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Report for faster-rcnn-vggvd-pascal

Model params 523 MB

Estimates for a single full pass of model at input size 600 x 850:

  • Memory required for features: 600 MB
  • Flops: 172 GFLOPs

Estimates are given below of the burden of computing the relu5_3 features in the network for different input sizes using a batch size of 128:

input size feature size feature memory flops
300 x 425 19 x 27 x 512 18 GB 5 TFLOPs
600 x 850 38 x 54 x 512 73 GB 20 TFLOPs
900 x 1275 57 x 80 x 512 164 GB 45 TFLOPs
1200 x 1700 75 x 107 x 512 292 GB 80 TFLOPs
1500 x 2125 94 x 133 x 512 456 GB 125 TFLOPs
1800 x 2550 113 x 160 x 512 657 GB 181 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:

faster-rcnn-vggvd-pascal profile

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