Thanks for writing this up. Actually, I got the same questions when I look into YOLOv3 because the description in the original paper is a little bit ambiguous. So I will do my best but the best way is to ask the researcher directly.

The concept has some similarity with Faster R-CNN. Some quotes from the Faster R-CNN paper:

“ We assign a positive label to an anchor that has an IoU overlap higher than 0.7 with any ground-truth box. …

We assign a negative label to a non-positive anchor if its IoU ratio is lower than 0.3 for all ground-truth boxes. Anchors that are neither positive nor negative do not contribute to the training objective.”

Faster R-CNN compute the confidence loss from these 2 groups only.

Faster R-CNN can have multiple positive anchors but in YOLOv3, just the top match. So the confusion is from

“ If a bounding box prior is not assigned to a ground truth object it incurs no loss for coordinate or class predictions, only objectness.”

I interpret it as: for the group with low confidence score, the label assigned is 0 and therefore we compute penalty for its confidence score not being 0.

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Deep Learning

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