MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
作者： Marco Andreetto , Tobias Weyand , Hartwig Adam , Menglong Zhu , Dmitry Kalenichenko
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摘要: We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and …
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