{"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/theano-based-large-scale-visual-recognition","title":"Theano-based Large-Scale Visual Recognition with Multiple GPUs","arxiv_id":"1412.2302","date":"2014-12-07","proceeding":null,"authors":["Weiguang Ding","Ruoyan Wang","Fei Mao","Graham Taylor"],"abstract":"In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012)\nimplementation and its naive data parallelism on multiple GPUs. Our performance\non 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014)\nrun on 1 GPU. To the best of our knowledge, this is the first open-source\nPython-based AlexNet implementation to-date.","url_abs":"http://arxiv.org/abs/1412.2302v4","url_pdf":"http://arxiv.org/pdf/1412.2302v4.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":"theano-based-large-scale-visual-recognition","repo_url":"https://github.com/uoguelph-mlrg/theano_alexnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"theano-based-large-scale-visual-recognition","repo_url":"https://github.com/uoguelph-mlrg/theano_multi_gpu","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}