Hello everyone, I am using the YOLOv8 model and I wanted to do analyze my results depending on the zone of the image.
To do this I can’t just use overall metrics returned by the model and have to calculate them myself, so that is what I am doing.
I do a model.predict with conf = 0.25 and IoU = 0.5 and then compare the preds with the ground truth calculating from here the TP, FN and FP and subsequently the overall Precision and Recall.
To make sure I am calculating them correctly I am testing on the val set and comparing them to the best epoch results of my model (the output when training shows the P and the R, those are the values I am comparing to).
However, my Precision and Recall values from the manual calculations seem to be like 10% better than the output values when training.
I heard that YOLO’s table reports macro averages (per-class P and R, then averaged across classes and that could be a cause of the problem. Is this real? Does that make sense or am I probably calculating the TP, FN and FP the wrong way? (visually they look correct)