{"url":"/dataset/tao","name":"TAO","full_name":"Tracking Any Object Dataset","description_markdown":"TAO is a federated dataset for Tracking Any Object, containing 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. A bottom-up approach was used for discovering a large vocabulary of 833 categories, an order of magnitude more than prior tracking benchmarks. \r\n\r\nThe dataset was annotated by labelling tracks for objects that move at any point in the video, and giving names to them post factum. \r\n\r\nSource: [TAO: A Large-Scale Benchmark for Tracking Any Object](/paper/tao-a-large-scale-benchmark-for-tracking-any)","description_withheld":null,"homepage":"http://taodataset.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/tao-a-large-scale-benchmark-for-tracking-any","title":"TAO: A Large-Scale Benchmark for Tracking Any Object","first_author":"Achal Dave","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Multi-Object Tracking","url":"/task/multi-object-tracking","datasets_with_task":"/datasets/task/multi-object-tracking"},{"name":"Robot Navigation","url":"/task/robot-navigation","datasets_with_task":"/datasets/task/robot-navigation"}],"languages":[],"variants":["TAO"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/tao","frameworks":["tf","jax"]}],"num_papers_in_archive":49,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-object-tracking-on-tao","task":"Multi-Object Tracking","dataset_variant":"TAO","rows":9,"metrics":["TETA","LocA","AssocA","ClsA","Track mAP"],"first_row_in_archive_order":{"model":"AED (Co-DETR)","paper":"/paper/associate-everything-detected-facilitating","metrics":{"AssocA":"52.4","ClsA":"41.7","LocA":"71.8","TETA":"55.3"},"code_links":[{"title":"balabooooo/aed","url":"https://github.com/balabooooo/aed"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/associate-everything-detected-facilitating","title":"Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown","date":"2024-09-14","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/general-object-foundation-model-for-images","title":"General Object Foundation Model for Images and Videos at Scale","date":"2023-12-14","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":8,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tracking-every-thing-in-the-wild","title":"Tracking Every Thing in the Wild","date":"2022-07-26","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/1st-place-solution-to-eccv-tao-2020-detect","title":"1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking","date":"2021-01-20","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":13,"samples_ran":8,"samples_unverified":5,"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."}