metadata
license: mit
This repository contains the robust ImageNet models used in our paper "Do adversarially robust imagenet models transfer better?".
See our papers's GitHub repository for more details!
Standard Accuracy of L2-Robust ImageNet Models
| Model | ε=0 | ε=0.01 | ε=0.03 | ε=0.05 | ε=0.1 | ε=0.25 | ε=0.5 | ε=1.0 | ε=3.0 | ε=5.0 |
|---|---|---|---|---|---|---|---|---|---|---|
| ResNet-18 | 69.79 | 69.90 | 69.24 | 69.15 | 68.77 | 67.43 | 65.49 | 62.32 | 53.12 | 45.59 |
| ResNet-50 | 75.80 | 75.68 | 75.76 | 75.59 | 74.78 | 74.14 | 73.16 | 70.43 | 62.83 | 56.13 |
| Wide-ResNet-50-2 | 76.97 | 77.25 | 77.26 | 77.17 | 76.74 | 76.21 | 75.11 | 73.41 | 66.90 | 60.94 |
| Wide-ResNet-50-4 | 77.91 | 78.02 | 77.87 | 77.77 | 77.64 | 77.10 | 76.52 | 75.51 | 69.67 | 65.20 |
| Model | ε=0 | ε=3 |
|---|---|---|
| DenseNet | 77.37 | 66.98 |
| MNASNET | 60.97 | 41.83 |
| MobileNet-v2 | 65.26 | 50.40 |
| ResNeXt50_32x4d | 77.38 | 66.25 |
| ShuffleNet | 64.25 | 43.32 |
| VGG16_bn | 73.66 | 57.19 |