{"url":"/dataset/shape-bias","name":"shape bias","full_name":null,"description_markdown":"The 'shape bias' dataset was introduced in Geirhos et al. (ICLR 2019) and consists of 224x224 images with conflicting texture and shape information (e.g., cat shape with elephant texture). This is used to measure the shape vs. texture bias of image classifiers.","description_withheld":null,"homepage":"https://github.com/rgeirhos/texture-vs-shape","introduced_date":"2018-11-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/imagenet-trained-cnns-are-biased-towards","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","first_author":"Robert Geirhos","url":null},"license":{"name":"CC-BY 4.0","url":"https://github.com/rgeirhos/texture-vs-shape/blob/master/DATASET_LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"},{"name":"Object Recognition","url":"/task/object-recognition","datasets_with_task":"/datasets/task/object-recognition"},{"name":"Out-of-Distribution Generalization","url":"/task/out-of-distribution-generalization","datasets_with_task":"/datasets/task/out-of-distribution-generalization"}],"languages":[],"variants":["shape bias"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://github.com/tensorflow/datasets","frameworks":["tf"]},{"repo":"https://github.com/rgeirhos/texture-vs-shape","url":"https://github.com/rgeirhos/texture-vs-shape","frameworks":["pytorch"]},{"repo":"https://github.com/bethgelab/model-vs-human","url":"https://github.com/bethgelab/model-vs-human","frameworks":["tf","pytorch"]}],"num_papers_in_archive":120,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-recognition-on-shape-bias","task":"Object Recognition","dataset_variant":"shape bias","rows":18,"metrics":["shape bias"],"first_row_in_archive_order":{"model":"Imagen","paper":"/paper/intriguing-properties-of-generative","metrics":{"shape bias":"98.7"},"code_links":[{"title":"SamsungSAILMontreal/ForestDiffusion","url":"https://github.com/SamsungSAILMontreal/ForestDiffusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/intriguing-properties-of-generative","title":"Intriguing properties of generative classifiers","date":"2023-09-28","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":10,"samples_unverified":2,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/scaling-vision-transformers-to-22-billion","title":"Scaling Vision Transformers to 22 Billion Parameters","date":"2023-02-10","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","rows_on_this_dataset":1,"code_links":82,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":16,"samples_unverified":4,"pointer_only_for_licence":16,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/do-adversarially-robust-imagenet-models","title":"Do Adversarially Robust ImageNet Models Transfer Better?","date":"2020-07-16","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/a-simple-framework-for-contrastive-learning","title":"A Simple Framework for Contrastive Learning of Visual Representations","date":"2020-02-13","rows_on_this_dataset":3,"code_links":96,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":137,"samples_ran":79,"samples_unverified":58,"pointer_only_for_licence":52,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploring-the-origins-and-prevalence-of","title":"The Origins and Prevalence of Texture Bias in Convolutional Neural Networks","date":"2019-11-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/billion-scale-semi-supervised-learning-for","title":"Billion-scale semi-supervised learning for image classification","date":"2019-05-02","rows_on_this_dataset":2,"code_links":4,"syntology":null},{"paper":"/paper/imagenet-trained-cnns-are-biased-towards","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","date":"2018-11-29","rows_on_this_dataset":4,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"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":4,"samples_harvested":175,"samples_ran":105,"samples_unverified":70,"pointer_only_for_licence":80,"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."}