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SE-ResNeXt-101-32x4d.md

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Report for SE-ResNeXt-101-32x4d

Model params 187 MB

Estimates for a single full pass of model at input size 224 x 224:

  • Memory required for features: 197 MB
  • Flops: 8 GFLOPs

Estimates are given below of the burden of computing the conv5_3 features in the network for different input sizes using a batch size of 128:

input size feature size feature memory flops
112 x 112 4 x 4 x 2048 6 GB 264 GFLOPs
224 x 224 7 x 7 x 2048 25 GB 1 TFLOPs
336 x 336 11 x 11 x 2048 56 GB 2 TFLOPs
448 x 448 14 x 14 x 2048 98 GB 4 TFLOPs
560 x 560 18 x 18 x 2048 154 GB 6 TFLOPs
672 x 672 21 x 21 x 2048 221 GB 9 TFLOPs

A rough outline of where in the network memory is allocated to parameters and features and where the greatest computational cost lies is shown below. The x-axis does not show labels (it becomes hard to read for networks containing hundreds of layers) - it should be interpreted as depicting increasing depth from left to right. The goal is simply to give some idea of the overall profile of the model:

SE-ResNeXt-101-32x4d profile