{"url":"/dataset/nico","name":"NICO","full_name":"Non-I.I.D. Image dataset with Contexts","description_markdown":"I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing in- stable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with Con- vNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I.I.D. settings.","description_withheld":null,"homepage":"http://nico.thumedialab.com","introduced_date":"2019-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/nico-a-dataset-towards-non-iid-image","title":"Towards Non-I.I.D. Image Classification: A Dataset and Baselines","first_author":"Yue He","url":null},"license":null,"modalities":[],"tasks":[{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"}],"languages":[],"variants":["NICO","NICO Animal","NICO Vehicle"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/domain-generalization-on-nico-animal","task":"Domain Generalization","dataset_variant":"NICO Animal","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NAS-OoD","paper":"/paper/nas-ood-neural-architecture-search-for-out-of","metrics":{"Accuracy":"88.72"},"code_links":[{"title":"HaoyueBaiZJU/NAS-OoD","url":"https://github.com/HaoyueBaiZJU/NAS-OoD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/domain-generalization-on-nico-vehicle","task":"Domain Generalization","dataset_variant":"NICO Vehicle","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NAS-OoD","paper":"/paper/nas-ood-neural-architecture-search-for-out-of","metrics":{"Accuracy":"81.59"},"code_links":[{"title":"HaoyueBaiZJU/NAS-OoD","url":"https://github.com/HaoyueBaiZJU/NAS-OoD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/nas-ood-neural-architecture-search-for-out-of","title":"NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization","date":"2021-09-05","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":3,"samples_unverified":3,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/decaug-out-of-distribution-generalization-via","title":"DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation","date":"2020-12-17","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/distributionally-robust-neural-networks-for","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","date":"2019-11-20","rows_on_this_dataset":2,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":9,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/domain-generalization-by-solving-jigsaw","title":"Domain Generalization by Solving Jigsaw Puzzles","date":"2019-03-16","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/deep-coral-correlation-alignment-for-deep","title":"Deep CORAL: Correlation Alignment for Deep Domain Adaptation","date":"2016-07-06","rows_on_this_dataset":2,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":10,"samples_unverified":6,"pointer_only_for_licence":10,"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":3,"samples_harvested":32,"samples_ran":22,"samples_unverified":10,"pointer_only_for_licence":18,"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."}