{"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/caffe-convolutional-architecture-for-fast","title":"Caffe: Convolutional Architecture for Fast Feature Embedding","arxiv_id":"1408.5093","date":"2014-06-20","proceeding":null,"authors":["Yangqing Jia","Evan Shelhamer","Jeff Donahue","Sergey Karayev","Jonathan Long","Ross Girshick","Sergio Guadarrama","Trevor Darrell"],"abstract":"Caffe provides multimedia scientists and practitioners with a clean and\nmodifiable framework for state-of-the-art deep learning algorithms and a\ncollection of reference models. The framework is a BSD-licensed C++ library\nwith Python and MATLAB bindings for training and deploying general-purpose\nconvolutional neural networks and other deep models efficiently on commodity\narchitectures. Caffe fits industry and internet-scale media needs by CUDA GPU\ncomputation, processing over 40 million images a day on a single K40 or Titan\nGPU ($\\approx$ 2.5 ms per image). By separating model representation from\nactual implementation, Caffe allows experimentation and seamless switching\namong platforms for ease of development and deployment from prototyping\nmachines to cloud environments. Caffe is maintained and developed by the\nBerkeley Vision and Learning Center (BVLC) with the help of an active community\nof contributors on GitHub. It powers ongoing research projects, large-scale\nindustrial applications, and startup prototypes in vision, speech, and\nmultimedia.","url_abs":"http://arxiv.org/abs/1408.5093v1","url_pdf":"http://arxiv.org/pdf/1408.5093v1.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":"caffe-convolutional-architecture-for-fast","repo_url":"https://github.com/BVLC/caffe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"caffe-convolutional-architecture-for-fast","repo_url":"https://github.com/yihui-he/exemplar-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1408.5093","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}