Benchmarking the robustness to distribution shifts traditionally relies on dataset collection which is typically laborious and expensive, in particular for datasets with a large number of classes like ImageNet. An exception to this procedure is ImageNet-C (Hendrycks & Dietterich, 2019), a dataset created by applying common real-world corruptions at different levels of intensity to the (clean) ImageNet images. Inspired by this work, we introduce ImageNet-Cartoon and ImageNet-Drawing, two datasets constructed by converting ImageNet images into cartoons and colored pencil drawings, using a GAN framework (Wang & Yu, 2020) and simple image processing (Lu et al., 2012), respectively. This repository contains ImageNet-Cartoon and ImageNet-Drawing. Checkout the official GitHub Repo for the code on how to reproduce the datasets. If you find this useful in your research, please consider citing: @inproceedings{imagenetshift, title={ImageNet-Cartoon and ImageNet-Drawing: two domain shift datasets for ImageNet}, author={Tiago Salvador and Adam M. Oberman}, booktitle={ICML Workshop on Shift happens: Crowdsourcing metrics and test datasets beyond ImageNet.}, year={2022} }