{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-semi-supervised-inaturalist-aves","title":"The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop","arxiv_id":"2103.06937","date":"2021-03-11","proceeding":null,"authors":["Jong-Chyi Su","Subhransu Maji"],"abstract":"This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~\\cite{semi-aves} at the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds sampled from the iNat-2018 dataset for a total of nearly 150k images. From this collection, we sample a subset of classes and their labels, while adding the images from the remaining classes to the unlabeled set of images. The presence of out-of-domain data (novel classes), high class-imbalance, and fine-grained similarity between classes poses significant challenges for existing semi-supervised recognition techniques in the literature. The dataset is available here: \\url{https://github.com/cvl-umass/semi-inat-2020}","url_abs":"https://arxiv.org/abs/2103.06937v1","url_pdf":"https://arxiv.org/pdf/2103.06937v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-semi-supervised-inaturalist-aves","repo_url":"https://github.com/cvl-umass/semi-inat-2020","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"the-semi-supervised-inaturalist-aves","repo_url":"https://github.com/cvl-umass/ssl-evaluation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.06937","atlas_url":"https://app.syntology.ai/?focus=2103.06937","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}