I might have a solution, but I’m not sure if it’s general, but I think that it worth checking.
It seems that on datasets that have low number of classes and/or low number of pictures, some nodes on the YOLOv8 are getting almost nullified.
I’ve used this command to force a wider dynamic range on the outputs:
quantization_param([conv42, conv53, conv63], force_range_out=[0.0, 1.0])
This is relevant for YOLOV8s trained from our retraining dockers. These are the end nodes before the NMS layer.
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