{"url":"/dataset/dyml-animal","name":"DyML-Animal","full_name":"Dynamic Metric Learning Animal","description_markdown":"DyML-Animal is based on animal images selected from ImageNet-5K [1]. It has 5 semantic scales (i.e., classes, order, family, genus, species) according to biological taxonomy. Specifically, there are 611 “species” for the fine level, 47 categories corresponding to “order”, “family” or “genus” for the middle level, and 5 “classes” for the coarse level. We note some animals have contradiction between visual perception and biological taxonomy, e.g., whale in “mammal” actually looks more similar to fish. Annotating the whale images as belonging to mammal would cause confusion to visual recognition. So we take a detailed check on potential contradictions and intentionally leave out those animals.\r\n\r\n[1] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009. 5","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-Animal"],"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-animal","task":"Metric Learning","dataset_variant":"DyML-Animal","rows":2,"metrics":["Average-mAP"],"first_row_in_archive_order":{"model":"HAPPIER","paper":"/paper/hierarchical-average-precision-training-for","metrics":{"Average-mAP":"43.8"},"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":"2026-09-25T09:33:49+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":"2026-09-25T09:33:49+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":"2026-09-25T09:33:49+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."}