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I have been reading more about computer vision and I'm bothered by YOLO and similar deep learning architectures.

The thing I am confused about is how non-class image sections are dealt with. In particular, it's not clear to me at all why YOLO doesn't consider every part of an image a possible class.

What actually sets the cutoff for detection and then classification?

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The output of YOLO is (x,y,w,h,confidence,class). The confidence value presents whether the rectangle holds an object, the rectangle is non-classed when confidence is low.

The class value will be used, only when confidence is high.

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