updates
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				|  | @ -294,7 +294,7 @@ def build_targets(target, anchor_wh, nA, nC, nG): | |||
| 
 | ||||
| def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4): | ||||
|     """ | ||||
|     Removes detections with lower object confidence score than 'conf_thres' and performs | ||||
|     Removes detections with lower object confidence score than 'conf_thres' | ||||
|     Non-Maximum Suppression to further filter detections. | ||||
|     Returns detections with shape: | ||||
|         (x1, y1, x2, y2, object_conf, class_score, class_pred) | ||||
|  | @ -369,7 +369,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4): | |||
|         if prediction.is_cuda: | ||||
|             unique_labels = unique_labels.cuda(prediction.device) | ||||
| 
 | ||||
|         nms_style = 'OR'  # 'AND' or 'OR' (classical) | ||||
|         nms_style = 'OR'  # 'AND', 'OR' (classical), 'MERGE' (experimental) | ||||
|         for c in unique_labels: | ||||
|             # Get the detections with the particular class | ||||
|             det_class = detections[detections[:, -1] == c] | ||||
|  |  | |||
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