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So I am currently going over the ResNet paper and trying to understand the output dimensions of each of the layer, and it seems that I am already stuck on the first layer and its output dimension.

If I understand correctly, the size of output of a particular layer can be calculated as (ORIGINAL_SIZE - FILTER_SIZE) / STRIDE + 1

In the plain example of the ResNet, presented below on the right hand side, they claim they use 224x224 image. Therefore, when I calculate the output dimension of the 7x7 convolution with stride 2, I get

(224 - 7) / 2 + 1 = 109.5, so I round it 110.

However, in the paper they claim that their output size is 112. How would that be possible?

ResNet plain 34-layer

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  • $\begingroup$ 112=224/2 seems to be the correct calculation. $\endgroup$ – Yuval Filmus Mar 9 '18 at 13:37
  • $\begingroup$ @YuvalFilmus so where is the 7x7 convolution? $\endgroup$ – Johhny Bravo Mar 9 '18 at 14:50
  • $\begingroup$ It's probably an implementation detail. Maybe they apply the convolution even outside the boundary of the actual image. It's not a big deal. $\endgroup$ – Yuval Filmus Mar 9 '18 at 14:52
  • $\begingroup$ I am asking as I am trying to understand what happens when remaining size is 1, but there is still a convolution layer with filter size greater than the actual width. $\endgroup$ – Johhny Bravo Mar 9 '18 at 15:13
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I managed to find an official github repository, and they use a padding of size 3, and therefore the output 112.

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