Model params 255 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 220 MB
- Flops: 11 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 | 7 GB | 372 GFLOPs |
224 x 224 | 7 x 7 x 2048 | 27 GB | 1 TFLOPs |
336 x 336 | 11 x 11 x 2048 | 62 GB | 3 TFLOPs |
448 x 448 | 14 x 14 x 2048 | 110 GB | 6 TFLOPs |
560 x 560 | 18 x 18 x 2048 | 171 GB | 9 TFLOPs |
672 x 672 | 21 x 21 x 2048 | 246 GB | 13 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: