{"url":"/dataset/visdrone","name":"VisDrone","full_name":null,"description_markdown":"**VisDrone** is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University, China. The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc.), and density (sparse and crowded scenes). Note that, the dataset was collected using various drone platforms (i.e., drones with different models), in different scenarios, and under various weather and lighting conditions. These frames are manually annotated with more than 2.6 million bounding boxes of targets of frequent interests, such as pedestrians, cars, bicycles, and tricycles. Some important attributes including scene visibility, object class and occlusion, are also provided for better data utilization.\r\n\r\nSource: [https://github.com/VisDrone/VisDrone-Dataset](https://github.com/VisDrone/VisDrone-Dataset)\r\nImage Source: [https://arxiv.org/pdf/2001.06303.pdf](https://arxiv.org/pdf/2001.06303.pdf)","description_withheld":null,"homepage":"https://github.com/VisDrone/VisDrone-Dataset","introduced_date":"2018-04-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/vision-meets-drones-a-challenge","title":"Vision Meets Drones: A Challenge","first_author":"Pengfei Zhu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Optical Flow Estimation","url":"/task/optical-flow-estimation","datasets_with_task":"/datasets/task/optical-flow-estimation"}],"languages":[],"variants":["VisDrone"," VisDrone-DET2019","VisDrone-DET2019","DGTA-VisDrone"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/visdrone-det-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/VisDrone/VisDrone-Dataset","url":"https://github.com/VisDrone/VisDrone-Dataset","frameworks":[]}],"num_papers_in_archive":73,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-detection-on-visdrone-det2019-1","task":"Object Detection","dataset_variant":"VisDrone-DET2019","rows":5,"metrics":["AP50","AP","APvt","APt","APs","AP75","FPS","APm"],"first_row_in_archive_order":{"model":"CZ Det.","paper":"/paper/cascaded-zoom-in-detector-for-high-resolution","metrics":{"AP":"33.22","AP50":"58.3","AP75":"33.16","FPS":"8.44"},"code_links":[{"title":"akhilpm/dronedetectron2","url":"https://github.com/akhilpm/dronedetectron2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/legnet-lightweight-edge-gaussian-driven","title":"LEGNet: Lightweight Edge-Gaussian Driven Network for Low-Quality Remote Sensing Image Object Detection","date":"2025-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dynamic-coarse-to-fine-learning-for-oriented","title":"Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection","date":"2023-04-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cascaded-zoom-in-detector-for-high-resolution","title":"Cascaded Zoom-in Detector for High Resolution Aerial Images","date":"2023-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rfla-gaussian-receptive-field-based-label","title":"RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection","date":"2022-08-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-normalized-gaussian-wasserstein-distance","title":"A Normalized Gaussian Wasserstein Distance for Tiny Object Detection","date":"2021-10-26","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"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":2,"samples_ran":2,"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."}