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Pre-computed receptive field parameters

Table with results

The table below presents the receptive field parameters and cost (in terms of floating point operations — FLOPs) for several popular convolutional neural networks and their end-points. These are computed using the models from the TF-Slim repository, by using the rf_benchmark script.

Questions? See the FAQ.

CNN resolution end-point FLOPs (Billion) RF effective stride effective padding
alexnet_v2 None alexnet_v2/conv1 None 11 4 0
alexnet_v2 None alexnet_v2/pool1 None 19 8 0
alexnet_v2 None alexnet_v2/conv2 None 51 8 16
alexnet_v2 None alexnet_v2/conv3 None 99 16 32
alexnet_v2 None alexnet_v2/conv4 None 131 16 48
alexnet_v2 None alexnet_v2/conv5 None 163 16 64
alexnet_v2 None alexnet_v2/pool5 None 195 32 64
alexnet_v2 224 alexnet_v2/conv1 0.136 11 4 0
alexnet_v2 224 alexnet_v2/pool1 0.136 19 8 0
alexnet_v2 224 alexnet_v2/conv2 0.552 51 8 16
alexnet_v2 224 alexnet_v2/conv3 0.743 99 16 32
alexnet_v2 224 alexnet_v2/conv4 1.125 131 16 48
alexnet_v2 224 alexnet_v2/conv5 1.380 163 16 64
alexnet_v2 224 alexnet_v2/pool5 1.380 195 32 64
alexnet_v2 321 alexnet_v2/conv1 0.283 11 4 0
alexnet_v2 321 alexnet_v2/pool1 0.284 19 8 0
alexnet_v2 321 alexnet_v2/conv2 1.171 51 8 16
alexnet_v2 321 alexnet_v2/conv3 1.602 99 16 32
alexnet_v2 321 alexnet_v2/conv4 2.462 131 16 48
alexnet_v2 321 alexnet_v2/conv5 3.036 163 16 64
alexnet_v2 321 alexnet_v2/pool5 3.036 195 32 64
vgg_a None vgg_a/conv1/conv1_1 None 3 1 1
vgg_a None vgg_a/pool1 None 4 2 1
vgg_a None vgg_a/conv2/conv2_1 None 8 2 3
vgg_a None vgg_a/pool2 None 10 4 3
vgg_a None vgg_a/conv3/conv3_1 None 18 4 7
vgg_a None vgg_a/conv3/conv3_2 None 26 4 11
vgg_a None vgg_a/pool3 None 30 8 11
vgg_a None vgg_a/conv4/conv4_1 None 46 8 19
vgg_a None vgg_a/conv4/conv4_2 None 62 8 27
vgg_a None vgg_a/pool4 None 70 16 27
vgg_a None vgg_a/conv5/conv5_1 None 102 16 43
vgg_a None vgg_a/conv5/conv5_2 None 134 16 59
vgg_a None vgg_a/pool5 None 150 32 59
vgg_a 224 vgg_a/conv1/conv1_1 0.177 3 1 1
vgg_a 224 vgg_a/pool1 0.180 4 2 1
vgg_a 224 vgg_a/conv2/conv2_1 2.031 8 2 3
vgg_a 224 vgg_a/pool2 2.033 10 4 3
vgg_a 224 vgg_a/conv3/conv3_1 3.883 18 4 7
vgg_a 224 vgg_a/conv3/conv3_2 7.583 26 4 11
vgg_a 224 vgg_a/pool3 7.584 30 8 11
vgg_a 224 vgg_a/conv4/conv4_1 9.434 46 8 19
vgg_a 224 vgg_a/conv4/conv4_2 13.134 62 8 27
vgg_a 224 vgg_a/pool4 13.134 70 16 27
vgg_a 224 vgg_a/conv5/conv5_1 14.059 102 16 43
vgg_a 224 vgg_a/conv5/conv5_2 14.984 134 16 59
vgg_a 224 vgg_a/pool5 14.984 150 32 59
vgg_a 321 vgg_a/conv1/conv1_1 0.363 3 1 1
vgg_a 321 vgg_a/pool1 0.369 4 2 1
vgg_a 321 vgg_a/conv2/conv2_1 4.147 8 2 3
vgg_a 321 vgg_a/pool2 4.151 10 4 3
vgg_a 321 vgg_a/conv3/conv3_1 7.927 18 4 7
vgg_a 321 vgg_a/conv3/conv3_2 15.479 26 4 11
vgg_a 321 vgg_a/pool3 15.480 30 8 11
vgg_a 321 vgg_a/conv4/conv4_1 19.256 46 8 19
vgg_a 321 vgg_a/conv4/conv4_2 26.806 62 8 27
vgg_a 321 vgg_a/pool4 26.807 70 16 27
vgg_a 321 vgg_a/conv5/conv5_1 28.695 102 16 43
vgg_a 321 vgg_a/conv5/conv5_2 30.583 134 16 59
vgg_a 321 vgg_a/pool5 30.583 150 32 59
vgg_16 None vgg_16/conv1/conv1_1 None 3 1 1
vgg_16 None vgg_16/pool1 None 6 2 2
vgg_16 None vgg_16/conv2/conv2_1 None 10 2 4
vgg_16 None vgg_16/pool2 None 16 4 6
vgg_16 None vgg_16/conv3/conv3_1 None 24 4 10
vgg_16 None vgg_16/conv3/conv3_2 None 32 4 14
vgg_16 None vgg_16/pool3 None 44 8 18
vgg_16 None vgg_16/conv4/conv4_1 None 60 8 26
vgg_16 None vgg_16/conv4/conv4_2 None 76 8 34
vgg_16 None vgg_16/pool4 None 100 16 42
vgg_16 None vgg_16/conv5/conv5_1 None 132 16 58
vgg_16 None vgg_16/conv5/conv5_2 None 164 16 74
vgg_16 None vgg_16/pool5 None 212 32 90
vgg_16 224 vgg_16/conv1/conv1_1 0.177 3 1 1
vgg_16 224 vgg_16/pool1 3.882 6 2 2
vgg_16 224 vgg_16/conv2/conv2_1 5.734 10 2 4
vgg_16 224 vgg_16/pool2 9.436 16 4 6
vgg_16 224 vgg_16/conv3/conv3_1 11.287 24 4 10
vgg_16 224 vgg_16/conv3/conv3_2 14.987 32 4 14
vgg_16 224 vgg_16/pool3 18.688 44 8 18
vgg_16 224 vgg_16/conv4/conv4_1 20.538 60 8 26
vgg_16 224 vgg_16/conv4/conv4_2 24.238 76 8 34
vgg_16 224 vgg_16/pool4 27.938 100 16 42
vgg_16 224 vgg_16/conv5/conv5_1 28.863 132 16 58
vgg_16 224 vgg_16/conv5/conv5_2 29.788 164 16 74
vgg_16 224 vgg_16/pool5 30.713 212 32 90
vgg_16 321 vgg_16/conv1/conv1_1 0.363 3 1 1
vgg_16 321 vgg_16/pool1 7.973 6 2 2
vgg_16 321 vgg_16/conv2/conv2_1 11.751 10 2 4
vgg_16 321 vgg_16/pool2 19.307 16 4 6
vgg_16 321 vgg_16/conv3/conv3_1 23.084 24 4 10
vgg_16 321 vgg_16/conv3/conv3_2 30.635 32 4 14
vgg_16 321 vgg_16/pool3 38.188 44 8 18
vgg_16 321 vgg_16/conv4/conv4_1 41.964 60 8 26
vgg_16 321 vgg_16/conv4/conv4_2 49.514 76 8 34
vgg_16 321 vgg_16/pool4 57.066 100 16 42
vgg_16 321 vgg_16/conv5/conv5_1 58.954 132 16 58
vgg_16 321 vgg_16/conv5/conv5_2 60.841 164 16 74
vgg_16 321 vgg_16/pool5 62.729 212 32 90
inception_v2 None Conv2d_1a_7x7 None 7 2 None
inception_v2 None MaxPool_2a_3x3 None 11 4 None
inception_v2 None Conv2d_2b_1x1 None 11 4 None
inception_v2 None Conv2d_2c_3x3 None 19 4 None
inception_v2 None MaxPool_3a_3x3 None 27 8 None
inception_v2 None Mixed_3b None 59 8 None
inception_v2 None Mixed_3c None 91 8 None
inception_v2 None Mixed_4a None 123 16 None
inception_v2 None Mixed_4b None 187 16 None
inception_v2 None Mixed_4c None 251 16 None
inception_v2 None Mixed_4d None 315 16 None
inception_v2 None Mixed_4e None 379 16 None
inception_v2 None Mixed_5a None 443 32 None
inception_v2 None Mixed_5b None 571 32 None
inception_v2 None Mixed_5c None 699 32 None
inception_v2 224 Conv2d_1a_7x7 0.069 7 2 2
inception_v2 224 MaxPool_2a_3x3 0.071 11 4 2
inception_v2 224 Conv2d_2b_1x1 0.097 11 4 2
inception_v2 224 Conv2d_2c_3x3 0.791 19 4 6
inception_v2 224 MaxPool_3a_3x3 0.792 27 8 6
inception_v2 224 Mixed_3b 1.136 59 8 22
inception_v2 224 Mixed_3c 1.544 91 8 38
inception_v2 224 Mixed_4a 1.833 123 16 46
inception_v2 224 Mixed_4b 2.073 187 16 78
inception_v2 224 Mixed_4c 2.334 251 16 110
inception_v2 224 Mixed_4d 2.686 315 16 142
inception_v2 224 Mixed_4e 3.120 379 16 174
inception_v2 224 Mixed_5a 3.446 443 32 190
inception_v2 224 Mixed_5b 3.660 571 32 254
inception_v2 224 Mixed_5c 3.883 699 32 318
inception_v2 321 Conv2d_1a_7x7 0.142 7 2 3
inception_v2 321 MaxPool_2a_3x3 0.146 11 4 5
inception_v2 321 Conv2d_2b_1x1 0.200 11 4 5
inception_v2 321 Conv2d_2c_3x3 1.653 19 4 9
inception_v2 321 MaxPool_3a_3x3 1.656 27 8 13
inception_v2 321 Mixed_3b 2.393 59 8 29
inception_v2 321 Mixed_3c 3.268 91 8 45
inception_v2 321 Mixed_4a 3.898 123 16 61
inception_v2 321 Mixed_4b 4.438 187 16 93
inception_v2 321 Mixed_4c 5.025 251 16 125
inception_v2 321 Mixed_4d 5.817 315 16 157
inception_v2 321 Mixed_4e 6.795 379 16 189
inception_v2 321 Mixed_5a 7.545 443 32 221
inception_v2 321 Mixed_5b 8.073 571 32 285
inception_v2 321 Mixed_5c 8.626 699 32 349
inception_v2-no-separable-conv None Conv2d_1a_7x7 None 7 2 None
inception_v2-no-separable-conv None MaxPool_2a_3x3 None 11 4 None
inception_v2-no-separable-conv None Conv2d_2b_1x1 None 11 4 None
inception_v2-no-separable-conv None Conv2d_2c_3x3 None 19 4 None
inception_v2-no-separable-conv None MaxPool_3a_3x3 None 27 8 None
inception_v2-no-separable-conv None Mixed_3b None 59 8 None
inception_v2-no-separable-conv None Mixed_3c None 91 8 None
inception_v2-no-separable-conv None Mixed_4a None 123 16 None
inception_v2-no-separable-conv None Mixed_4b None 187 16 None
inception_v2-no-separable-conv None Mixed_4c None 251 16 None
inception_v2-no-separable-conv None Mixed_4d None 315 16 None
inception_v2-no-separable-conv None Mixed_4e None 379 16 None
inception_v2-no-separable-conv None Mixed_5a None 443 32 None
inception_v2-no-separable-conv None Mixed_5b None 571 32 None
inception_v2-no-separable-conv None Mixed_5c None 699 32 None
inception_v2-no-separable-conv 224 Conv2d_1a_7x7 0.237 7 2 2
inception_v2-no-separable-conv 224 MaxPool_2a_3x3 0.239 11 4 2
inception_v2-no-separable-conv 224 Conv2d_2b_1x1 0.265 11 4 2
inception_v2-no-separable-conv 224 Conv2d_2c_3x3 0.959 19 4 6
inception_v2-no-separable-conv 224 MaxPool_3a_3x3 0.960 27 8 6
inception_v2-no-separable-conv 224 Mixed_3b 1.304 59 8 22
inception_v2-no-separable-conv 224 Mixed_3c 1.712 91 8 38
inception_v2-no-separable-conv 224 Mixed_4a 2.001 123 16 46
inception_v2-no-separable-conv 224 Mixed_4b 2.241 187 16 78
inception_v2-no-separable-conv 224 Mixed_4c 2.502 251 16 110
inception_v2-no-separable-conv 224 Mixed_4d 2.854 315 16 142
inception_v2-no-separable-conv 224 Mixed_4e 3.288 379 16 174
inception_v2-no-separable-conv 224 Mixed_5a 3.614 443 32 190
inception_v2-no-separable-conv 224 Mixed_5b 3.828 571 32 254
inception_v2-no-separable-conv 224 Mixed_5c 4.051 699 32 318
inception_v2-no-separable-conv 321 Conv2d_1a_7x7 0.489 7 2 3
inception_v2-no-separable-conv 321 MaxPool_2a_3x3 0.493 11 4 5
inception_v2-no-separable-conv 321 Conv2d_2b_1x1 0.547 11 4 5
inception_v2-no-separable-conv 321 Conv2d_2c_3x3 2.000 19 4 9
inception_v2-no-separable-conv 321 MaxPool_3a_3x3 2.003 27 8 13
inception_v2-no-separable-conv 321 Mixed_3b 2.740 59 8 29
inception_v2-no-separable-conv 321 Mixed_3c 3.615 91 8 45
inception_v2-no-separable-conv 321 Mixed_4a 4.246 123 16 61
inception_v2-no-separable-conv 321 Mixed_4b 4.785 187 16 93
inception_v2-no-separable-conv 321 Mixed_4c 5.373 251 16 125
inception_v2-no-separable-conv 321 Mixed_4d 6.164 315 16 157
inception_v2-no-separable-conv 321 Mixed_4e 7.142 379 16 189
inception_v2-no-separable-conv 321 Mixed_5a 7.892 443 32 221
inception_v2-no-separable-conv 321 Mixed_5b 8.421 571 32 285
inception_v2-no-separable-conv 321 Mixed_5c 8.973 699 32 349
inception_v3 None Conv2d_1a_3x3 None 3 2 0
inception_v3 None Conv2d_2a_3x3 None 7 2 0
inception_v3 None Conv2d_2b_3x3 None 11 2 2
inception_v3 None MaxPool_3a_3x3 None 15 4 2
inception_v3 None Conv2d_3b_1x1 None 15 4 2
inception_v3 None Conv2d_4a_3x3 None 23 4 2
inception_v3 None MaxPool_5a_3x3 None 31 8 2
inception_v3 None Mixed_5b None 63 8 18
inception_v3 None Mixed_5c None 95 8 34
inception_v3 None Mixed_5d None 127 8 50
inception_v3 None Mixed_6a None 159 16 58
inception_v3 None Mixed_6b None 351 16 154
inception_v3 None Mixed_6c None 543 16 250
inception_v3 None Mixed_6d None 735 16 346
inception_v3 None Mixed_6e None 927 16 442
inception_v3 None Mixed_7a None 1055 32 490
inception_v3 None Mixed_7b None 1183 32 554
inception_v3 None Mixed_7c None 1311 32 618
inception_v3 224 Conv2d_1a_3x3 0.022 3 2 0
inception_v3 224 Conv2d_2a_3x3 0.241 7 2 0
inception_v3 224 Conv2d_2b_3x3 0.680 11 2 2
inception_v3 224 MaxPool_3a_3x3 0.681 15 4 2
inception_v3 224 Conv2d_3b_1x1 0.712 15 4 2
inception_v3 224 Conv2d_4a_3x3 1.460 23 4 2
inception_v3 224 MaxPool_5a_3x3 1.461 31 8 2
inception_v3 224 Mixed_5b 1.781 63 8 18
inception_v3 224 Mixed_5c 2.128 95 8 34
inception_v3 224 Mixed_5d 2.485 127 8 50
inception_v3 224 Mixed_6a 2.889 159 16 58
inception_v3 224 Mixed_6b 3.263 351 16 154
inception_v3 224 Mixed_6c 3.750 543 16 250
inception_v3 224 Mixed_6d 4.237 735 16 346
inception_v3 224 Mixed_6e 4.854 927 16 442
inception_v3 224 Mixed_7a 5.132 1055 32 490
inception_v3 224 Mixed_7b 5.385 1183 32 554
inception_v3 224 Mixed_7c 5.689 1311 32 618
inception_v3 321 Conv2d_1a_3x3 0.045 3 2 0
inception_v3 321 Conv2d_2a_3x3 0.506 7 2 0
inception_v3 321 Conv2d_2b_3x3 1.428 11 2 2
inception_v3 321 MaxPool_3a_3x3 1.431 15 4 2
inception_v3 321 Conv2d_3b_1x1 1.494 15 4 2
inception_v3 321 Conv2d_4a_3x3 3.092 23 4 2
inception_v3 321 MaxPool_5a_3x3 3.095 31 8 2
inception_v3 321 Mixed_5b 3.796 63 8 18
inception_v3 321 Mixed_5c 4.557 95 8 34
inception_v3 321 Mixed_5d 5.339 127 8 50
inception_v3 321 Mixed_6a 6.241 159 16 58
inception_v3 321 Mixed_6b 7.082 351 16 154
inception_v3 321 Mixed_6c 8.178 543 16 250
inception_v3 321 Mixed_6d 9.275 735 16 346
inception_v3 321 Mixed_6e 10.663 927 16 442
inception_v3 321 Mixed_7a 11.303 1055 32 490
inception_v3 321 Mixed_7b 11.948 1183 32 554
inception_v3 321 Mixed_7c 12.727 1311 32 618
inception_v4 None Conv2d_1a_3x3 None 3 2 0
inception_v4 None Conv2d_2a_3x3 None 7 2 0
inception_v4 None Conv2d_2b_3x3 None 11 2 2
inception_v4 None Mixed_3a None 15 4 2
inception_v4 None Mixed_4a None 47 4 14
inception_v4 None Mixed_5a None 55 8 14
inception_v4 None Mixed_5b None 87 8 30
inception_v4 None Mixed_5c None 119 8 46
