{"url":"/dataset/tiny-imagenet-c","name":"Tiny-ImageNet-C","full_name":null,"description_markdown":"Tiny ImageNet-C is an open-source data set comprising algorithmically generated corruptions applied to the Tiny ImageNet (ImageNet-200) test set comprising 200 classes following the concept of ImageNet-C. It was introduced by Hendrycks et al. (\"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations\") and comprises 19 different corruptions (15 test corruptions and 4 validation corruptions) spanning 5 severity levels. This results in 200,000 images for the validation set and 750,000 images for the test set. For further information visit the original GitHub repository of ImageNet-C.","description_withheld":null,"homepage":"https://github.com/hendrycks/robustness","introduced_date":"2019-03-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-neural-network-robustness-to-2","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","first_author":"Dan Hendrycks","url":null},"license":{"name":"Apache-2.0","url":"https://github.com/hendrycks/robustness/blob/master/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"}],"languages":[],"variants":["Tiny-ImageNet-C"],"data_loaders":[{"repo":"https://github.com/ensta-u2is/torch-uncertainty","url":"https://torch-uncertainty.github.io/","frameworks":["pytorch"]},{"repo":"https://github.com/cemos-is/robust-minisets","url":"https://github.com/cemos-is/robust-minisets","frameworks":["pytorch"]}],"num_papers_in_archive":55,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}