{"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/semi-supervised-recognition-under-a-noisy-and","title":"Semi-Supervised Recognition under a Noisy and Fine-grained Dataset","arxiv_id":"2006.10702","date":"2020-06-18","proceeding":null,"authors":["Cheng Cui","Zhi Ye","Yangxi Li","Xinjian Li","Min Yang","Kai Wei","Bing Dai","Yanmei Zhao","Zhongji Liu","Rong Pang"],"abstract":"Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different ways of deep recognition models, we finally won the third place in the competition.","url_abs":"https://arxiv.org/abs/2006.10702v1","url_pdf":"https://arxiv.org/pdf/2006.10702v1.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":"semi-supervised-recognition-under-a-noisy-and","repo_url":"https://github.com/PaddlePaddle/PaddleClas","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"fine-grained-image-recognition","task_name":"Fine-Grained Image Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"tag","task_name":"TAG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"ResNet200_vd_26w_4s_ssld","rank_in_archive_order":258,"of":1060,"metrics":{"Number of params":"76M","Top 1 Accuracy":"85.1%"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"Fix_ResNet50_vd_ssld","rank_in_archive_order":364,"of":1060,"metrics":{"Number of params":"25.58M","Top 1 Accuracy":"84.0%"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"ResNet50_vd_ssld","rank_in_archive_order":477,"of":1060,"metrics":{"Number of params":"25.58M","Top 1 Accuracy":"83.0%"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-imagenet","task":"Image Classification","dataset":"ImageNet","model":"MobileNetV3_large_x1_0_ssld","rank_in_archive_order":790,"of":1060,"metrics":{"Number of params":"5.47M","Top 1 Accuracy":"79.0%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}