inception_v4 None Mixed_5d None 151 8 62
inception_v4 None Mixed_5e None 183 8 78
inception_v4 None Mixed_6a None 215 16 86
inception_v4 None Mixed_6b None 407 16 182
inception_v4 None Mixed_6c None 599 16 278
inception_v4 None Mixed_6d None 791 16 374
inception_v4 None Mixed_6e None 983 16 470
inception_v4 None Mixed_6f None 1175 16 566
inception_v4 None Mixed_6g None 1367 16 662
inception_v4 None Mixed_6h None 1559 16 758
inception_v4 None Mixed_7a None 1687 32 806
inception_v4 None Mixed_7b None 1815 32 870
inception_v4 None Mixed_7c None 1943 32 934
inception_v4 None Mixed_7d None 2071 32 998
inception_v4 224 Conv2d_1a_3x3 0.022 3 2 0
inception_v4 224 Conv2d_2a_3x3 0.241 7 2 0
inception_v4 224 Conv2d_2b_3x3 0.680 11 2 2
inception_v4 224 Mixed_3a 1.004 15 4 2
inception_v4 224 Mixed_4a 2.057 47 4 14
inception_v4 224 Mixed_5a 2.473 55 8 14
inception_v4 224 Mixed_5b 2.871 87 8 30
inception_v4 224 Mixed_5c 3.269 119 8 46
inception_v4 224 Mixed_5d 3.668 151 8 62
inception_v4 224 Mixed_5e 4.066 183 8 78
inception_v4 224 Mixed_6a 5.173 215 16 86
inception_v4 224 Mixed_6b 6.019 407 16 182
inception_v4 224 Mixed_6c 6.865 599 16 278
inception_v4 224 Mixed_6d 7.711 791 16 374
inception_v4 224 Mixed_6e 8.557 983 16 470
inception_v4 224 Mixed_6f 9.403 1175 16 566
inception_v4 224 Mixed_6g 10.249 1367 16 662
inception_v4 224 Mixed_6h 11.095 1559 16 758
inception_v4 224 Mixed_7a 11.588 1687 32 806
inception_v4 224 Mixed_7b 11.815 1815 32 870
inception_v4 224 Mixed_7c 12.043 1943 32 934
inception_v4 224 Mixed_7d 12.271 2071 32 998
inception_v4 321 Conv2d_1a_3x3 0.045 3 2 0
inception_v4 321 Conv2d_2a_3x3 0.506 7 2 0
inception_v4 321 Conv2d_2b_3x3 1.428 11 2 2
inception_v4 321 Mixed_3a 2.105 15 4 2
inception_v4 321 Mixed_4a 4.332 47 4 14
inception_v4 321 Mixed_5a 5.243 55 8 14
inception_v4 321 Mixed_5b 6.115 87 8 30
inception_v4 321 Mixed_5c 6.987 119 8 46
inception_v4 321 Mixed_5d 7.859 151 8 62
inception_v4 321 Mixed_5e 8.731 183 8 78
inception_v4 321 Mixed_6a 11.189 215 16 86
inception_v4 321 Mixed_6b 13.092 407 16 182
inception_v4 321 Mixed_6c 14.996 599 16 278
inception_v4 321 Mixed_6d 16.899 791 16 374
inception_v4 321 Mixed_6e 18.802 983 16 470
inception_v4 321 Mixed_6f 20.706 1175 16 566
inception_v4 321 Mixed_6g 22.609 1367 16 662
inception_v4 321 Mixed_6h 24.513 1559 16 758
inception_v4 321 Mixed_7a 25.640 1687 32 806
inception_v4 321 Mixed_7b 26.223 1815 32 870
inception_v4 321 Mixed_7c 26.807 1943 32 934
inception_v4 321 Mixed_7d 27.390 2071 32 998
inception_resnet_v2 None Conv2d_1a_3x3 None 3 2 0
inception_resnet_v2 None Conv2d_2a_3x3 None 7 2 0
inception_resnet_v2 None Conv2d_2b_3x3 None 11 2 2
inception_resnet_v2 None MaxPool_3a_3x3 None 15 4 2
inception_resnet_v2 None Conv2d_3b_1x1 None 15 4 2
inception_resnet_v2 None Conv2d_4a_3x3 None 23 4 2
inception_resnet_v2 None MaxPool_5a_3x3 None 31 8 2
inception_resnet_v2 None Mixed_5b None 63 8 18
inception_resnet_v2 None Mixed_6a None 415 16 186
inception_resnet_v2 None PreAuxLogits None 2335 16 1146
inception_resnet_v2 None Mixed_7a None 2399 32 1162
inception_resnet_v2 None Conv2d_7b_1x1 None 3039 32 1482
inception_resnet_v2 224 Conv2d_1a_3x3 0.022 3 2 0
inception_resnet_v2 224 Conv2d_2a_3x3 0.241 7 2 0
inception_resnet_v2 224 Conv2d_2b_3x3 0.680 11 2 2
inception_resnet_v2 224 MaxPool_3a_3x3 0.681 15 4 2
inception_resnet_v2 224 Conv2d_3b_1x1 0.712 15 4 2
inception_resnet_v2 224 Conv2d_4a_3x3 1.460 23 4 2
inception_resnet_v2 224 MaxPool_5a_3x3 1.461 31 8 2
inception_resnet_v2 224 Mixed_5b 1.796 63 8 18
inception_resnet_v2 224 Mixed_6a 4.745 415 16 186
inception_resnet_v2 224 PreAuxLogits 11.230 2335 16 1146
inception_resnet_v2 224 Mixed_7a 11.781 2399 32 1162
inception_resnet_v2 224 Conv2d_7b_1x1 12.958 3039 32 1482
inception_resnet_v2 321 Conv2d_1a_3x3 0.045 3 2 0
inception_resnet_v2 321 Conv2d_2a_3x3 0.506 7 2 0
inception_resnet_v2 321 Conv2d_2b_3x3 1.428 11 2 2
inception_resnet_v2 321 MaxPool_3a_3x3 1.431 15 4 2
inception_resnet_v2 321 Conv2d_3b_1x1 1.494 15 4 2
inception_resnet_v2 321 Conv2d_4a_3x3 3.092 23 4 2
