{"url":"/dataset/dada-2000","name":"DADA-2000","full_name":null,"description_markdown":"DADA-2000 is a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. \r\n\r\nSource: [DADA: A Large-scale Benchmark and Model for Driver Attention Prediction in Accidental Scenarios](https://arxiv.org/pdf/1912.12148)\r\nImage Source: [Fang et al](https://arxiv.org/pdf/1912.12148)","description_withheld":null,"homepage":"https://github.com/JWFangit/LOTVS-DADA","introduced_date":"2019-12-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/dada-a-large-scale-benchmark-and-model-for","title":"DADA: Driver Attention Prediction in Driving Accident Scenarios","first_author":"Jianwu Fang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Autonomous Vehicles","url":"/task/autonomous-vehicles","datasets_with_task":"/datasets/task/autonomous-vehicles"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"}],"languages":[],"variants":["DADA-2000"],"data_loaders":[{"repo":"https://github.com/JWFangit/LOTVS-DADA","url":"https://github.com/JWFangit/LOTVS-DADA","frameworks":[]}],"num_papers_in_archive":16,"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":"2026-09-25T09:33:49+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."}