Sidi Yang

您好，PIPAL数据集容易出现过拟合问题。我们的比赛经验是，学习率大小和训练epoch数是成反比的，当epoch数过大，会出现较大的drop，这是由于过拟合引起的。所以在训练时我们选择了一个能保证了我们只用训一个epoch就能达到最好的效果的学习率。

直接调用det.detect()替换掉demo.py中的det.feedcap(). 实际上AIDetector_pytorch.py中的类会输出结果，但是内置feedcap调用时出问题会导致空矩阵.

I supppose this is the reason why the performance of the model decreases in the test dataset.

Besides, the cut-off frequency setting in the generate_filter is not resonable. 1/16 of the length of the spectrum is unequal to the code"(row + col) == size // 16". Obviously,...

Thanks for your detailed experiments and reply. Frankly speaking, there are some unknown module parameters in the paper which cause the incomformity in the reproduction. It is a pity that...