inception_resnet_v2 321 MaxPool_5a_3x3 3.095 31 8 2
inception_resnet_v2 321 Mixed_5b 3.829 63 8 18
inception_resnet_v2 321 Mixed_6a 10.323 415 16 186
inception_resnet_v2 321 PreAuxLogits 24.913 2335 16 1146
inception_resnet_v2 321 Mixed_7a 26.190 2399 32 1162
inception_resnet_v2 321 Conv2d_7b_1x1 29.203 3039 32 1482
inception_resnet_v2-same None Conv2d_1a_3x3 None 3 2 None
inception_resnet_v2-same None Conv2d_2a_3x3 None 7 2 None
inception_resnet_v2-same None Conv2d_2b_3x3 None 11 2 None
inception_resnet_v2-same None MaxPool_3a_3x3 None 15 4 None
inception_resnet_v2-same None Conv2d_3b_1x1 None 15 4 None
inception_resnet_v2-same None Conv2d_4a_3x3 None 23 4 None
inception_resnet_v2-same None MaxPool_5a_3x3 None 31 8 None
inception_resnet_v2-same None Mixed_5b None 63 8 None
inception_resnet_v2-same None Mixed_6a None 415 16 None
inception_resnet_v2-same None PreAuxLogits None 2335 16 None
inception_resnet_v2-same None Mixed_7a None 2399 32 None
inception_resnet_v2-same None Conv2d_7b_1x1 None 3039 32 None
inception_resnet_v2-same 224 Conv2d_1a_3x3 0.022 3 2 0
inception_resnet_v2-same 224 Conv2d_2a_3x3 0.254 7 2 2
inception_resnet_v2-same 224 Conv2d_2b_3x3 0.717 11 2 4
inception_resnet_v2-same 224 MaxPool_3a_3x3 0.719 15 4 4
inception_resnet_v2-same 224 Conv2d_3b_1x1 0.751 15 4 4
inception_resnet_v2-same 224 Conv2d_4a_3x3 1.619 23 4 8
inception_resnet_v2-same 224 MaxPool_5a_3x3 1.620 31 8 8
inception_resnet_v2-same 224 Mixed_5b 2.041 63 8 24
inception_resnet_v2-same 224 Mixed_6a 5.801 415 16 192
inception_resnet_v2-same 224 PreAuxLogits 14.627 2335 16 1152
inception_resnet_v2-same 224 Mixed_7a 15.449 2399 32 1168
inception_resnet_v2-same 224 Conv2d_7b_1x1 17.755 3039 32 1488
inception_resnet_v2-same 321 Conv2d_1a_3x3 0.046 3 2 1
inception_resnet_v2-same 321 Conv2d_2a_3x3 0.524 7 2 3
inception_resnet_v2-same 321 Conv2d_2b_3x3 1.481 11 2 5
inception_resnet_v2-same 321 MaxPool_3a_3x3 1.485 15 4 7
inception_resnet_v2-same 321 Conv2d_3b_1x1 1.553 15 4 7
inception_resnet_v2-same 321 Conv2d_4a_3x3 3.368 23 4 11
inception_resnet_v2-same 321 MaxPool_5a_3x3 3.371 31 8 15
inception_resnet_v2-same 321 Mixed_5b 4.273 63 8 31
inception_resnet_v2-same 321 Mixed_6a 12.419 415 16 207
inception_resnet_v2-same 321 PreAuxLogits 32.278 2335 16 1167
inception_resnet_v2-same 321 Mixed_7a 34.177 2399 32 1199
inception_resnet_v2-same 321 Conv2d_7b_1x1 39.873 3039 32 1519
mobilenet_v1 None Conv2d_0 None 3 2 None
mobilenet_v1 None Conv2d_1_pointwise None 7 2 None
mobilenet_v1 None Conv2d_2_pointwise None 11 4 None
mobilenet_v1 None Conv2d_3_pointwise None 19 4 None
mobilenet_v1 None Conv2d_4_pointwise None 27 8 None
mobilenet_v1 None Conv2d_5_pointwise None 43 8 None
mobilenet_v1 None Conv2d_6_pointwise None 59 16 None
mobilenet_v1 None Conv2d_7_pointwise None 91 16 None
mobilenet_v1 None Conv2d_8_pointwise None 123 16 None
mobilenet_v1 None Conv2d_9_pointwise None 155 16 None
mobilenet_v1 None Conv2d_10_pointwise None 187 16 None
mobilenet_v1 None Conv2d_11_pointwise None 219 16 None
mobilenet_v1 None Conv2d_12_pointwise None 251 32 None
mobilenet_v1 None Conv2d_13_pointwise None 315 32 None
mobilenet_v1 224 Conv2d_0 0.022 3 2 0
mobilenet_v1 224 Conv2d_1_pointwise 0.082 7 2 2
mobilenet_v1 224 Conv2d_2_pointwise 0.137 11 4 2
mobilenet_v1 224 Conv2d_3_pointwise 0.248 19 4 6
mobilenet_v1 224 Conv2d_4_pointwise 0.302 27 8 6
mobilenet_v1 224 Conv2d_5_pointwise 0.409 43 8 14
mobilenet_v1 224 Conv2d_6_pointwise 0.461 59 16 14
mobilenet_v1 224 Conv2d_7_pointwise 0.566 91 16 30
mobilenet_v1 224 Conv2d_8_pointwise 0.671 123 16 46
mobilenet_v1 224 Conv2d_9_pointwise 0.775 155 16 62
mobilenet_v1 224 Conv2d_10_pointwise 0.880 187 16 78
mobilenet_v1 224 Conv2d_11_pointwise 0.985 219 16 94
mobilenet_v1 224 Conv2d_12_pointwise 1.037 251 32 94
mobilenet_v1 224 Conv2d_13_pointwise 1.140 315 32 126
mobilenet_v1 321 Conv2d_0 0.046 3 2 1
mobilenet_v1 321 Conv2d_1_pointwise 0.169 7 2 3
mobilenet_v1 321 Conv2d_2_pointwise 0.286 11 4 5
mobilenet_v1 321 Conv2d_3_pointwise 0.517 19 4 9
mobilenet_v1 321 Conv2d_4_pointwise 0.632 27 8 13
