{"url":"/dataset/tennis","name":"Tennis","full_name":null,"description_markdown":"This dataset was introduced by [1], but was not used in its experiment. \r\n[2] propose to use all the clips included in the first 7 games as a training set, and the remainings as a testing set.\r\n\r\n[1] Y.-C. Huang et al., TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications. AVSS, 2019.\r\n\r\n[2] S. Tarashima et al., Widely Applicable Strong Baseline for Sports Ball Detection and Tracking, BMVC, 2023.","description_withheld":null,"homepage":"https://github.com/nttcom/WASB-SBDT/blob/main/GET_STARTED.md#data-preparation","introduced_date":"2019-07-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/tracknet-a-deep-learning-network-for-tracking","title":"TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications","first_author":"Yu-Chuan Huang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Sports Ball Detection and Tracking","url":"/task/sports-ball-detection-and-tracking","datasets_with_task":"/datasets/task/sports-ball-detection-and-tracking"},{"name":"Reinforcement Learning","url":"/task/reinforcement-learning","datasets_with_task":"/datasets/task/reinforcement-learning"}],"languages":[],"variants":["Tennis"],"data_loaders":[],"num_papers_in_archive":14,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sports-ball-detection-and-tracking-on-tennis","task":"Sports Ball Detection and Tracking","dataset_variant":"Tennis","rows":8,"metrics":["F1 (%)","Accuracy (%)","Average Precision (%)"],"first_row_in_archive_order":{"model":"WASB (Step=1)","paper":"/paper/widely-applicable-strong-baseline-for-sports","metrics":{"Accuracy (%)":"91.8","Average Precision (%)":"94.2","F1 (%)":"95.6"},"code_links":[{"title":"nttcom/wasb-sbdt","url":"https://github.com/nttcom/wasb-sbdt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/widely-applicable-strong-baseline-for-sports","title":"Widely Applicable Strong Baseline for Sports Ball Detection and Tracking","date":"2023-11-09","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/monotrack-shuttle-trajectory-reconstruction","title":"MonoTrack: Shuttle trajectory reconstruction from monocular badminton video","date":"2022-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/tracknetv2-efficient-shuttlecock-tracking","title":"TrackNetV2: Efficient Shuttlecock Tracking Network","date":"2020-12-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real-time-cnn-based-segmentation-architecture","title":"Real-time CNN-based Segmentation Architecture for Ball Detection in a Single View Setup","date":"2020-07-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deepball-deep-neural-network-ball-detector","title":"DeepBall: Deep Neural-Network Ball Detector","date":"2019-02-19","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"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."}