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We present a class of efficient models called MobileNets for mobile andembedded vision applications. MobileNets are based on a streamlinedarchitecture that uses depth-wise separable convolutions to build light weightdeep neural networks. We introduce two simple global hyper-parameters thatefficiently trade off between latency and accuracy. These hyper-parametersallow the model builder to choose the right sized model for their applicationbased on the constraints of the problem. We present extensive experiments onresource and accuracy tradeoffs and show strong performance compared to otherpopular models on ImageNet classification. We then demonstrate theeffectiveness of MobileNets across a wide range of applications and use casesincluding object detection, finegrain classification, face attributes and largescale geo-localization.

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications