{"url":"/dataset/cifar-10c","name":"CIFAR-10C","full_name":null,"description_markdown":"Common corruptions dataset for CIFAR10","description_withheld":null,"homepage":"https://github.com/hendrycks/robustness","introduced_date":null,"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 License 2.0","url":"https://github.com/hendrycks/robustness/blob/master/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CIFAR-10C"],"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":494,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-cifar-10c","task":"Classification","dataset_variant":"CIFAR-10C","rows":1,"metrics":["Accuracy on Brightness Corrupted Images"],"first_row_in_archive_order":{"model":"ViT-L/16 (Background)","paper":"/paper/reduction-of-class-activation-uncertainty","metrics":{"Accuracy on Brightness Corrupted Images":"99.03"},"code_links":[{"title":"dipuk0506/SpinalNet","url":"https://github.com/dipuk0506/SpinalNet"},{"title":"dipuk0506/uq","url":"https://github.com/dipuk0506/uq"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/domain-generalization-on-cifar-10c","task":"Domain Generalization","dataset_variant":"CIFAR-10C","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GLOT-DR","paper":"/paper/global-local-regularization-via","metrics":{"Accuracy":"84.5"},"code_links":[{"title":"viethoang1512/glot","url":"https://github.com/viethoang1512/glot"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/reduction-of-class-activation-uncertainty","title":"Reduction of Class Activation Uncertainty with Background Information","date":"2023-05-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/global-local-regularization-via","title":"Global-Local Regularization Via Distributional Robustness","date":"2022-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}