{"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/fine-tuning-deep-cnn-models-on-specific-ms","title":"Fine-tuning deep CNN models on specific MS COCO categories","arxiv_id":"1709.01476","date":"2017-09-05","proceeding":null,"authors":["Daniel Sonntag","Michael Barz","Jan Zacharias","Sven Stauden","Vahid Rahmani","Áron Fóthi","András Lőrincz"],"abstract":"Fine-tuning of a deep convolutional neural network (CNN) is often desired.\nThis paper provides an overview of our publicly available py-faster-rcnn-ft\nsoftware library that can be used to fine-tune the VGG_CNN_M_1024 model on\ncustom subsets of the Microsoft Common Objects in Context (MS COCO) dataset.\nFor example, we improved the procedure so that the user does not have to look\nfor suitable image files in the dataset by hand which can then be used in the\ndemo program. Our implementation randomly selects images that contain at least\none object of the categories on which the model is fine-tuned.","url_abs":"http://arxiv.org/abs/1709.01476v1","url_pdf":"http://arxiv.org/pdf/1709.01476v1.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":"fine-tuning-deep-cnn-models-on-specific-ms","repo_url":"https://github.com/DFKI-Interactive-Machine-Learning/py-faster-rcnn-ft","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}