non-maxima suppresion after feature generation is computationally wasteful
anlutfi opened this issue · 0 comments
anlutfi commented
In object_tracker.py you first do detections = [Detection(bbox, score, class_name, feature) for bbox, score, class_name, feature in zip(converted_boxes, scores[0], names, features)]
and then indices = preprocessing.non_max_suppression(boxs, classes, nms_max_overlap, scores)
I suggest that you perform NMS immediately after running yolo, so you don't waste time computing boxes that will just end up unused