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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.
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@ -1049,10 +1049,10 @@ python3 yolo_to_onnx.py -m yolov7-320
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#### YOLOv9
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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`).
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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`).
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```sh
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docker build . --build-arg MODEL_SIZE=t --output . -f- <<'EOF'
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docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --output . -f- <<'EOF'
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FROM python:3.11 AS build
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RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
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COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
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@ -1061,11 +1061,13 @@ ADD https://github.com/WongKinYiu/yolov9.git .
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RUN uv pip install --system -r requirements.txt
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RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1
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ARG MODEL_SIZE
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ARG IMG_SIZE
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ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
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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
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RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz 320 --simplify --include onnx
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RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz ${IMG_SIZE} --simplify --include onnx
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FROM scratch
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ARG MODEL_SIZE
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COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /
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ARG IMG_SIZE
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COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /yolov9-${MODEL_SIZE}-${IMG_SIZE}.onnx
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EOF
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```
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