{"url":"/dataset/artbench-10","name":"ArtBench-10 (32x32)","full_name":null,"description_markdown":"We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10 distinctive artistic styles, with 5,000 training images and 1,000 testing images per style. ArtBench-10 has several advantages over previous artwork datasets. Firstly, it is class-balanced while most previous artwork datasets suffer from the long tail class distributions. Secondly, the images are of high quality with clean annotations. Thirdly, ArtBench-10 is created with standardized data collection, annotation, filtering, and preprocessing procedures. We provide three versions of the dataset with different resolutions (32×32, 256×256, and original image size), formatted in a way that is easy to be incorporated by popular machine learning frameworks.","description_withheld":null,"homepage":"https://github.com/liaopeiyuan/artbench","introduced_date":"2022-06-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-artbench-dataset-benchmarking-generative","title":"The ArtBench Dataset: Benchmarking Generative Models with Artworks","first_author":"Peiyuan Liao","url":null},"license":{"name":"Fair Use","url":"https://github.com/liaopeiyuan/artbench/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Image Generation","url":"/task/image-generation","datasets_with_task":"/datasets/task/image-generation"},{"name":"Few-Shot Image Classification","url":"/task/few-shot-image-classification","datasets_with_task":"/datasets/task/few-shot-image-classification"},{"name":"Conditional Image Generation","url":"/task/conditional-image-generation","datasets_with_task":"/datasets/task/conditional-image-generation"},{"name":"Unconditional Image Generation","url":"/task/unconditional-image-generation","datasets_with_task":"/datasets/task/unconditional-image-generation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ArtBench-10 (32x32)"],"data_loaders":[{"repo":"https://github.com/liaopeiyuan/artbench","url":"https://github.com/liaopeiyuan/artbench","frameworks":["pytorch"]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/conditional-image-generation-on-artbench-10","task":"Conditional Image Generation","dataset_variant":"ArtBench-10 (32x32)","rows":6,"metrics":["FID"],"first_row_in_archive_order":{"model":"StyleGAN2 + ADA","paper":"/paper/training-generative-adversarial-networks-with-2","metrics":{"FID":"2.625"},"code_links":[{"title":"NVlabs/stylegan2-ada-pytorch","url":"https://github.com/NVlabs/stylegan2-ada-pytorch"},{"title":"keras-team/keras-io","url":"https://github.com/keras-team/keras-io/blob/master/examples/generative/gan_ada.py"},{"title":"NVlabs/stylegan2-ada","url":"https://github.com/NVlabs/stylegan2-ada"},{"title":"toshas/torch-fidelity","url":"https://github.com/toshas/torch-fidelity"},{"title":"pbaylies/stylegan2","url":"https://github.com/pbaylies/stylegan2"},{"title":"mahmoudnafifi/HistoGAN","url":"https://github.com/mahmoudnafifi/HistoGAN"},{"title":"eps696/stylegan2ada","url":"https://github.com/eps696/stylegan2ada"},{"title":"sangyun884/Face2Webtoon","url":"https://github.com/sangyun884/Face2Webtoon"},{"title":"woctezuma/steam-stylegan2-ada","url":"https://github.com/woctezuma/steam-stylegan2-ada"},{"title":"aiksir/stylegan2-ada-blending","url":"https://github.com/aiksir/stylegan2-ada-blending"},{"title":"ChristophReich1996/Multi-StyleGAN","url":"https://github.com/ChristophReich1996/Multi-StyleGAN"},{"title":"beresandras/gan-flavours-keras","url":"https://github.com/beresandras/gan-flavours-keras"},{"title":"jiangshuyi0v0/cvd-gan","url":"https://github.com/jiangshuyi0v0/cvd-gan"},{"title":"NariMo91/GANs-generative-art","url":"https://github.com/NariMo91/GANs-generative-art"},{"title":"duskvirkus/stylegan2-ada-lightning","url":"https://github.com/duskvirkus/stylegan2-ada-lightning"},{"title":"wjdals3406/stylegan2-ada-encoder","url":"https://github.com/wjdals3406/stylegan2-ada-encoder"},{"title":"sh4174/3d-stylegan2-ada","url":"https://github.com/sh4174/3d-stylegan2-ada"},{"title":"buganart/stylegan2-ada-pytorch","url":"https://github.com/buganart/stylegan2-ada-pytorch"},{"title":"wangamelia/cmpm202p2p2","url":"https://github.com/wangamelia/cmpm202p2p2"},{"title":"datduong/stylegan2-ada-Ws-22q","url":"https://github.com/datduong/stylegan2-ada-Ws-22q"},{"title":"esimpsontheartist/stylegan2-FineArt","url":"https://github.com/esimpsontheartist/stylegan2-FineArt"},{"title":"vsemecky/stylegan2-ada","url":"https://github.com/vsemecky/stylegan2-ada"},{"title":"BearNinja123/StyleGAN_ADAnough","url":"https://github.com/BearNinja123/StyleGAN_ADAnough"},{"title":"usufyan29/stylegan2_runway","url":"https://github.com/usufyan29/stylegan2_runway"},{"title":"matjazmav/fri-2021-ibb-seminar","url":"https://github.com/matjazmav/fri-2021-ibb-seminar"},{"title":"Eitan177/testGenImages","url":"https://github.com/Eitan177/testGenImages"},{"title":"lelechen63/stylegannerf","url":"https://github.com/lelechen63/stylegannerf"},{"title":"fai07600521/final-project","url":"https://github.com/fai07600521/final-project"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rebooting-acgan-auxiliary-classifier-gans","title":"Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training","date":"2021-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":9,"samples_unverified":0,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/projected-gans-converge-faster","title":"Projected GANs Converge Faster","date":"2021-11-01","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":49,"samples_ran":38,"samples_unverified":11,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/training-generative-adversarial-networks-with-2","title":"Training Generative Adversarial Networks with Limited Data","date":"2020-06-11","rows_on_this_dataset":1,"code_links":28,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":29,"samples_ran":5,"samples_unverified":24,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","rows_on_this_dataset":1,"code_links":126,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":92,"samples_ran":29,"samples_unverified":63,"pointer_only_for_licence":18,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/consistency-regularization-for-generative-1","title":"Consistency Regularization for Generative Adversarial Networks","date":"2019-10-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/large-scale-gan-training-for-high-fidelity","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","date":"2018-09-28","rows_on_this_dataset":1,"code_links":35,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":41,"samples_ran":14,"samples_unverified":27,"pointer_only_for_licence":4,"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":5,"samples_harvested":220,"samples_ran":95,"samples_unverified":125,"pointer_only_for_licence":41,"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."}