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I'm not quite sure what is the main reason why YOLO uses multiple bounding boxes for a grid cell. An answer I can find on web is for multiple aspect ratio in the prediction. However I don’t think it’s completely true because if I understand correctly, YOLO optimizes the offset of a prior proportional to the size of the image, and then the box can expand to anywhere in the image, and the number of boxes doesn’t really matter. My guess is that maybe it’s a heuristic move to speed up the computation or to avoid sticking to a bad local optima. Or is there another good explanation here?

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