CONVERT yolov5 TO onxx and openvino format about yolov5

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[glenn-jocher]

@FrancoArtale hello! It seems you encountered an issue with tensor shape incompatibility during the OpenVINO export. The error indicates that the input tensor shape expected by OpenVINO does not match the model's input shape defined during the export.

This common issue generally occurs during quantization when the configurations don't align perfectly. In your case, since --int8 triggers NNCF quantization to 8 bits, ensure that all input dimensions remain consistent.

For this specific situation, make sure that the image size (--imgsz) used during OpenVINO export matches the size that the model was trained on or adjusted for during the quantization process. I recommend revisiting the export parameters, particularly the --imgsz to ensure it matches across both your model training and exporting scripts.

If double-checking the export parameter sizes doesn't resolve the issue, it might be worthwhile to experiment with --simplify during the ONNX export, which can sometimes resolve tensor shape discrepancies before moving to OpenVINO conversion.

Hope that helps! Let us know how it goes. 🌟

[FrancoArtale]

--simplify didn't work but if I change from --include openvino to --include onnx it works.

what's the difference of using openvino or onnx besides the format?
Is one of the two better than the other? faster?

Greetings,
FA.

[glenn-jocher]

@FrancoArtale hello FA! 🌟

The key difference between using OpenVINO and ONNX is mainly the backend they are optimized for. ONNX is a more generic model format that provides interoperability across different AI frameworks. It allows you to use the model in a variety of platforms and environments.

OpenVINO, on the other hand, is specifically optimized for Intel hardware. Using OpenVINO can lead to better performance optimization in terms of inference speed and efficiency when deployed on Intel CPUs, GPUs, or VPUs.

As to which is better: if your deployment target includes Intel hardware, OpenVINO might give you better performance optimizations. Otherwise, ONNX provides great flexibility.

Hope this clears things up! 🚀