{"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/icassava-2019fine-grained-visual","title":"iCassava 2019 Fine-Grained Visual Categorization Challenge","arxiv_id":"1908.02900","date":"2019-08-08","proceeding":null,"authors":["Ernest Mwebaze","Timnit Gebru","Andrea Frome","Solomon Nsumba","Jeremy Tusubira"],"abstract":"Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in Africa.At least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava. Since many of these farmers have smart phones, they can easily obtain photos of dis-eased and healthy cassava leaves in their farms, allowing the opportunity to use computer vision techniques to monitor the disease type and severity and increase yields. How-ever, annotating these images is extremely difficult as ex-perts who are able to distinguish between highly similar dis-eases need to be employed. We provide a dataset of labeled and unlabeled cassava leaves and formulate a Kaggle challenge to encourage participants to improve the performance of their algorithms using semi-supervised approaches. This paper describes our dataset and challenge which is part of the Fine-Grained Visual Categorization workshop at CVPR2019.","url_abs":"https://arxiv.org/abs/1908.02900v2","url_pdf":"https://arxiv.org/pdf/1908.02900v2.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":"icassava-2019fine-grained-visual","repo_url":"https://github.com/Sunil4423/-cassava_disease_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"icassava-2019fine-grained-visual","repo_url":"https://github.com/dcpatton/cassava_disease_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"icassava-2019fine-grained-visual","repo_url":"https://github.com/mjvakili/cassava_disease_classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"icassava-2019fine-grained-visual","repo_url":"https://github.com/nii4u/Cassava-Disease-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"icassava-2019fine-grained-visual","repo_url":"https://github.com/riron1206/kaggle_Cassava","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"fine-grained-visual-categorization","task_name":"Fine-Grained Visual Categorization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.02900","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}