I am using YOLOv5 for object detection.

I understand that any labelled classes that are not predicted, that is, false negatives (FN) shows up as background. But how are the false positive (FP) being calculated? As in if the background is not explicitly labelled in the data, how are we calculating the false positives?

Please see the following confusion matrix for reference. 6 classes with the background class The last row is "background FN". The last column is "background FP".

Text Image source: https://github.com/ultralytics/yolov5/issues/6738

  • $\begingroup$ I am facing the same issue, did you have any reasonable answer? I would be happy to hear $\endgroup$ Sep 13, 2023 at 14:04


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