Model params 5 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 17 MB
- Flops: 360 MFLOPs
Estimates are given below of the burden of computing the features_12_cat features in the network for different input sizes using a batch size of 128:
| input size | feature size | feature memory | flops |
|---|---|---|---|
| 112 x 112 | 6 x 6 x 512 | 483 MB | 8 GFLOPs |
| 224 x 224 | 13 x 13 x 512 | 2 GB | 35 GFLOPs |
| 336 x 336 | 20 x 20 x 512 | 5 GB | 81 GFLOPs |
| 448 x 448 | 27 x 27 x 512 | 8 GB | 146 GFLOPs |
| 560 x 560 | 34 x 34 x 512 | 13 GB | 230 GFLOPs |
| 672 x 672 | 41 x 41 x 512 | 19 GB | 333 GFLOPs |
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:
