{"url":"/dataset/finegym","name":"FineGym","full_name":"FineGym","description_markdown":"**FineGym** is an action recognition dataset build on top of gymnasium videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a \"balance beam\" event will be annotated as a sequence of elementary sub-actions derived from five sets: \"leap-jumphop\", \"beam-turns\", \"flight-salto\", \"flight-handspring\", and \"dismount\", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes.","description_withheld":null,"homepage":"https://sdolivia.github.io/FineGym/","introduced_date":"2020-04-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/finegym-a-hierarchical-video-dataset-for-fine","title":"FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding","first_author":"Dian Shao","url":null},"license":{"name":"CC BY-NC 4.0","url":"https://creativecommons.org/licenses/by-nc/4.0/"},"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"},{"name":"Temporal Action Localization","url":"/task/action-recognition","datasets_with_task":"/datasets/task/action-recognition"},{"name":"Human-Object Interaction Detection","url":"/task/human-object-interaction-detection","datasets_with_task":"/datasets/task/human-object-interaction-detection"}],"languages":[],"variants":["FineGym"],"data_loaders":[{"repo":"https://github.com/open-mmlab/mmaction2","url":"https://github.com/open-mmlab/mmaction2/blob/master/tools/data/gym/README.md","frameworks":["pytorch"]}],"num_papers_in_archive":76,"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."}