{"url":"/dataset/ucf101-ds","name":"UCF101-DS","full_name":"UCF101 Distribution Shift","description_markdown":"Existing benchmark datasets in real-world distribution shifts are generally synthetically generated via augmentations to simulate real-world shifts such as weather and camera rotation. The UCF101-DS dataset consists of real-world distribution shifts from user-generated videos without synthetic augmentation. It has videos for 47 UCF-101 classes with 63 different distribution shifts that can be categorized into 15 categories. A total of 536 unique videos split into a total of 4,708 clips. Each clip ranges from 7 to 10 seconds long.","description_withheld":null,"homepage":"https://www.crcv.ucf.edu/research/projects/ucf101-ds-action-recognition-for-real-world-distribution-shifts/","introduced_date":"2022-07-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/large-scale-robustness-analysis-of-video","title":"Large-scale Robustness Analysis of Video Action Recognition Models","first_author":"Madeline Chantry Schiappa","url":null},"license":{"name":"Creative Commons","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Action Recognition","url":"/task/action-recognition-in-videos","datasets_with_task":"/datasets/task/action-recognition-in-videos"}],"languages":[],"variants":["UCF101-DS"],"data_loaders":[],"num_papers_in_archive":1,"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."}