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Revamp YOLOv9 export guide (#19224)

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* Revamp YOLOv9 export guide

* Make variant a build arg

* Change VARIANT to MODEL_SIZE

* Mention available models
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Felipe Santos 2025-07-20 12:19:23 -03:00 committed by GitHub
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docs/docs/configuration/object_detectors.md
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@ -1032,22 +1032,23 @@ python3 yolo_to_onnx.py -m yolov7-320
#### YOLOv9
YOLOv9 models can be exported using the below code
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
git clone https://github.com/WongKinYiu/yolov9
cd yolov9
# setup the virtual environment so installation doesn't affect main system
# NOTE: Virtual environment must be using Python 3.11 or older.
python3 -m venv ./
bin/pip install -r requirements.txt
bin/pip install onnx onnxruntime onnx-simplifier>=0.4.1
# download the weights
wget -O yolov9-t.pt "https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt" # download the weights
# prepare and run export script
sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" ./models/experimental.py
bin/python3 export.py --weights ./yolov9-t.pt --imgsz 320 --simplify --include onnx
docker build . --build-arg MODEL_SIZE=t --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
ARG MODEL_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-${MODEL_SIZE}.pt --imgsz 320 --simplify --include onnx
FROM scratch
ARG MODEL_SIZE
COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /
EOF
```