Comments (3)
Hi @John1231983 We follow ResNet's design on both datasets. https://github.com/facebook/fb.resnet.torch
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Thanks. I knew it. In your opinion, do you have any explanation?
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In ImageNet we need to downsample the image twice (so that the image becomes of 56x56 size) before entering the first dense block, this is through one stride convolution and one max pooling.
In CIFAR dataset, the input image and the first dense block feature maps are of the same size (32x32), the first convolution is just a conventional design so that we don't include raw input image in the first dense block.
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