mobilenet_v1 321 Conv2d_5_pointwise 0.861 43 8 21
mobilenet_v1 321 Conv2d_6_pointwise 0.979 59 16 29
mobilenet_v1 321 Conv2d_7_pointwise 1.215 91 16 45
mobilenet_v1 321 Conv2d_8_pointwise 1.450 123 16 61
mobilenet_v1 321 Conv2d_9_pointwise 1.686 155 16 77
mobilenet_v1 321 Conv2d_10_pointwise 1.922 187 16 93
mobilenet_v1 321 Conv2d_11_pointwise 2.158 219 16 109
mobilenet_v1 321 Conv2d_12_pointwise 2.286 251 32 125
mobilenet_v1 321 Conv2d_13_pointwise 2.542 315 32 157
mobilenet_v1_075 None Conv2d_0 None 3 2 None
mobilenet_v1_075 None Conv2d_1_pointwise None 7 2 None
mobilenet_v1_075 None Conv2d_2_pointwise None 11 4 None
mobilenet_v1_075 None Conv2d_3_pointwise None 19 4 None
mobilenet_v1_075 None Conv2d_4_pointwise None 27 8 None
mobilenet_v1_075 None Conv2d_5_pointwise None 43 8 None
mobilenet_v1_075 None Conv2d_6_pointwise None 59 16 None
mobilenet_v1_075 None Conv2d_7_pointwise None 91 16 None
mobilenet_v1_075 None Conv2d_8_pointwise None 123 16 None
mobilenet_v1_075 None Conv2d_9_pointwise None 155 16 None
mobilenet_v1_075 None Conv2d_10_pointwise None 187 16 None
mobilenet_v1_075 None Conv2d_11_pointwise None 219 16 None
mobilenet_v1_075 None Conv2d_12_pointwise None 251 32 None
mobilenet_v1_075 None Conv2d_13_pointwise None 315 32 None
mobilenet_v1_075 224 Conv2d_0 0.017 3 2 0
mobilenet_v1_075 224 Conv2d_1_pointwise 0.052 7 2 2
mobilenet_v1_075 224 Conv2d_2_pointwise 0.084 11 4 2
mobilenet_v1_075 224 Conv2d_3_pointwise 0.148 19 4 6
mobilenet_v1_075 224 Conv2d_4_pointwise 0.178 27 8 6
mobilenet_v1_075 224 Conv2d_5_pointwise 0.239 43 8 14
mobilenet_v1_075 224 Conv2d_6_pointwise 0.269 59 16 14
mobilenet_v1_075 224 Conv2d_7_pointwise 0.328 91 16 30
mobilenet_v1_075 224 Conv2d_8_pointwise 0.387 123 16 46
mobilenet_v1_075 224 Conv2d_9_pointwise 0.447 155 16 62
mobilenet_v1_075 224 Conv2d_10_pointwise 0.506 187 16 78
mobilenet_v1_075 224 Conv2d_11_pointwise 0.565 219 16 94
mobilenet_v1_075 224 Conv2d_12_pointwise 0.594 251 32 94
mobilenet_v1_075 224 Conv2d_13_pointwise 0.653 315 32 126
mobilenet_v1_075 321 Conv2d_0 0.034 3 2 1
mobilenet_v1_075 321 Conv2d_1_pointwise 0.107 7 2 3
mobilenet_v1_075 321 Conv2d_2_pointwise 0.174 11 4 5
mobilenet_v1_075 321 Conv2d_3_pointwise 0.308 19 4 9
mobilenet_v1_075 321 Conv2d_4_pointwise 0.373 27 8 13
mobilenet_v1_075 321 Conv2d_5_pointwise 0.503 43 8 21
mobilenet_v1_075 321 Conv2d_6_pointwise 0.570 59 16 29
mobilenet_v1_075 321 Conv2d_7_pointwise 0.704 91 16 45
mobilenet_v1_075 321 Conv2d_8_pointwise 0.837 123 16 61
mobilenet_v1_075 321 Conv2d_9_pointwise 0.970 155 16 77
mobilenet_v1_075 321 Conv2d_10_pointwise 1.104 187 16 93
mobilenet_v1_075 321 Conv2d_11_pointwise 1.237 219 16 109
mobilenet_v1_075 321 Conv2d_12_pointwise 1.310 251 32 125
mobilenet_v1_075 321 Conv2d_13_pointwise 1.454 315 32 157
resnet_v1_50 None resnet_v1_50/block1 None 35 8 None
resnet_v1_50 None resnet_v1_50/block2 None 99 16 None
resnet_v1_50 None resnet_v1_50/block3 None 291 32 None
resnet_v1_50 None resnet_v1_50/block4 None 483 32 None
resnet_v1_50 224 resnet_v1_50/block1 1.323 35 8 15
resnet_v1_50 224 resnet_v1_50/block2 2.974 99 16 47
resnet_v1_50 224 resnet_v1_50/block3 5.498 291 32 143
resnet_v1_50 224 resnet_v1_50/block4 6.963 483 32 239
resnet_v1_50 321 resnet_v1_50/block1 2.767 35 8 17
resnet_v1_50 321 resnet_v1_50/block2 6.315 99 16 49
resnet_v1_50 321 resnet_v1_50/block3 12.013 291 32 145
resnet_v1_50 321 resnet_v1_50/block4 15.629 483 32 241
resnet_v1_101 None resnet_v1_101/block1 None 35 8 None
resnet_v1_101 None resnet_v1_101/block2 None 99 16 None
resnet_v1_101 None resnet_v1_101/block3 None 835 32 None
resnet_v1_101 None resnet_v1_101/block4 None 1027 32 None
resnet_v1_101 224 resnet_v1_101/block1 1.323 35 8 15
resnet_v1_101 224 resnet_v1_101/block2 2.974 99 16 47
resnet_v1_101 224 resnet_v1_101/block3 12.923 835 32 415
resnet_v1_101 224 resnet_v1_101/block4 14.387 1027 32 511
resnet_v1_101 321 resnet_v1_101/block1 2.767 35 8 17
resnet_v1_101 321 resnet_v1_101/block2 6.315 99 16 49
resnet_v1_101 321 resnet_v1_101/block3 28.718 835 32 417
