{"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/imagenet-pre-trained-models-with-batch","title":"ImageNet pre-trained models with batch normalization","arxiv_id":"1612.01452","date":"2016-12-05","proceeding":null,"authors":["Marcel Simon","Erik Rodner","Joachim Denzler"],"abstract":"Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone\nof most state-of-the-art approaches. In this paper, we present a new set of\npre-trained models with popular state-of-the-art architectures for the Caffe\nframework. The first release includes Residual Networks (ResNets) with\ngeneration script as well as the batch-normalization-variants of AlexNet and\nVGG19. All models outperform previous models with the same architecture. The\nmodels and training code are available at\nhttp://www.inf-cv.uni-jena.de/Research/CNN+Models.html and\nhttps://github.com/cvjena/cnn-models","url_abs":"http://arxiv.org/abs/1612.01452v2","url_pdf":"http://arxiv.org/pdf/1612.01452v2.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":"imagenet-pre-trained-models-with-batch","repo_url":"https://github.com/cvjena/cnn-models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"imagenet-pre-trained-models-with-batch","repo_url":"https://github.com/ChuuyaZZZ/6787-Final-project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"imagenet-pre-trained-models-with-batch","repo_url":"https://github.com/TiantianWang/ICCV17_SRM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"imagenet-pre-trained-models-with-batch","repo_url":"https://github.com/fdac18/ForensicImages","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"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":{"syntology_url":"https://syntology.ai/paper/1612.01452","atlas_url":"https://app.syntology.ai/?focus=1612.01452","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}