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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

Model ε=0.5/255 ε=1/255 ε=2/255 ε=4/255 ε=8/255
ResNet-18 66.13 63.46 59.63 52.49 42.11
ResNet-50 73.73 72.05 69.10 63.86 54.53
Wide-ResNet-50-2 75.82 74.65 72.35 68.41 60.82
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