实验结果的可视化问题

[archyin]

跑了一下test，出来的结果是这样的， 有那种二值化的结果图，或者把异常区域圈出来的吗？

[ByChelsea]

Hi! You can binarize the anomaly map by selecting a threshold. I recommend using the optimal threshold obtained when calculating the F1-max score:

precisions, recalls, thresholds = precision_recall_curve(label, predict)
f1_scores = (2 * precisions * recalls) / (precisions + recalls)
f1_max = np.max(f1_scores[np.isfinite(f1_scores)])
f1_max_index = np.argmax(f1_scores[np.isfinite(f1_scores)])

where f1_max represents the F1-max score, and thresholds[f1_max_index] is the corresponding optimal threshold.

[archyin]

Hi! You can binarize the anomaly map by selecting a threshold. I recommend using the optimal threshold obtained when calculating the F1-max score:

precisions, recalls, thresholds = precision_recall_curve(label, predict)
f1_scores = (2 * precisions * recalls) / (precisions + recalls)
f1_max = np.max(f1_scores[np.isfinite(f1_scores)])
f1_max_index = np.argmax(f1_scores[np.isfinite(f1_scores)])

where f1_max represents the F1-max score, and thresholds[f1_max_index] is the corresponding optimal threshold.

每类图像集用同一个阈值吗？

[ByChelsea]

Yes, this is a viable approach.