{"url":"/dataset/shift15m","name":"SHIFT15M","full_name":null,"description_markdown":"**SHIFT15M** is a dataset that can be used to properly evaluate models in situations where the distribution of data changes between training and testing. \r\nThe SHIFT15M dataset has several good properties: (i) Multiobjective. Each instance in the dataset has several numerical values that can be used as target variables. (ii) Large-scale. The SHIFT15M dataset consists of 15million fashion images. (iii) Coverage of types of dataset shifts. SHIFT15M contains multiple dataset shift problem settings (e.g., covariate shift or target shift). SHIFT15M also enables the performance evaluation of the model under various magnitudes of dataset shifts by switching the magnitude.","description_withheld":null,"homepage":"https://github.com/st-tech/zozo-shift15m/tree/main/benchmarks","introduced_date":"2021-08-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/shift15m-multiobjective-large-scale-fashion","title":"SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts","first_author":"Masanari Kimura","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Popularity Forecasting","url":"/task/popularity-forecasting","datasets_with_task":"/datasets/task/popularity-forecasting"}],"languages":[],"variants":["SHIFT15M"],"data_loaders":[{"repo":"https://github.com/st-tech/zozo-shift15m","url":"https://github.com/st-tech/zozo-shift15m","frameworks":["pytorch"]}],"num_papers_in_archive":3,"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."}