MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto and Hartwig Adam submitted MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications on April 17, 2017. The paper presents MobileNets as efficient models for mobile and embedded vision applications, based on depthwise separable convolutions and two global hyperparameters for trading off latency and accuracy.