resnet_v1_101 321 resnet_v1_101/block4 32.334 1027 32 513
resnet_v1_152 None resnet_v1_152/block1 None 35 8 None
resnet_v1_152 None resnet_v1_152/block2 None 163 16 None
resnet_v1_152 None resnet_v1_152/block3 None 1315 32 None
resnet_v1_152 None resnet_v1_152/block4 None 1507 32 None
resnet_v1_152 224 resnet_v1_152/block1 1.323 35 8 15
resnet_v1_152 224 resnet_v1_152/block2 4.721 163 16 79
resnet_v1_152 224 resnet_v1_152/block3 20.347 1315 32 655
resnet_v1_152 224 resnet_v1_152/block4 21.811 1507 32 751
resnet_v1_152 321 resnet_v1_152/block1 2.767 35 8 17
resnet_v1_152 321 resnet_v1_152/block2 10.061 163 16 81
resnet_v1_152 321 resnet_v1_152/block3 45.238 1315 32 657
resnet_v1_152 321 resnet_v1_152/block4 48.854 1507 32 753
resnet_v1_200 None resnet_v1_200/block1 None 35 8 None
resnet_v1_200 None resnet_v1_200/block2 None 419 16 None
resnet_v1_200 None resnet_v1_200/block3 None 1571 32 None
resnet_v1_200 None resnet_v1_200/block4 None 1763 32 None
resnet_v1_200 224 resnet_v1_200/block1 1.323 35 8 15
resnet_v1_200 224 resnet_v1_200/block2 11.709 419 16 207
resnet_v1_200 224 resnet_v1_200/block3 27.335 1571 32 783
resnet_v1_200 224 resnet_v1_200/block4 28.799 1763 32 879
resnet_v1_200 321 resnet_v1_200/block1 2.767 35 8 17
resnet_v1_200 321 resnet_v1_200/block2 25.043 419 16 209
resnet_v1_200 321 resnet_v1_200/block3 60.220 1571 32 785
resnet_v1_200 321 resnet_v1_200/block4 63.836 1763 32 881
resnet_v2_50 None resnet_v2_50/block1 None 35 8 None
resnet_v2_50 None resnet_v2_50/block2 None 99 16 None
resnet_v2_50 None resnet_v2_50/block3 None 291 32 None
resnet_v2_50 None resnet_v2_50/block4 None 483 32 None
resnet_v2_50 224 resnet_v2_50/block1 1.327 35 8 15
resnet_v2_50 224 resnet_v2_50/block2 2.979 99 16 47
resnet_v2_50 224 resnet_v2_50/block3 5.505 291 32 143
resnet_v2_50 224 resnet_v2_50/block4 6.969 483 32 239
resnet_v2_50 321 resnet_v2_50/block1 2.774 35 8 17
resnet_v2_50 321 resnet_v2_50/block2 6.326 99 16 49
resnet_v2_50 321 resnet_v2_50/block3 12.026 291 32 145
resnet_v2_50 321 resnet_v2_50/block4 15.643 483 32 241
resnet_v2_101 None resnet_v2_101/block1 None 35 8 None
resnet_v2_101 None resnet_v2_101/block2 None 99 16 None
resnet_v2_101 None resnet_v2_101/block3 None 835 32 None
resnet_v2_101 None resnet_v2_101/block4 None 1027 32 None
resnet_v2_101 224 resnet_v2_101/block1 1.327 35 8 15
resnet_v2_101 224 resnet_v2_101/block2 2.979 99 16 47
resnet_v2_101 224 resnet_v2_101/block3 12.932 835 32 415
resnet_v2_101 224 resnet_v2_101/block4 14.397 1027 32 511
resnet_v2_101 321 resnet_v2_101/block1 2.774 35 8 17
resnet_v2_101 321 resnet_v2_101/block2 6.326 99 16 49
resnet_v2_101 321 resnet_v2_101/block3 28.739 835 32 417
resnet_v2_101 321 resnet_v2_101/block4 32.356 1027 32 513
resnet_v2_152 None resnet_v2_152/block1 None 35 8 None
resnet_v2_152 None resnet_v2_152/block2 None 163 16 None
resnet_v2_152 None resnet_v2_152/block3 None 1315 32 None
resnet_v2_152 None resnet_v2_152/block4 None 1507 32 None
resnet_v2_152 224 resnet_v2_152/block1 1.327 35 8 15
resnet_v2_152 224 resnet_v2_152/block2 4.728 163 16 79
resnet_v2_152 224 resnet_v2_152/block3 20.361 1315 32 655
resnet_v2_152 224 resnet_v2_152/block4 21.826 1507 32 751
resnet_v2_152 321 resnet_v2_152/block1 2.774 35 8 17
resnet_v2_152 321 resnet_v2_152/block2 10.075 163 16 81
resnet_v2_152 321 resnet_v2_152/block3 45.268 1315 32 657
resnet_v2_152 321 resnet_v2_152/block4 48.886 1507 32 753
resnet_v2_200 None resnet_v2_200/block1 None 35 8 None
resnet_v2_200 None resnet_v2_200/block2 None 419 16 None
resnet_v2_200 None resnet_v2_200/block3 None 1571 32 None
resnet_v2_200 None resnet_v2_200/block4 None 1763 32 None
resnet_v2_200 224 resnet_v2_200/block1 1.327 35 8 15
resnet_v2_200 224 resnet_v2_200/block2 11.722 419 16 207
resnet_v2_200 224 resnet_v2_200/block3 27.355 1571 32 783
resnet_v2_200 224 resnet_v2_200/block4 28.820 1763 32 879
resnet_v2_200 321 resnet_v2_200/block1 2.774 35 8 17
resnet_v2_200 321 resnet_v2_200/block2 25.072 419 16 209
resnet_v2_200 321 resnet_v2_200/block3 60.265 1571 32 785
resnet_v2_200 321 resnet_v2_200/block4 63.882 1763 32 881

