In general, the positive samples are much larger than negative samples in object detection. In two-stage detection method such as Faster-rcnn, hard negative mining is used to extremely reduce the negative samples. But in one-stage method such as yolov3, it’s not seemly necessary to use the methods like hard negative mining and all negative samples participate in training. Why?