reduce merge limit to 3000
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@ -556,7 +556,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
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boxes, scores = x[:, :4].clone() + c.view(-1, 1) * max_wh, x[:, 4] # boxes (offset by class), scores
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if method == 'merge': # Merge NMS (boxes merged using weighted mean)
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i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
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if n < 1E4: # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
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if n < 3E3: # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
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# weights = (box_iou(boxes, boxes).tril_() > iou_thres) * scores.view(-1, 1) # box weights
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# weights /= weights.sum(0) # normalize
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# x[:, :4] = torch.mm(weights.T, x[:, :4])
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