How does YOLO handle non-class objects? 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?
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.