{"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/deeplogo-hitting-logo-recognition-with-the","title":"DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer","arxiv_id":"1510.02131","date":"2015-10-07","proceeding":null,"authors":["Forrest N. Iandola","Anting Shen","Peter Gao","Kurt Keutzer"],"abstract":"Recently, there has been a flurry of industrial activity around logo\nrecognition, such as Ditto's service for marketers to track their brands in\nuser-generated images, and LogoGrab's mobile app platform for logo recognition.\nHowever, relatively little academic or open-source logo recognition progress\nhas been made in the last four years. Meanwhile, deep convolutional neural\nnetworks (DCNNs) have revolutionized a broad range of object recognition\napplications. In this work, we apply DCNNs to logo recognition. We propose\nseveral DCNN architectures, with which we surpass published state-of-art\naccuracy on a popular logo recognition dataset.","url_abs":"http://arxiv.org/abs/1510.02131v1","url_pdf":"http://arxiv.org/pdf/1510.02131v1.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":[],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"logo-recognition","task_name":"Logo Recognition"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-flickrlogos-32","task":"Image Classification","dataset":"FlickrLogos-32","model":"DeepLogo (GoogLeNet-GP)","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"89.6"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-flickrlogos-32","task":"Object Detection","dataset":"FlickrLogos-32","model":"DeepLogo (VGG)","rank_in_archive_order":2,"of":3,"metrics":{"MAP":"74.4"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-flickrlogos-32","task":"Object Detection","dataset":"FlickrLogos-32","model":"DeepLogo (AlexNet)","rank_in_archive_order":3,"of":3,"metrics":{"MAP":"73.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1510.02131","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}