{"url":"/dataset/apy","name":"aPY","full_name":"Attribute Pascal and Yahoo","description_markdown":"**aPY** is a coarse-grained dataset composed of 15339 images from 3 broad categories (animals, objects and vehicles), further divided into a total of 32 subcategories (aeroplane, …, zebra).\r\n\r\nSource: [From Classical to Generalized Zero-Shot Learning: a Simple Adaptation Process](https://arxiv.org/abs/1809.10120)\r\nImage Source: [https://www.cs.cmu.edu/~afarhadi/papers/Attributes.pdf](https://www.cs.cmu.edu/~afarhadi/papers/Attributes.pdf)","description_withheld":null,"homepage":"https://vision.cs.uiuc.edu/attributes/","introduced_date":"2009-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Describing objects by their attributes","first_author":null,"url":"https://doi.org/10.1109/CVPR.2009.5206772"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Zero-Shot Learning","url":"/task/zero-shot-learning","datasets_with_task":"/datasets/task/zero-shot-learning"},{"name":"Few-Shot Image Classification","url":"/task/few-shot-image-classification","datasets_with_task":"/datasets/task/few-shot-image-classification"},{"name":"Generalized Zero-Shot Learning","url":"/task/generalized-zero-shot-learning","datasets_with_task":"/datasets/task/generalized-zero-shot-learning"},{"name":"Concept-based Classification","url":"/task/concept-based-classification","datasets_with_task":"/datasets/task/concept-based-classification"}],"languages":[],"variants":["aPY - 0-Shot","aPY"],"data_loaders":[],"num_papers_in_archive":147,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/concept-based-classification-on-apy","task":"Concept-based Classification","dataset_variant":"aPY","rows":1,"metrics":["Task Accuracy (%)","Concept Accuracy (%)"],"first_row_in_archive_order":{"model":"CGEM (ResNet-34)","paper":"/paper/concept-graph-embedding-models-for-enhanced","metrics":{"Concept Accuracy (%)":"71.19","Task Accuracy (%)":"43.75"},"code_links":[{"title":"jumpsnack/cgem","url":"https://github.com/jumpsnack/cgem"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-image-classification-on-apy-0-shot","task":"Few-Shot Image Classification","dataset_variant":"aPY - 0-Shot","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TAFE-Net","paper":"/paper/tafe-net-task-aware-feature-embeddings-for-1","metrics":{"Accuracy":"42.2"},"code_links":[{"title":"ucbdrive/tafe-net","url":"https://github.com/ucbdrive/tafe-net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/generalized-zero-shot-learning-on-apy","task":"Generalized Zero-Shot Learning","dataset_variant":"aPY","rows":1,"metrics":["H"],"first_row_in_archive_order":{"model":"WGAN+ZLAP","paper":"/paper/zero-shot-logit-adjustment","metrics":{"H":"46"},"code_links":[{"title":"cdb342/ijcai-2022-zla","url":"https://github.com/cdb342/ijcai-2022-zla"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/generalized-zero-shot-learning-on-apy-0-shot","task":"Generalized Zero-Shot Learning","dataset_variant":"aPY - 0-Shot","rows":1,"metrics":["Harmonic mean"],"first_row_in_archive_order":{"model":"ZSL-KG","paper":"/paper/zero-shot-learning-with-common-sense","metrics":{"Harmonic mean":"61.57"},"code_links":[{"title":"BatsResearch/zsl-kg","url":"https://github.com/BatsResearch/zsl-kg"},{"title":"BatsResearch/nayak-arxiv20-code","url":"https://github.com/BatsResearch/nayak-arxiv20-code"},{"title":"batsresearch/nayak-tmlr22-code","url":"https://github.com/batsresearch/nayak-tmlr22-code"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/zero-shot-learning-on-apy-0-shot","task":"Zero-Shot Learning","dataset_variant":"aPY - 0-Shot","rows":1,"metrics":["Top-1"],"first_row_in_archive_order":{"model":"ZSL-KG","paper":"/paper/zero-shot-learning-with-common-sense","metrics":{"Top-1":"60.54"},"code_links":[{"title":"BatsResearch/zsl-kg","url":"https://github.com/BatsResearch/zsl-kg"},{"title":"BatsResearch/nayak-arxiv20-code","url":"https://github.com/BatsResearch/nayak-arxiv20-code"},{"title":"batsresearch/nayak-tmlr22-code","url":"https://github.com/batsresearch/nayak-tmlr22-code"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/concept-graph-embedding-models-for-enhanced","title":"Concept Graph Embedding Models for Enhanced Accuracy and Interpretability","date":"2024-08-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/zero-shot-logit-adjustment","title":"Zero-Shot Logit Adjustment","date":"2022-04-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/zero-shot-learning-with-common-sense","title":"Zero-Shot Learning with Common Sense Knowledge Graphs","date":"2020-06-18","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/tafe-net-task-aware-feature-embeddings-for-1","title":"TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning","date":"2019-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"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":3,"samples_ran":1,"samples_unverified":2,"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."}