We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization. Andrew G. Howard, et al., "Mobile…

We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. Andrew G. Howard, et al., "MobileNets: Efficient Convolutional Neural Networks for…

These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. Andrew G. Howard, et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision …

We introduce two simple global hyperparameters that efficiently trade off between latency and accuracy. Andrew G. Howard, et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" https://arxiv.org/abs/17…

MobileNets are based on a streamlined architecture that uses depthwise separable convolutions to build light weight deep neural networks. Andrew G. Howard, et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Appl…

We present a class of efficient models called MobileNets for mobile and embedded vision applications. Andrew G. Howard, et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" https://arxiv.org/abs/1704…