Frigate

detectors:
  onnx:
    type: onnx
    device: GPU
model:
  model_type: yolo-generic
  path: /config/model_cache/onnx/yolov9-s-320.onnx
  input_tensor: nchw
  input_pixel_format: rgb
  input_dtype: float
  width: 640
  height: 640
  labelmap_path: /labelmap/coco-80.txt

Dockerfile

FROM python:3.11 AS build
RUN apt-get update && apt-get install --no-install-recommends -y cmake libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.10.4 /uv /bin/
WORKDIR /yolov9
ADD https://github.com/WongKinYiu/yolov9.git .
RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier==0.4.* onnxscript
ARG MODEL_SIZE
ARG IMG_SIZE
ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx

FROM scratch
ARG MODEL_SIZE
ARG IMG_SIZE
COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
EOF

sudo docker build . --build-arg MODEL_SIZE=s --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
sudo mv yolov9-s-320.onnx /application/frigate/model_cache/onnx/