Guidance/Libary Needed for ONNX Model Post-Processing

[Throws100]

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Question

I've trained a custom YOLOv5 model that performs well, and I've exported it as an ONNX file. However, the output from the ONNX model differs from my expectations. After researching, I understand that ONNX outputs require additional post-processing to match the results seen with the PyTorch model.

Unfortunately, I'm struggling to implement this post-processing manually. Since deploying models in other languages is a common task, I was wondering if anyone could point me to an existing component or library that handles YOLOv5 ONNX post-processing.

Any guidance or resources would be greatly appreciated! Thank you, Seb

Additional

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[UltralyticsAssistant]

👋 Hello @Throws100, thank you for your interest in YOLOv5 🚀! Your question about ONNX model post-processing is noted. This is an automated response, but an Ultralytics engineer will assist you further as soon as possible 😊.

For now, if this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it. This should include any relevant code snippets and a description of inputs and outputs.

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git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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[pderrenger]

@Throws100 thank you for your query, Seb. For YOLOv5 ONNX post-processing, you’ll need to handle the post-processing manually, as ONNX outputs raw model predictions. This typically includes applying non-maximum suppression (NMS), decoding bounding box coordinates, and reshaping logits. Libraries like ONNX Runtime or OpenCV’s DNN module might help.

Refer to the following for a detailed example:
YOLOv5 ONNX Export Documentation.

If you’re still encountering issues, you might want to explore discussions or examples from community repositories like Hexmagic/ONNX-yolov5. Let us know if further clarification is needed!