Model params 31 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 126 MB
- Flops: 3 GFLOPs
Estimates are given below of the burden of computing the features_2
features in the network for different input sizes using a batch size of 128:
input size | feature size | feature memory | flops |
---|---|---|---|
224 x 224 | 1 x 1 x 1024 | 16 GB | 367 GFLOPs |
336 x 336 | 1 x 1 x 1024 | 35 GB | 823 GFLOPs |
448 x 448 | 2 x 2 x 1024 | 63 GB | 1 TFLOPs |
560 x 560 | 2 x 2 x 1024 | 98 GB | 2 TFLOPs |
672 x 672 | 3 x 3 x 1024 | 142 GB | 3 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: