(feats, anchors, num_classes, n)
return boxes, box_scores
Process Conv layer output
Process Conv layer output
Process Conv layer output
def yolo_boxes_and_scores(feats, anchors, num_classes, n):
    '''Process Conv layer output'''
    box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats, anchors, num_classes, n)
    # Convert boxes to be ready for filtering functions 
    boxes = yolo_boxes_to_corners(box_xy, box_wh)
    boxes = K.reshape(boxes, [-1, 3, 4])
    # Compute box scores
    box_scores = box_confidence * box_class_probs
    box_scores = K.reshape(box_scores, [-1, 3, num_classes])
    return boxes, box_scores
def yolo_boxes_and_scores ( feats , anchors , num_classes , n ) : box_xy , box_wh , box_confidence , box_class_probs = yolo_head ( feats , anchors , num_classes , n ) # Convert boxes to be ready for filtering functions  boxes = yolo_boxes_to_corners ( box_xy , box_wh ) boxes = K . reshape ( boxes , [ - 1 , 3 , 4 ] ) # Compute box scores box_scores = box_confidence * box_class_probs box_scores = K . reshape ( box_scores , [ - 1 , 3 , num_classes ] ) return boxes , box_scores