{"url":"/dataset/veri-776","name":"VeRi-776","full_name":"VeRi-776","description_markdown":"**VeRi-776** is a vehicle re-identification dataset which contains 49,357 images of 776 vehicles from 20 cameras. The dataset is collected in the real traffic scenario, which is close to the setting of CityFlow. The dataset contains bounding boxes, types, colors and brands.\r\n\r\nSource: [VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification](https://arxiv.org/abs/2004.06305)\r\nImage Source: [https://vehiclereid.github.io/VeRi/](https://vehiclereid.github.io/VeRi/)","description_withheld":null,"homepage":"https://vehiclereid.github.io/VeRi/","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance","first_author":null,"url":"https://doi.org/10.1007/978-3-319-46475-6_53"},"license":{"name":"Custom (non-commercial)","url":"https://vehiclereid.github.io/VeRi/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Unsupervised Domain Adaptation","url":"/task/unsupervised-domain-adaptation","datasets_with_task":"/datasets/task/unsupervised-domain-adaptation"},{"name":"Vehicle Re-Identification","url":"/task/vehicle-re-identification","datasets_with_task":"/datasets/task/vehicle-re-identification"},{"name":"Unsupervised Vehicle Re-Identification","url":"/task/unsupervised-vehicle-re-identification","datasets_with_task":"/datasets/task/unsupervised-vehicle-re-identification"}],"languages":[],"variants":["VeRi-776","VehicleID to VeRi-776 "],"data_loaders":[{"repo":"https://github.com/michuanhaohao/reid-strong-baseline","url":"https://github.com/michuanhaohao/reid-strong-baseline","frameworks":["pytorch"]}],"num_papers_in_archive":79,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/vehicle-re-identification-on-veri-776","task":"Vehicle Re-Identification","dataset_variant":"VeRi-776","rows":17,"metrics":["mAP","Rank-1","Rank1","Rank5","Rank-10","Rank-5"],"first_row_in_archive_order":{"model":"MBR4B-LAI  (w/ RK)","paper":"/paper/strength-in-diversity-multi-branch","metrics":{"Rank-1":"98.0","Rank5":"98.6","mAP":"92.1"},"code_links":[{"title":"videturfortuna/vehicle_reid_itsc2023","url":"https://github.com/videturfortuna/vehicle_reid_itsc2023"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ca-jaccard-camera-aware-jaccard-distance-for","title":"CA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification","date":"2023-11-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/strength-in-diversity-multi-branch","title":"Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification","date":"2023-10-02","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-person-re-identification-from-a","title":"Rethinking Person Re-identification from a Projection-on-Prototypes Perspective","date":"2023-08-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/msinet-twins-contrastive-search-of-multi","title":"MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID","date":"2023-03-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/clip-reid-exploiting-vision-language-model","title":"CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels","date":"2022-11-25","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/relation-preserving-triplet-mining-for","title":"Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems","date":"2021-10-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/counterfactual-attention-learning-for-fine","title":"Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification","date":"2021-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/git-graph-interactive-transformer-for-vehicle","title":"GiT: Graph Interactive Transformer for Vehicle Re-identification","date":"2021-07-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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},{"paper":"/paper/cluster-contrast-for-unsupervised-person-re","title":"Cluster Contrast for Unsupervised Person Re-Identification","date":"2021-03-22","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":7,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/transreid-transformer-based-object-re","title":"TransReID: Transformer-based Object Re-Identification","date":"2021-02-08","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/attributenet-attribute-enhanced-vehicle-re","title":"AttributeNet: Attribute Enhanced Vehicle Re-Identification","date":"2021-02-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/exploring-spatial-significance-via-hybrid","title":"Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification","date":"2020-05-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vehiclenet-learning-robust-visual","title":"VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification","date":"2020-04-14","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/vehicle-re-identification-using-quadruple","title":"Vehicle Re-identification Using Quadruple Directional Deep Learning Features","date":"2018-11-13","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":35,"samples_ran":20,"samples_unverified":15,"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."}