{"url":"/dataset/dyml-vehicle","name":"DyML-Vehicle","full_name":"Dynamic Metric Learning Vehicle","description_markdown":"DyML-Vehicle merges two vehicle re-ID datasets PKU VehicleID [1], VERI-Wild [1]. Since these two datasets have only annotations on the identity (fine) level, we manually annotate each image with “model” label (e.g., Toyota Camry, Honda Accord, Audi A4) and “body type” label (e.g., car, suv, microbus, pickup). Moreover, we label all the taxi images as a novel testing class under coarse level.\r\n\r\n[1] Hongye Liu, Yonghong Tian, Yaowei Wang, Lu Pang, and Tiejun Huang. Deep relative distance learning: Tell the difference between similar vehicles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2167–2175, 2016. 4\r\n\r\n[2] Y. Lou, Y. Bai, J. Liu, S. Wang, and L. Duan. Veri-wild: A large dataset and a new method for vehicle re-identification in the wild. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3230–3238, 2019. 4","description_withheld":null,"homepage":"https://github.com/SupetZYK/DynamicMetricLearning","introduced_date":"2021-03-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/dynamic-metric-learning-towards-a-scalable","title":"Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales","first_author":"Yifan Sun","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Metric Learning","url":"/task/metric-learning","datasets_with_task":"/datasets/task/metric-learning"}],"languages":[],"variants":["DyML-Vehicle"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/metric-learning-on-dyml-vehicle","task":"Metric Learning","dataset_variant":"DyML-Vehicle","rows":2,"metrics":["Average-mAP"],"first_row_in_archive_order":{"model":"HAPPIER","paper":"/paper/hierarchical-average-precision-training-for","metrics":{"Average-mAP":"37.0"},"code_links":[{"title":"elias-ramzi/happier","url":"https://github.com/elias-ramzi/happier"},{"title":"elias-ramzi/suprank","url":"https://github.com/elias-ramzi/suprank"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hierarchical-average-precision-training-for","title":"Hierarchical Average Precision Training for Pertinent Image Retrieval","date":"2022-07-05","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dynamic-metric-learning-towards-a-scalable","title":"Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales","date":"2021-03-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"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":2,"samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"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."}