{"url":"/dataset/animal","name":"ANIMAL","full_name":"ANIMAL-10N","description_markdown":"10 classes with 50, 000 training and 5, 000 testing images. Please note that, in ANIMAL10N, noisy labels were injected naturally by human mistakes, where its noise rate was estimated at 8%.","description_withheld":null,"homepage":"https://dm.kaist.ac.kr/datasets/animal-10n/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Learning with noisy labels","url":"/task/learning-with-noisy-labels","datasets_with_task":"/datasets/task/learning-with-noisy-labels"}],"languages":[],"variants":["ANIMAL"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/animal-animal10n-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/learning-with-noisy-labels-on-animal","task":"Learning with noisy labels","dataset_variant":"ANIMAL","rows":19,"metrics":["Accuracy","Network","ImageNet Pretrained"],"first_row_in_archive_order":{"model":"Jigsaw-ViT","paper":"/paper/jigsaw-vit-learning-jigsaw-puzzles-in-vision","metrics":{"Accuracy":"89.0","ImageNet Pretrained":"NO","Network":"DeiT-S"},"code_links":[{"title":"yingyichen-cyy/JigsawViT","url":"https://github.com/yingyichen-cyy/JigsawViT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/psscl-a-progressive-sample-selection","title":"PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels","date":"2024-12-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/clipcleaner-cleaning-noisy-labels-with-clip","title":"CLIPCleaner: Cleaning Noisy Labels with CLIP","date":"2024-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-to-merge-training-with-class-balance","title":"Cross-to-merge training with class balance strategy for learning with noisy labels","date":"2024-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sure-survey-recipes-for-building-reliable-and","title":"SURE: SUrvey REcipes for building reliable and robust deep networks","date":"2024-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/generative-noisy-label-learning-by-implicit","title":"Partial Label Supervision for Agnostic Generative Noisy Label Learning","date":"2023-08-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dynamic-loss-for-robust-learning","title":"Dynamic Loss For Robust Learning","date":"2022-11-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bootstrapping-the-relationship-between-images","title":"Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels","date":"2022-10-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/instance-dependent-noisy-label-learning-via","title":"Instance-Dependent Noisy Label Learning via Graphical Modelling","date":"2022-09-02","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/jigsaw-vit-learning-jigsaw-puzzles-in-vision","title":"Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer","date":"2022-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/compressing-features-for-learning-with-noisy","title":"Compressing Features for Learning with Noisy Labels","date":"2022-06-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scalable-penalized-regression-for-noise","title":"Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels","date":"2022-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/s3-supervised-self-supervised-learning-under-1","title":"SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise","date":"2021-11-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/boosting-co-teaching-with-compression","title":"Boosting Co-teaching with Compression Regularization for Label Noise","date":"2021-04-28","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":14,"samples_ran":13,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-with-feature-dependent-label-noise-a-1","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","date":"2021-03-13","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/selfie-refurbishing-unclean-samples-for","title":"SELFIE: Refurbishing Unclean Samples for Robust Deep Learning","date":"2019-06-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":4,"samples_harvested":24,"samples_ran":20,"samples_unverified":4,"pointer_only_for_licence":10,"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."}