{"url":"/dataset/omnibenchmark","name":"OmniBenchmark","full_name":null,"description_markdown":"Omni-Realm Benchmark (OmniBenchmark) is a diverse (21 semantic realm-wise datasets) and concise (realm-wise datasets have no concepts overlapping) benchmark for evaluating pre-trained model generalization across semantic super-concepts/realms, e.g. across mammals to aircraft. \r\n\r\n[**ECCV2022**]","description_withheld":null,"homepage":"https://zhangyuanhan-ai.github.io/OmniBenchmark/","introduced_date":"2022-07-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-omni-vision-representation","title":"Benchmarking Omni-Vision Representation through the Lens of Visual Realms","first_author":"Yuanhan Zhang","url":null},"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License","url":"http://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Representation Learning","url":"/task/representation-learning","datasets_with_task":"/datasets/task/representation-learning"},{"name":"Prompt Engineering","url":"/task/prompt-engineering","datasets_with_task":"/datasets/task/prompt-engineering"},{"name":"Fine-Grained Image Recognition","url":"/task/fine-grained-image-recognition","datasets_with_task":"/datasets/task/fine-grained-image-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["OmniBenchmark"],"data_loaders":[{"repo":null,"url":"","frameworks":["pytorch"]}],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-omnibenchmark","task":"Image Classification","dataset_variant":"OmniBenchmark","rows":22,"metrics":["Average Top-1 Accuracy"],"first_row_in_archive_order":{"model":"NOAH-ViTB/16","paper":"/paper/neural-prompt-search","metrics":{"Average Top-1 Accuracy":"47.6"},"code_links":[{"title":"ZhangYuanhan-AI/NOAH","url":"https://github.com/ZhangYuanhan-AI/NOAH"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/neural-prompt-search","title":"Neural Prompt Search","date":"2022-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bamboo-building-mega-scale-vision-dataset","title":"Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy","date":"2022-03-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","rows_on_this_dataset":1,"code_links":58,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":137,"samples_ran":71,"samples_unverified":66,"pointer_only_for_licence":73,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/beit-bert-pre-training-of-image-transformers","title":"BEiT: BERT Pre-Training of Image Transformers","date":"2021-06-15","rows_on_this_dataset":1,"code_links":14,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":6,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mlp-mixer-an-all-mlp-architecture-for-vision","title":"MLP-Mixer: An all-MLP Architecture for Vision","date":"2021-05-04","rows_on_this_dataset":1,"code_links":49,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":134,"samples_ran":106,"samples_unverified":28,"pointer_only_for_licence":36,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/emerging-properties-in-self-supervised-vision","title":"Emerging Properties in Self-Supervised Vision Transformers","date":"2021-04-29","rows_on_this_dataset":1,"code_links":32,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":5,"samples_unverified":15,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","rows_on_this_dataset":1,"code_links":80,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":207,"samples_ran":108,"samples_unverified":99,"pointer_only_for_licence":43,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/re-labeling-imagenet-from-single-to-multi","title":"Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels","date":"2021-01-13","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/mopro-webly-supervised-learning-with-momentum","title":"MoPro: Webly Supervised Learning with Momentum Prototypes","date":"2020-09-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/meal-v2-boosting-vanilla-resnet-50-to-80-top","title":"MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks","date":"2020-09-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-learning-of-visual-features-by","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","date":"2020-06-17","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":13,"samples_unverified":4,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/large-scale-learning-of-general-visual","title":"Big Transfer (BiT): General Visual Representation Learning","date":"2019-12-24","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/momentum-contrast-for-unsupervised-visual","title":"Momentum Contrast for Unsupervised Visual Representation Learning","date":"2019-11-13","rows_on_this_dataset":1,"code_links":44,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":42,"samples_ran":26,"samples_unverified":16,"pointer_only_for_licence":16,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","rows_on_this_dataset":1,"code_links":144,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":302,"samples_ran":171,"samples_unverified":131,"pointer_only_for_licence":112,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","rows_on_this_dataset":1,"code_links":30,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":17,"samples_unverified":7,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":1,"code_links":4,"syntology":null},{"paper":"/paper/parameter-efficient-transfer-learning-for-nlp","title":"Parameter-Efficient Transfer Learning for NLP","date":"2019-02-02","rows_on_this_dataset":1,"code_links":17,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":8,"samples_unverified":14,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/manifold-mixup-better-representations-by","title":"Manifold Mixup: Better Representations by Interpolating Hidden States","date":"2018-06-13","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":2,"samples_unverified":9,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/inception-v4-inception-resnet-and-the-impact","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","date":"2016-02-23","rows_on_this_dataset":1,"code_links":87,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","rows_on_this_dataset":2,"code_links":484,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":377,"samples_ran":230,"samples_unverified":147,"pointer_only_for_licence":187,"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":18,"samples_harvested":1348,"samples_ran":792,"samples_unverified":556,"pointer_only_for_licence":508,"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."}