Model params 55 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 152 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 1664 | 19 GB | 435 GFLOPs |
| 336 x 336 | 1 x 1 x 1664 | 42 GB | 971 GFLOPs |
| 448 x 448 | 2 x 2 x 1664 | 76 GB | 2 TFLOPs |
| 560 x 560 | 2 x 2 x 1664 | 118 GB | 3 TFLOPs |
| 672 x 672 | 3 x 3 x 1664 | 171 GB | 4 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:
