generate a model that will run on the DLA by appending `-dla` to your model name, e.g. specify `YOLO_MODELS=yolov7-320-dla`. The model will run on DLA0 (Frigate does not currently support DLA1). DLA-incompatible layers will fall back to running on the GPU.
If your GPU does not support FP16 operations, you can pass the environment variable `USE_FP16=False` to disable it.
Specific models can be selected by passing an environment variable to the `docker run` command or in your `docker-compose.yml` file. Use the form `-e YOLO_MODELS=yolov4-416,yolov4-tiny-416` to select one or more model names. The models available are shown below.
<details>
<summary>Available Models</summary>
```
yolov3-288
yolov3-416
yolov3-608
yolov3-spp-288
yolov3-spp-416
yolov3-spp-608
yolov3-tiny-288
yolov3-tiny-416
yolov4-288
yolov4-416
yolov4-608
yolov4-csp-256
yolov4-csp-512
yolov4-p5-448
yolov4-p5-896
yolov4-tiny-288
yolov4-tiny-416
yolov4x-mish-320
yolov4x-mish-640
yolov7-tiny-288
yolov7-tiny-416
yolov7-640
yolov7-416
yolov7-320
yolov7x-640
yolov7x-320
```
</details>
An example `docker-compose.yml` fragment that converts the `yolov4-608` and `yolov7x-640` models would look something like this:
```yml
frigate:
environment:
- YOLO_MODELS=yolov7-320,yolov7x-640
- USE_FP16=false
```
### Configuration Parameters
The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration_video.md#nvidia-gpus) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
The TensorRT detector uses `.trt` model files that are located in `/config/model_cache/tensorrt` by default. These model path and dimensions used will depend on which model you have generated.
Use the config below to work with generated TRT models:
```yaml
detectors:
tensorrt:
type: tensorrt
device: 0 #This is the default, select the first GPU
model:
path: /config/model_cache/tensorrt/yolov7-320.trt
labelmap_path: /labelmap/coco-80.txt
input_tensor: nchw
input_pixel_format: rgb
width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
```
## Rockchip platform
Hardware accelerated object detection is supported on the following SoCs:
@ -1033,7 +951,7 @@ python3 yolo_to_onnx.py -m yolov7-320
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`).
```sh
docker build . --build-arg MODEL_SIZE=t --output . -f- <<'EOF'
docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=640 --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/
@ -1042,11 +960,13 @@ 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
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
```