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Receptive Field Computation for Deep Models

Table of Contents:

Caffe

Layer Name Input Size Filter Num Filter Size Stride(:Pad) Output Size RF Formula RF Size
conv1 227x227x3 96 11x11 4 55x55x96 (x - 7) / 4 11
pool1 55x55x96 96 3x3 2 27x27x96 (x - 11) / 8 19
conv2 27x27x96 256 5x5 1:2 27x27x256 (x - 43) / 8 51
pool2 27x27x256 256 3x3 2 13x13x256 (x - 51) / 16 67
conv3 13x13x256 384 3x3 1:1 13x13x384 (x - 83) / 16 99
conv4 13x13x384 384 3x3 1:1 13x13x384 (x - 115) / 16 131
conv5 13x13x384 256 3x3 1:1 13x13x256 (x - 147) / 16 163
pool5 13x13x256 256 3x3 2 6x6x256 (x - 163) / 32 195
fc6 6x6x256 4096 6x6 1 1x1x4096 (x - 323) / 32 353
fc7 1x1x4096 4096 1x1 1 1x1x4096 (x - 323) / 32 353
fc8 1x1x4096 1000 1x1 1 1x1x1000 (x - 323) / 32 353

Zeiler-Fergus

Layer Name Input Size Filter Num Filter Size Stride(:Pad) Output Size RF Formula RF Size
conv1 224x224x3 96 7x7 2:1 110x110x96 (x - 5) / 2 7
pool1 110x110x96 96 3x3 2:1 55x55x96 (x - 7) / 4 11
conv2 55x55x96 256 5x5 2 26x26x256 (x - 19) / 8 27
pool2 26x26x256 256 3x3 2:1 13x13x256 (x - 27) / 16 43
conv3 13x13x256 384 3x3 1:1 13x13x384 (x - 59) / 16 75
conv4 13x13x384 384 3x3 1:1 13x13x384 (x - 91) / 16 107
conv5 13x13x384 256 3x3 1:1 13x13x256 (x - 123) / 16 139
pool5 13x13x256 256 3x3 2 6x6x256 (x - 139) / 32 171
fc6 6x6x256 4096 6x6 1 1x1x4096 (x - 299) / 32 331
fc7 1x1x4096 4096 1x1 1 1x1x4096 (x - 299) / 32 331
fc8 1x1x4096 1000 1x1 1 1x1x1000 (x - 299) / 32 331

VGG-16

Layer Name Input Size Filter Num Filter Size Stride(:Pad) Output Size RF Formula RF Size
conv1-1 224x224x3 64 3x3 1:1 224x224x64 (x - 2) / 1 3
conv1-2 224x224x64 64 3x3 1:1 224x224x64 (x - 4) / 1 5
pool1 224x224x64 64 2x2 2 112x112x64 (x - 4) / 2 6
conv2-1 112x112x64 128 3x3 1:1 112x112x128 (x - 8) / 2 10
conv2-2 112x112x128 128 3x3 1:1 112x112x128 (x - 12) / 2 14
pool2 112x112x128 128 2x2 2 56x56x128 (x - 12) / 4 16
conv3-1 56x56x128 256 3x3 1:1 56x56x256 (x - 20) / 4 24
conv3-2 56x56x256 256 3x3 1:1 56x56x256 (x - 28) / 4 32
conv3-3 56x56x256 256 3x3 1:1 56x56x256 (x - 36) / 4 40
pool3 56x56x256 256 2x2 2 28x28x256 (x - 36) / 8 44
conv4-1 28x28x256 512 3x3 1:1 28x28x512 (x - 52) / 8 60
conv4-2 28x28x512 512 3x3 1:1 28x28x512 (x - 68) / 8 76
conv4-3 28x28x512 512 3x3 1:1 28x28x512 (x - 84) / 8 92
pool4 28x28x512 512 2x2 2 14x14x512 (x - 84) / 16 100
conv5-1 14x14x512 512 3x3 1:1 14x14x512 (x - 116) / 16 132
conv5-2 14x14x512 512 3x3 1:1 14x14x512 (x - 148) / 16 164
conv5-3 14x14x512 512 3x3 1:1 14x14x512 (x - 180) / 16 196
pool5 14x14x512 512 2x2 2 7x7x512 (x - 180) / 32 212
fc6 7x7x512 4096 7x7 1 1x1x4096 (x - 372) / 32 404
fc7 1x1x4096 4096 1x1 1 1x1x4096 (x - 372) / 32 404
fc8 1x1x4096 1000 1x1 1 1x1x1000 (x - 372) / 32 404
to be continued.
19 January 2015
deep learning
#Deep Learning #Receptive Field #Caffe