Model params 100 MB
Estimates for a single full pass of model at input size 300 x 300:
- Memory required for features: 116 MB
- Flops: 31 GFLOPs
Estimates are given below of the burden of computing the relu4_3 features in the network for different input sizes using a batch size of 128:
| input size | feature size | feature memory | flops |
|---|---|---|---|
| 150 x 150 | 19 x 19 x 512 | 3 GB | 818 GFLOPs |
| 300 x 300 | 38 x 38 x 512 | 13 GB | 3 TFLOPs |
| 450 x 450 | 57 x 57 x 512 | 28 GB | 7 TFLOPs |
| 600 x 600 | 75 x 75 x 512 | 50 GB | 13 TFLOPs |
| 750 x 750 | 94 x 94 x 512 | 78 GB | 20 TFLOPs |
| 900 x 900 | 113 x 113 x 512 | 113 GB | 29 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:
