{"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/image-classification-with-small-datasets","title":"Image Classification With Small Datasets: Overview and Benchmark","arxiv_id":null,"date":"2022-05-05","proceeding":"IEEE Access 2022 5","authors":["Lorenzo Brigato","Björn Barz","Luca Iocchi","Joachim Denzler"],"abstract":"Image classification with small datasets has been an active research area in the recent past.\r\nHowever, as research in this scope is still in its infancy, two key ingredients are missing for ensuring\r\nreliable and truthful progress: a systematic and extensive overview of the state of the art, and a common\r\nbenchmark to allow for objective comparisons between published methods. This article addresses both\r\nissues. First, we systematically organize and connect past studies to consolidate a community that is currently\r\nfragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison\r\nof approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery,\r\nsatellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the\r\nstandard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned\r\nvenues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a\r\nhighly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a\r\nsingle specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline\r\nclassifier.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9770050","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9770050","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":"image-classification-with-small-datasets","repo_url":"https://github.com/lorenzobrigato/gem","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"small-data","task_name":"Small Data Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}