{"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/mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","arxiv_id":"1704.04861","date":"2017-04-17","proceeding":null,"authors":["Andrew G. Howard","Menglong Zhu","Bo Chen","Dmitry Kalenichenko","Weijun Wang","Tobias Weyand","Marco Andreetto","Hartwig Adam"],"abstract":"We present a class of efficient models called MobileNets for mobile and\nembedded vision applications. MobileNets are based on a streamlined\narchitecture that uses depth-wise separable convolutions to build light weight\ndeep neural networks. We introduce two simple global hyper-parameters that\nefficiently trade off between latency and accuracy. These hyper-parameters\nallow the model builder to choose the right sized model for their application\nbased on the constraints of the problem. We present extensive experiments on\nresource and accuracy tradeoffs and show strong performance compared to other\npopular models on ImageNet classification. We then demonstrate the\neffectiveness of MobileNets across a wide range of applications and use cases\nincluding object detection, finegrain classification, face attributes and large\nscale geo-localization.","url_abs":"http://arxiv.org/abs/1704.04861v1","url_pdf":"http://arxiv.org/pdf/1704.04861v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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