{"url":"/dataset/openttgames","name":"OpenTTGames","full_name":null,"description_markdown":"OSAI introduces OpenTTGames - an open dataset aimed at evaluation of different computer vision tasks in Table Tennis: ball detection, semantic segmentation of humans, table and scoreboard and fast in-game events spotting.\r\n\r\nIt includes full-HD videos of table tennis games recorded at 120 fps with an industrial camera. Every video is equipped with an annotation containing the frame numbers and corresponding targets for this particular frame: manually labeled in-game events (ball bounces, net hits, or empty event targets) and/or ball coordinates and segmentation masks, which were labeled with deep learning-aided annotation models.","description_withheld":null,"homepage":"https://lab.osai.ai/","introduced_date":"2020-04-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/ttnet-real-time-temporal-and-spatial-video","title":"TTNet: Real-time temporal and spatial video analysis of table tennis","first_author":"Roman Voeikov","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Multi-Task Learning","url":"/task/multi-task-learning","datasets_with_task":"/datasets/task/multi-task-learning"},{"name":"Event Detection","url":"/task/event-detection","datasets_with_task":"/datasets/task/event-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["OpenTTGames"],"data_loaders":[],"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."}