{"url":"/dataset/badminton","name":"Badminton","full_name":null,"description_markdown":"This dataset was introduced by the TrackNetV2 work. \r\nFollowing the dataset split defined by the authors, we use all the clips from 26 matches as a training set and the remaining 3 matches as a testing set.","description_withheld":null,"homepage":"https://github.com/nttcom/WASB-SBDT/blob/main/GET_STARTED.md#data-preparation","introduced_date":"2020-12-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/tracknetv2-efficient-shuttlecock-tracking","title":"TrackNetV2: Efficient Shuttlecock Tracking Network","first_author":"Nien-En Sun","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"}],"languages":[],"variants":["Badminton"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sports-ball-detection-and-tracking-on","task":"Sports Ball Detection and Tracking","dataset_variant":"Badminton","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 (%)":"89.0","Average Precision (%)":"91.6","F1 (%)":"93.1"},"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."}