{"url":"/dataset/imagenet-1k-vs-openimage-o","name":"OpenImage-O","full_name":null,"description_markdown":"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":"https://ooddetection.github.io/","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":[],"tasks":[{"name":"Out-of-Distribution Detection","url":"/task/out-of-distribution-detection","datasets_with_task":"/datasets/task/out-of-distribution-detection"},{"name":"Out of Distribution (OOD) Detection","url":"/task/ood-detection","datasets_with_task":"/datasets/task/ood-detection"}],"languages":[],"variants":["OpenImage-O"],"data_loaders":[{"repo":"https://github.com/ensta-u2is/torch-uncertainty","url":"https://torch-uncertainty.github.io/","frameworks":["pytorch"]},{"repo":"https://github.com/haoqiwang/vim","url":"https://github.com/haoqiwang/vim","frameworks":["pytorch"]}],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}