Model params 110 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 235 MB
- Flops: 8 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 2208 | 29 GB | 997 GFLOPs |
| 336 x 336 | 1 x 1 x 2208 | 66 GB | 2 TFLOPs |
| 448 x 448 | 2 x 2 x 2208 | 118 GB | 4 TFLOPs |
| 560 x 560 | 2 x 2 x 2208 | 183 GB | 6 TFLOPs |
| 672 x 672 | 3 x 3 x 2208 | 265 GB | 9 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:
