{"url":"/dataset/vedai","name":"VEDAI","full_name":"Vehicle Detection in Aerial Imagery","description_markdown":"VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduced and compared. We also give the performance of some baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.","description_withheld":null,"homepage":"https://downloads.greyc.fr/vedai/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"},{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["VEDAI"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-detection-on-vedai","task":"Object Detection","dataset_variant":"VEDAI","rows":3,"metrics":["mAP50"],"first_row_in_archive_order":{"model":"GHOST","paper":"/paper/guided-hybrid-quantization-for-object","metrics":{"mAP50":"80.31"},"code_links":[{"title":"icey-zhang/ghost","url":"https://github.com/icey-zhang/ghost"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/icafusion-iterative-cross-attention-guided","title":"ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection","date":"2023-08-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/guided-hybrid-quantization-for-object","title":"Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching","date":"2022-12-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/superyolo-super-resolution-assisted-object","title":"SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery","date":"2022-09-27","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."}