updates
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@ -345,7 +345,7 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1)
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v = ((pred[:, 4] > conf_thres) & (class_prob > .3)) # TODO examine arbitrary 0.3 thres here
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v = (pred[:, 4] > (conf_thres * class_prob)) # TODO examine arbitrary 0.3 thres here
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v = v.nonzero().squeeze()
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if len(v.shape) == 0:
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v = v.unsqueeze(0)
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@ -389,6 +389,8 @@ def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
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# Image Total P R mAP
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# 5000 5000 0.627 0.593 0.584
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# 4964 5000 0.629 0.594 0.586 # complete probability sort
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elif nms_style == 'AND': # requires overlap, single boxes erased
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while len(dc) > 1:
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