pythonpytorchobject-detectionyolov5

I am using the YOLOv5 model provided by Ultralytics in PyTorch. How can I see which images the model is struggling with?


This is the YOLOv5 implementation I am talking about and this is the file I am using to test the model.

For some classes, it performing decently enough. However, for the rest of the classes, it is not doing a great job. I would like to see the type of images where the model struggles.

How can I get the name of the images or the file paths?

I tried running this file with the --save-txt parameter but I do not understand its meaning.

Thank you!


Solution

  • To be able to track the learning of a model I recommend environments like ClearML.
    ClearML allows you to see real-time results on some images while model learning

    Yolov5 allows you to integrate them without any problem for example here.

    Regarding --save-txt; on val.py save the validation results to a .txt file
    To default you can see your output into yolov5/runs/val/exp--