{"url":"/dataset/countix","name":"Countix","full_name":"Countix","description_markdown":"Countix is a real world dataset of repetition videos collected in the wild (i.e.YouTube) covering a wide range of semantic settings with significant challenges such as camera and object motion, diverse set of periods and counts, and changes in the speed of repeated actions. Countix include repeated videos of workout activities (squats, pull ups, battle rope training, exercising arm), dance moves (pirouetting, pumping fist), playing instruments (playing ukulele), using tools repeatedly (hammer hitting objects, chainsaw cutting wood, slicing onion), artistic performances (hula hooping, juggling soccer ball), sports (playing ping pong and tennis) and many others. Figure 6 illustrates some examples from the dataset as well as the distribution of repetition counts and period lengths.\r\n\r\nSource: [Counting Out Time: Class Agnostic Video Repetition Counting in the Wild](https://arxiv.org/pdf/2006.15418v1.pdf)","description_withheld":null,"homepage":"https://sites.google.com/view/repnet","introduced_date":"2020-06-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/counting-out-time-class-agnostic-video-1","title":"Counting Out Time: Class Agnostic Video Repetition Counting in the Wild","first_author":"Debidatta Dwibedi","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Repetitive Action Counting","url":"/task/repetitive-action-counting","datasets_with_task":"/datasets/task/repetitive-action-counting"}],"languages":[],"variants":["Countix"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/repetitive-action-counting-on-countix","task":"Repetitive Action Counting","dataset_variant":"Countix","rows":3,"metrics":["OBO","MAE","OBZ","RMSE"],"first_row_in_archive_order":{"model":"RepNet","paper":"/paper/counting-out-time-class-agnostic-video-1","metrics":{"MAE":"0.3641","OBO":"0.3034"},"code_links":[{"title":"confifu/RepNet-Pytorch","url":"https://github.com/confifu/RepNet-Pytorch"},{"title":"materight/RepNet-pytorch","url":"https://github.com/materight/RepNet-pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-short-note-on-evaluating-repnet-for","title":"A Short Note on Evaluating RepNet for Temporal Repetition Counting in Videos","date":"2024-11-13","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/every-shot-counts-using-exemplars-for","title":"Every Shot Counts: Using Exemplars for Repetition Counting in Videos","date":"2024-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/counting-out-time-class-agnostic-video-1","title":"Counting Out Time: Class Agnostic Video Repetition Counting in the Wild","date":"2020-06-27","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"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."}