Architectures #

MobileNetV2#

MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an inverted residual structure where the residual connections are between the bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. As a whole, the architecture of MobileNetV2 contains the initial fully convolution layer with 32 filters, followed by 19 residual bottleneck layers.

Architecture Configuration Parameters Model Size
MobileNetV2 default 2.3 M 9 MB

EfficientNet#

EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks.

Architecture Configuration Parameters Model Size
EfficientNet b0 4.1 M 16 MB
EfficientNet b1 6.6 M 26 MB
EfficientNet b2 7.8 M 30 MB
EfficientNet b3 10.8 M 42 MB
EfficientNet b4 17.6 M 70 MB
EfficientNet b5 28.4 M 113 MB
EfficientNet b6 40.8 M 163 MB
EfficientNet b7 63.8 M 225 MB
EfficientNet v2-s 20.2 M 80 MB
EfficientNet v2-m 52.9 M 211 MB
EfficientNet v2-l 117 M 468 MB

ResNet#

Residual Networks, or ResNets, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack residual blocks ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.

Architecture Configuration Parameters Model Size
ResNet 18 14.0 M 56 MB
ResNet 34 45.9 M 183 MB
ResNet 50 23.6 M 94 MB
ResNet 101 42.6 M 170 MB
ResNet 152 58.2 M 232 MB

Citation#

@article{DBLP:journals/corr/abs-1801-04381,
  author    = {Mark Sandler and
               Andrew G. Howard and
               Menglong Zhu and
               Andrey Zhmoginov and
               Liang{-}Chieh Chen},
  title     = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification,
               Detection and Segmentation},
  journal   = {CoRR},
  volume    = {abs/1801.04381},
  year      = {2018},
  url       = {http://arxiv.org/abs/1801.04381},
  archivePrefix = {arXiv},
  eprint    = {1801.04381},
  timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1801-04381.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1905-11946,
  author    = {Mingxing Tan and
               Quoc V. Le},
  title     = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1905.11946},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.11946},
  eprinttype = {arXiv},
  eprint    = {1905.11946},
  timestamp = {Mon, 03 Jun 2019 13:42:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-11946.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/HeZRS15,
  author    = {Kaiming He and
               Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  journal   = {CoRR},
  volume    = {abs/1512.03385},
  year      = {2015},
  url       = {http://arxiv.org/abs/1512.03385},
  eprinttype = {arXiv},
  eprint    = {1512.03385},
  timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}