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				|  | @ -388,10 +388,6 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4): | |||
|                     dc = dc[1:][iou < nms_thres]  # remove ious > threshold | ||||
| 
 | ||||
|                 # Image      Total          P          R        mAP | ||||
|                 #    32       5000      0.633      0.579      0.568 | ||||
|                 #    64       5000      0.619      0.579      0.568 | ||||
|                 #    96       5000      0.652      0.622      0.613 | ||||
|                 #   128       5000      0.651      0.625      0.617 | ||||
|                 #  5000       5000      0.627      0.593      0.584 | ||||
| 
 | ||||
|             elif nms_style == 'AND':  # requires overlap, single boxes erased | ||||
|  | @ -406,16 +402,15 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4): | |||
|                     iou = bbox_iou(dc[:1], dc[0:])  # iou with other boxes | ||||
|                     i = iou > nms_thres | ||||
| 
 | ||||
|                     weights = (dc[i, 4:5] * dc[i, 5:6]) ** 0.5 | ||||
|                     weights = (dc[i, 4:5] * dc[i, 5:6]) ** 2 | ||||
|                     dc[0, :4] = (weights * dc[i, :4]).sum(0) / weights.sum() | ||||
|                     det_max.append(dc[:1]) | ||||
|                     dc = dc[iou < nms_thres] | ||||
| 
 | ||||
|                 # Image      Total          P          R        mAP | ||||
|                 #    32       5000      0.635      0.581      0.569 | ||||
|                 #    64       5000       0.63      0.591      0.578 | ||||
|                 #    96       5000       0.66       0.63       0.62 | ||||
|                 #   128       5000      0.657      0.631      0.622 | ||||
|                 #  4964       5000      0.632      0.597      0.588  # normal | ||||
|                 #  4964       5000      0.632      0.597      0.588  # squared | ||||
|                 #  4964       5000      0.631      0.597      0.588  # sqrt | ||||
| 
 | ||||
|             if len(det_max) > 0: | ||||
|                 det_max = torch.cat(det_max) | ||||
|  |  | |||
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