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ShibaniSanturkar authored and andrewilyas committed May 28, 2019
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[submodule "robustness_lib"]
path = robustness_lib
url = https://github.com/MadryLab/robustness_lib.git
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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](http://gradsci.org/robust-apps.pdf),
[blog](http://gradsci.org/robust_apps)).

![](headline.jpg)

## Running the notebooks

Steps to run the notebooks (for now, requires CUDA):
- Clone this repository with `--recurse-submodules` to include submodules (`--recursive` pre-Git 2.13)
- Download our models from S3: [CIFAR-10](http://andrewilyas.com/CIFAR.pt), [Restricted ImageNet](http://andrewilyas.com/RestrictedImageNet.pt), [ImageNet](http://andrewilyas.com/ImageNet.pt), [Horse-to-Zebra](http://andrewilyas.com/H2Z.pt), [Summer-to-Winter](http://andrewilyas.com/S2W.pt), [Apple-to-Orange](http://andrewilyas.com/A2O.pt)
- 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 the `CIFAR` and `ImageNet` datasets
- Start a jupyter notebook server: `PYTHONPATH=robustness_lib/ 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={Preprint},
year={2019}
}
```

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torch
torchvision
pandas
numpy
scipy
GPUtil
dill
tensorboardX
tables
tqdm
Submodule robustness_lib added at 48e8fd
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# Path to dataset
DATA_PATH_DICT = {
'CIFAR': '/path/tools/cifar',
'RestrictedImageNet': '/path/tools/imagenet',
'ImageNet': '//path/tools/imagenet',
'H2Z': '/path/tools/horse2zebra',
'A2O': '/path/tools/apple2orange',
'S2W': '/path/tools/summer2winter_yosemite'
}

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