FAQ

What does a resolution of 'None' mean?

In this case, the input resolution is undefined. For most models, the receptive field parameters can be computed even without knowing the input resolution. The number of FLOPs cannot be computed in this case.

For some networks, effective_padding shows as 'None' (eg, for Inception_v2 or Mobilenet_v1 when input size is not specified). Why is that?

This means that the padding for these networks depends on the input size. So, unless we know exactly the input image dimensionality to be used, it is not possible to determine the padding applied at the different layers. Look at the other entries where the input size is fixed; for those cases, effective_padding is not None.

This happens due to Tensorflow's implementation of the 'SAME' padding mode, which may depend on the input feature map size to a given layer. For background on this, see these notes from the TF documentation.

Also, note that in this case the program is not able to check if the network is aligned (ie, it could be that the different paths from input to output have receptive fields which are not consistently centered at the same position in the input image).

So you should be aware that such networks might not be aligned -- the program has no way of checking it when the padding cannot be determined.

The receptive field parameters for network X seem different from what I expected... maybe your calculation is incorrect?

First, note that the results presented here are based on the tensorflow implementations from the TF-Slim model library. So, it is possible that due to some implementation details the RF parameters are different.

One common case of confusion is the TF-Slim Resnet implementation, which applies stride in the last residual unit of each block, instead of at the input activations in the first residual unit of each block (which is what is described in the Resnet paper) -- see this comment. This makes the stride with respect to each convolution block potentially different. In this case, though, note that a flag may be used to recover the original striding convention.

Second, it could be that we have a bug somewhere. While we include many tests in our library, it is always possible that we missed something. If you suspect this is the case, please file a GitHub issue here.

The number of FLOPs for network X seem different from what I expected... maybe your calculation is incorrect?

First, note that the results presented here are based on the tensorflow implementations from the TF-Slim model library. So, it is possible that due to some implementation details the number of FLOPs is different.

Second, one common confusion arises since some papers refer to FLOPs as the number of Multiply-Add operations; in other words, some papers count a Multiply-Add as one floating point operation while others count as two. Here, we follow the tensorflow.profiler convention and count a Multiply-Add as two operations. One noticeable counter-example is the ResNet paper, where the FLOPs mentioned in Table 1 therein actually mean the number of Multiply-Add's (see Section 3.3 in their paper). So there is roughly a factor of two between their paper and our numbers.

Finally, we rely on tensorflow.profiler for estimating the number of floating point operations. It could be that they have a bug somewhere, or that we are using their library incorrectly, or that we simply have a bug somewhere else. If you suspect this is the case, please file a GitHub issue here).

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