A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
This provides a more efficient alternative to CIFAR for researchers and practitioners in machine learning, though it is incremental as it builds on existing downsampling ideas.
The authors tackled the high computational cost of using the original ImageNet dataset by proposing downsampled variants (e.g., ImageNet32×32) that retain the same number of classes and images but with lower resolution, resulting in dramatically faster experiments while maintaining similar hyperparameter characteristics.
The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm design, architecture search, and hyperparameter tuning) on the original dataset might be prohibitive, we propose to consider a downsampled version of ImageNet. In contrast to the CIFAR datasets and earlier downsampled versions of ImageNet, our proposed ImageNet32$\times$32 (and its variants ImageNet64$\times$64 and ImageNet16$\times$16) contains exactly the same number of classes and images as ImageNet, with the only difference that the images are downsampled to 32$\times$32 pixels per image (64$\times$64 and 16$\times$16 pixels for the variants, respectively). Experiments on these downsampled variants are dramatically faster than on the original ImageNet and the characteristics of the downsampled datasets with respect to optimal hyperparameters appear to remain similar. The proposed datasets and scripts to reproduce our results are available at http://image-net.org/download-images and https://github.com/PatrykChrabaszcz/Imagenet32_Scripts