{"url":"/dataset/imagenet-k-vs-openimage-o","name":"ImageNet-1k vs OpenImage-O","full_name":null,"description_markdown":"OpenImage-O is built for the ID dataset ImageNet-1k. It is manually annotated, comes with a naturally diverse distribution, and has a large scale. It is built to overcome several shortcomings of existing OOD benchmarks. OpenImage-O is image-by-image filtered from the test set of OpenImage-V3, which has been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias.","description_withheld":null,"homepage":"","introduced_date":"2022-03-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/vim-out-of-distribution-with-virtual-logit","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","first_author":"Haoqi Wang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Out-of-Distribution Detection","url":"/task/out-of-distribution-detection","datasets_with_task":"/datasets/task/out-of-distribution-detection"}],"languages":[],"variants":["ImageNet-1k vs OpenImage-O"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-11","task":"Out-of-Distribution Detection","dataset_variant":"ImageNet-1k vs OpenImage-O","rows":7,"metrics":["AUROC","FPR95","Latency, ms"],"first_row_in_archive_order":{"model":"NNGuide (RegNet)","paper":"/paper/nearest-neighbor-guidance-for-out-of-1","metrics":{"AUROC":"97.73","FPR95":"10.79","Latency, ms":"31.00"},"code_links":[{"title":"jingkang50/openood","url":"https://github.com/jingkang50/openood"},{"title":"roomo7time/nnguide","url":"https://github.com/roomo7time/nnguide"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/discopatch-batch-statistics-are-all-you-need","title":"DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection","date":"2025-01-14","rows_on_this_dataset":1,"code_links":0,"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/forte-finding-outliers-with-representation","title":"Forte : Finding Outliers with Representation Typicality Estimation","date":"2024-10-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/nearest-neighbor-guidance-for-out-of-1","title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","date":"2023-09-26","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":6,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neuron-activation-coverage-rethinking-out-of","title":"Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization","date":"2023-06-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gen-pushing-the-limits-of-softmax-based-out","title":"GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vim-out-of-distribution-with-virtual-logit","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","date":"2022-03-21","rows_on_this_dataset":1,"code_links":2,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":16,"samples_ran":10,"samples_unverified":6,"pointer_only_for_licence":6,"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."}