ResNet Receptive Field Size

rfs.md

layer	resnet18	resnet34	resnet50	resnet101
conv1	7	7	7	7
maxpool	11	11	11	11
layer1	43	59	35	35
layer2	99	179	91	91
layer3	211	547	267	811
layer4	435	899	427	971

This is calculated on ResNet-V2 which does a stride 2 convolution on 3x3 kernel.

@kriskorrel-cw In my computation, the stride 2 is on 3x3 conv. This maybe the reason that leads to the difference if you compute the RF on the network do stride on conv 1x1.

@kriskorrel-cw In my computation, the stride 2 is on 3x3 conv. This maybe the reason that leads to the difference if you compute the RF on the network do stride on conv 1x1.

Thanks for your response! I think that this difference in interpretation of architecture indeed explains the difference between our calculated RF's. With your assumption, I indeed get 91 and 267 for resnet50 layer2 and layer3 respectively, and assume that this will hold for all other networks/layers.

Though I do think that stride 2 convolution is performed on the first 1x1 convolution of each stage, rather than on the first 3x3 convolution of each stage. This is based on code inspection of the keras-applications implementation.
In lines L236-L254 we see that each stage starts with a conv_block followed by multiple identity_blocks.
And in L114 we see that the first layer in conv_block is a 1x1 convolution with stride (2, 2). The first (or second, depending on indexing :)) stage is the only exception to this, as this already follows 3x3 max-pooling with stride 2.

@kriskorrel-cw Stride 2 on Conv1x1 is referred to ResNet-v1. And Stride 2 on Conv3x3 is V2 which performs better than v1.

Actually, there are plenty of versions of resnet, i.e. https://arxiv.org/abs/1812.01187 FYI.

@kriskorrel-cw Stride 2 on Conv1x1 is referred to ResNet-v1. And Stride 2 on Conv3x3 is V2 which performs better than v1.

Actually, there are plenty of versions of resnet, i.e. https://arxiv.org/abs/1812.01187 FYI.

Ah I see. Yeah I was basing my calculations on the original version. I don't know what the RF is for other versions.