{"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/effnet-an-efficient-structure-for","title":"EffNet: An Efficient Structure for Convolutional Neural Networks","arxiv_id":"1801.06434","date":"2018-01-19","proceeding":null,"authors":["Ido Freeman","Lutz Roese-Koerner","Anton Kummert"],"abstract":"With the ever increasing application of Convolutional Neural Networks to\ncustomer products the need emerges for models to efficiently run on embedded,\nmobile hardware. Slimmer models have therefore become a hot research topic with\nvarious approaches which vary from binary networks to revised convolution\nlayers. We offer our contribution to the latter and propose a novel convolution\nblock which significantly reduces the computational burden while surpassing the\ncurrent state-of-the-art. Our model, dubbed EffNet, is optimised for models\nwhich are slim to begin with and is created to tackle issues in existing models\nsuch as MobileNet and ShuffleNet.","url_abs":"http://arxiv.org/abs/1801.06434v6","url_pdf":"http://arxiv.org/pdf/1801.06434v6.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":"effnet-an-efficient-structure-for","repo_url":"https://github.com/andrijdavid/EffNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"effnet-an-efficient-structure-for","repo_url":"https://github.com/MindCode-4/code-11/tree/main/dropblock-a-regularization-method","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"effnet-an-efficient-structure-for","repo_url":"https://github.com/MindCode-4/code-6/tree/main/effnet-an-efficient-structure","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"channel-shuffle","method_name":"Channel Shuffle"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"mobilenetv1","method_name":"MobileNetV1"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"shufflenet","method_name":"ShuffleNet"},{"method_slug":"shufflenet-block","method_name":"ShuffleNet Block"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.06434","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}