Model params 170 MB
Estimates for a single full pass of model at input size 224 x 224:
- Memory required for features: 155 MB
- Flops: 8 GFLOPs
Estimates are given below of the burden of computing the res5c_relu 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 | 5 GB | 251 GFLOPs |
| 224 x 224 | 7 x 7 x 2048 | 19 GB | 974 GFLOPs |
| 336 x 336 | 11 x 11 x 2048 | 44 GB | 2 TFLOPs |
| 448 x 448 | 14 x 14 x 2048 | 77 GB | 4 TFLOPs |
| 560 x 560 | 18 x 18 x 2048 | 121 GB | 6 TFLOPs |
| 672 x 672 | 21 x 21 x 2048 | 174 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:
