Overview
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!
Summary of our pretrained models
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 |
Standard Accuracy of Linf-Robust ImageNet Models
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