{"url":"/dataset/metashift","name":"MetaShift","full_name":null,"description_markdown":"**MetaShift** is a collection of 12,868 sets of natural images across 410 classes. It can be used to benchmark and evaluate how robust machine learning models are to data shifts.","description_withheld":null,"homepage":"https://github.com/weixin-liang/metashift","introduced_date":"2022-02-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/metashift-a-dataset-of-datasets-for-1","title":"MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts","first_author":"Weixin Liang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[],"variants":["MetaShift"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/jameszou707/metashift","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/metashift","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":28,"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."}