{"url":"/dataset/semi-inat","name":"Semi-iNat","full_name":"Semi-Supervised iNaturalist","description_markdown":"Semi-iNat is a challenging dataset for semi-supervised classification with a long-tailed distribution of classes, fine-grained categories, and domain shifts between labeled and unlabeled data. The data is obtained from iNaturalist, a community driven project aimed at collecting observations of biodiversity. \r\n\r\nThe dataset comes with standard training, validation and test sets. The training set consists of:\r\n\r\n* labeled images from 810 species, where around 10% of the images are labeled.\r\n\r\n* unlabeled images contains unlabeled images from the same set of classes as the labeled images (in-class), plus the images from a different set of classes as the labeled set (out-of-class). The species are guaranteed to have species at the same phylum level in the labels set. This reflects a common scenario where a coarser taxonomic label of an image can be easily obtained.","description_withheld":null,"homepage":"https://github.com/cvl-umass/semi-inat-2021","introduced_date":"2021-06-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-semi-supervised-inaturalist-challenge-at","title":"The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop","first_author":"Jong-Chyi Su","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semi-Supervised Image Classification","url":"/task/semi-supervised-image-classification","datasets_with_task":"/datasets/task/semi-supervised-image-classification"}],"languages":[],"variants":["Semi-iNat"],"data_loaders":[],"num_papers_in_archive":12,"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":"2026-09-24T18:15:14+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."}