MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. .

Paper abstract bibtex

We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depthwise separable convolutions to build light weight deep neural networks. We introduce two simple global hyperparameters 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 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 cases including object detection, finegrain classification, face attributes and large scale geo-localization.

@online{mobilenet,
  title = {{{MobileNets}}: {{Efficient Convolutional Neural Networks}} for {{Mobile Vision Applications}}},
  shorttitle = {{{MobileNets}}},
  author = {Howard, Andrew G. and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig},
  date = {2017-04-16},
  eprint = {1704.04861},
  eprinttype = {arxiv},
  eprintclass = {cs},
  url = {http://arxiv.org/abs/1704.04861},
  urldate = {2023-11-23},
  abstract = {We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depthwise separable convolutions to build light weight deep neural networks. We introduce two simple global hyperparameters 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 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 cases including object detection, finegrain classification, face attributes and large scale geo-localization.},
  langid = {english},
  pubstate = {preprint},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {/Users/unaiaguinaco/Zotero/storage/5QC9D3F5/Howard et al. - 2017 - MobileNets Efficient Convolutional Neural Network.pdf}
}

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