{"url":"/dataset/awa2-1","name":"AwA2","full_name":"Animals with Attributes 2","description_markdown":"**Animals with Attributes 2** (**AwA2**) is a dataset for benchmarking transfer-learning algorithms, such as attribute base classification and zero-shot learning. AwA2 is a drop-in replacement of original Animals with Attributes (AwA) dataset, with more images released for each category. Specifically, AwA2 consists of in total 37322 images distributed in 50 animal categories. The AwA2 also provides a category-attribute matrix, which contains an 85-dim attribute vector (e.g., color, stripe, furry, size, and habitat) for each category.\r\n\r\nSource: [Learning from Noisy Web Data with Category-level Supervision](https://arxiv.org/abs/1803.03857)\nImage Source: [https://arxiv.org/pdf/1604.00326.pdf](https://arxiv.org/pdf/1604.00326.pdf)","description_withheld":null,"homepage":"https://cvml.ist.ac.at/AwA2/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/zero-shot-learning-a-comprehensive-evaluation","title":"Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly","first_author":"Yongqin Xian","url":null},"license":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 Few-Shot Learning","url":"/task/generalized-few-shot-learning","datasets_with_task":"/datasets/task/generalized-few-shot-learning"},{"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":["AWA2 - 0-Shot","AwA2"],"data_loaders":[{"repo":"https://github.com/Graviti-AI/datasets","url":"https://gas.graviti.com/dataset/graviti/AnimalsWithAttributes2","frameworks":["tf","pytorch"]}],"num_papers_in_archive":231,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/generalized-few-shot-learning-on-awa2","task":"Generalized Few-Shot Learning","dataset_variant":"AwA2","rows":6,"metrics":["Per-Class Accuracy (1-shot)","Per-Class Accuracy (2-shots)","Per-Class Accuracy (5-shots)","Per-Class Accuracy (10-shots)","Per-Class Accuracy (20-shots)"],"first_row_in_archive_order":{"model":"MVCN","paper":"/paper/better-generalized-few-shot-learning-even","metrics":{"Per-Class Accuracy (1-shot)":"69.9","Per-Class Accuracy (10-shots)":"82.2","Per-Class Accuracy (2-shots)":"76.4","Per-Class Accuracy (5-shots)":"81.2"},"code_links":[{"title":"bigdata-inha/zero-base-gfsl","url":"https://github.com/bigdata-inha/zero-base-gfsl"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/generalized-zero-shot-learning-on-awa2","task":"Generalized Zero-Shot Learning","dataset_variant":"AwA2","rows":4,"metrics":["Harmonic mean","Accuracy Seen","Accuracy Unseen","H"],"first_row_in_archive_order":{"model":"ZeroDiff","paper":"/paper/exploring-data-efficiency-in-zero-shot","metrics":{"Harmonic mean":"79.5"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/zero-shot-learning-on-awa2","task":"Zero-Shot Learning","dataset_variant":"AwA2","rows":4,"metrics":["average top-1 classification accuracy","Accuracy Seen","Accuracy Unseen","H"],"first_row_in_archive_order":{"model":"ZeroDiff","paper":"/paper/exploring-data-efficiency-in-zero-shot","metrics":{"average top-1 classification accuracy":"86.4"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/concept-based-classification-on-awa2","task":"Concept-based Classification","dataset_variant":"AwA2","rows":2,"metrics":["Task Accuracy (%)","Concept Accuracy (%)"],"first_row_in_archive_order":{"model":"EQ-CBM (ResNet-34)","paper":"/paper/eq-cbm-a-probabilistic-concept-bottleneck-1","metrics":{"Concept Accuracy (%)":"99.129","Task Accuracy (%)":"95.965"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-image-classification-on-awa2-0-shot","task":"Few-Shot Image Classification","dataset_variant":"AWA2 - 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":"69.3"},"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"}],"papers_with_a_benchmark_row":[{"paper":"/paper/eq-cbm-a-probabilistic-concept-bottleneck-1","title":"EQ-CBM: A Probabilistic Concept Bottleneck with Energy-based Models and Quantized Vectors","date":"2024-09-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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/exploring-data-efficiency-in-zero-shot","title":"Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models","date":"2024-06-05","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/better-generalized-few-shot-learning-even","title":"Better Generalized Few-Shot Learning Even Without Base Data","date":"2022-11-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/duet-cross-modal-semantic-grounding-for","title":"DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning","date":"2022-07-04","rows_on_this_dataset":1,"code_links":2,"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/long-tail-learning-with-attributes","title":"From Generalized zero-shot learning to long-tail with class descriptors","date":"2020-04-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/latent-embedding-feedback-and-discriminative","title":"Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification","date":"2020-03-17","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/generalized-zero-and-few-shot-learning-via-1","title":"Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders","date":"2019-06-01","rows_on_this_dataset":2,"code_links":1,"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."}},{"paper":"/paper/generalized-zero-and-few-shot-learning-via","title":"Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders","date":"2018-12-05","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-robust-visual-semantic-embeddings","title":"Learning Robust Visual-Semantic Embeddings","date":"2017-03-17","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":9,"samples_ran":4,"samples_unverified":5,"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."}