Corpus ID: 12670695

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

@article{Howard2017MobileNetsEC,
  title={MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications},
  author={A. Howard and Menglong Zhu and Bo Chen and D. Kalenichenko and W. Wang and Tobias Weyand and M. Andreetto and H. Adam},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.04861}
}
  • A. Howard, Menglong Zhu, +5 authors H. Adam
  • Published 2017
  • Computer Science
  • ArXiv
  • We present a class of efficient models called MobileNets for mobile and embedded vision applications. [...] Key Method 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 accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use…Expand Abstract
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    References

    SHOWING 1-10 OF 39 REFERENCES
    Quantized Convolutional Neural Networks for Mobile Devices
    • 560
    • PDF
    Very Deep Convolutional Networks for Large-Scale Image Recognition
    • 41,521
    • PDF
    Rethinking the Inception Architecture for Computer Vision
    • 9,260
    • Highly Influential
    • PDF
    Speeding up Convolutional Neural Networks with Low Rank Expansions
    • 878
    • PDF
    Structured Transforms for Small-Footprint Deep Learning
    • 160
    • PDF
    Caffe: Convolutional Architecture for Fast Feature Embedding
    • 12,312
    • PDF
    Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
    • 1,440
    • PDF
    Going deeper with convolutions
    • 20,952
    • Highly Influential
    • PDF
    Factorized Convolutional Neural Networks
    • 91
    • PDF
    XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
    • 1,492
    • PDF