updates
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@ -501,7 +501,12 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.5):
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# Box (center x, center y, width, height) to (x1, y1, x2, y2)
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pred[:, :4] = xywh2xyxy(pred[:, :4])
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# Detections ordered as (x1y1x2y2, conf, cls)
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# Expand
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expand = False
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if expand:
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i, j = (pred[:, 4:] > conf_thres).nonzero().t()
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pred = torch.cat((pred[i, :4], pred[i, j].unsqueeze(1), j.float().unsqueeze(1)), 1) # (x1y1x2y2, conf, cls)
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else:
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pred = torch.cat((pred[:, :4], conf[i].unsqueeze(1), cls[i].unsqueeze(1).float()), 1)
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# Get detections sorted by decreasing confidence scores
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