{"url":"/dataset/cityflow","name":"CityFlow","full_name":null,"description_markdown":"CityFlow is a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. \r\n\r\nCamera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). \r\n\r\nSource: [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](/paper/cityflow-a-city-scale-benchmark-for-multi)","description_withheld":null,"homepage":"https://www.aicitychallenge.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/cityflow-a-city-scale-benchmark-for-multi","title":"CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification","first_author":"Zheng Tang","url":null},"license":{"name":"Custom","url":"http://www.aicitychallenge.org/wp-content/uploads/2021/01/DataLicenseAgreement_AICityChallenge_2021.pdf"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Person Re-Identification","url":"/task/person-re-identification","datasets_with_task":"/datasets/task/person-re-identification"},{"name":"Multi-agent Reinforcement Learning","url":"/task/multi-agent-reinforcement-learning","datasets_with_task":"/datasets/task/multi-agent-reinforcement-learning"},{"name":"Vehicle Re-Identification","url":"/task/vehicle-re-identification","datasets_with_task":"/datasets/task/vehicle-re-identification"},{"name":"Attribute","url":"/task/attribute","datasets_with_task":"/datasets/task/attribute"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["CityFlow"],"data_loaders":[{"repo":"https://github.com/HanaeELmrabet2000/Analyse-des-risques-dans-une-zone-sans-signalisation-alt-rnative-pour-la-signalisation-","url":"https://www.cityflowdataset.com/documentation","frameworks":["tf"]}],"num_papers_in_archive":47,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/vehicle-re-identification-on-cityflow","task":"Vehicle Re-Identification","dataset_variant":"CityFlow","rows":1,"metrics":["mAP"],"first_row_in_archive_order":{"model":"A Strong Baseline","paper":"/paper/a-strong-baseline-for-vehicle-re","metrics":{"mAP":"61.34"},"code_links":[{"title":"cybercore-co-ltd/track2_aicity_2021","url":"https://github.com/cybercore-co-ltd/track2_aicity_2021"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-strong-baseline-for-vehicle-re","title":"A Strong Baseline for Vehicle Re-Identification","date":"2021-04-22","rows_on_this_dataset":1,"code_links":1,"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."}