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@@ -188,7 +188,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
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unique_classes = np.unique(target_cls)
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# Create Precision-Recall curve and compute AP for each class
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pr_score = 0.5 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
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pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
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s = [len(unique_classes), tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
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ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
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for ci, c in enumerate(unique_classes):
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