Model params 232 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 4 MB
- Flops: 727 MFLOPs
Estimates are given below of the burden of computing the pool5 features in the network for different input sizes using a batch size of 128:
| input size | feature size | feature memory | flops |
|---|---|---|---|
| 112 x 112 | 3 x 3 x 256 | 109 MB | 19 GFLOPs |
| 224 x 224 | 6 x 6 x 256 | 476 MB | 86 GFLOPs |
| 336 x 336 | 10 x 10 x 256 | 1 GB | 200 GFLOPs |
| 448 x 448 | 13 x 13 x 256 | 2 GB | 362 GFLOPs |
| 560 x 560 | 17 x 17 x 256 | 3 GB | 571 GFLOPs |
| 672 x 672 | 20 x 20 x 256 | 4 GB | 828 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:
