MadryLab / Robustness_applications
Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"
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Code for "Computer Vision with a Single (Robust) Classifier"
These are notebooks for reproducing our paper "Computer Vision with a Single (Robust) Classifier" (preprint, blog). Based on the robustness python library.
Running the notebooks
Steps to run the notebooks (for now, requires CUDA):
- Clone this repository
- Download our models from S3: CIFAR-10, Restricted ImageNet, ImageNet, Horse-to-Zebra, Summer-to-Winter, Apple-to-Orange
- Make a
models
folder in the main repository folder, and save the checkpoints there - Install all the required packages with
pip install -r requirements.txt
- Edit paths in
user_constants.py
to point to PyTorch-formatted versions of theCIFAR
andImageNet
datasets - Start a jupyter notebook server:
jupyter notebook . --ip 0.0.0.0
Citation
@inproceedings{santurkar2019computer,
title={Computer Vision with a Single (Robust) Classifier},
author={Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
booktitle={ArXiv preprint arXiv:1906.09453},
year={2019}
}
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