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MobileNets: Efficient Convolutional Neural Networks for Mobile VisionApplicationsAndrew G. HowardMenglong ZhuBo ChenDmitry KalenichenkoWeijun WangTobias WeyandMarco AndreettoHartwig AdamGoogle present a class of efficient models called MobileNetsfor mobile and embedded vision applications . MobileNetsare based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deepneural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency andaccuracy. These hyper-parameters allow the model builderto choose the right sized model for their application basedon the constraints of the problem. We present extensiveexperiments on resource and accuracy tradeoffs and showstrong performance compared to other popular models onImageNet classification.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam
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