Sure, the models are same as the models in original paper or in other frameworks (e.g., Keras). However, our Pytorch has a difference data preprocess. Namely, load a image with a range of [0, 1] and then normalise it using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. After preprocessing, the values are in range of [-2.1179, 2.64].
If the feature maps are padded by 0s, passed into a convolutional layer, the boundary pixels of new feature maps could become bigger since the feature maps or weights could be negative. The easy way to check this is to look into max_pool2D indices (especially in low layers), you will find that a lot of indices are on the boundary of feature maps.
It has nothing to do with the model accuracy. I mean that the classification score is contributed by the large boundary values partially. That is not what we expect. Look forward to your reply:grinning:!