Architectures of convolutional neural networks for image classification in PyTorch

• VGGNet

  Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014

  https://arxiv.org/pdf/1409.1556v6.pdf

• InceptionNet-v1 (GoogLeNet)

  Going Deeper with Convolutions, 2014

  https://arxiv.org/pdf/1409.4842v1.pdf

• ResNet

  Deep Residual Learning for Image Recognition, 2015

  https://arxiv.org/pdf/1512.03385v1.pdf

• ResNeXt

  Aggregated Residual Transformations for Deep Neural Networks, 2016

  https://arxiv.org/pdf/1611.05431.pdf

• SqueezeNet-v1

  SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and 0.5Mb Model Size, 2016

  https://arxiv.org/pdf/1602.07360v4.pdf

• DenseNet

  Densely Connected Convolutional Networks, 2017

  https://arxiv.org/pdf/1608.06993v5.pdf

• MobileNet-v1

  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017

  https://arxiv.org/pdf/1704.04861v1.pdf

• ShuffleNet-v1

  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, 2017

  https://arxiv.org/pdf/1707.01083v2.pdf

• SE-ResNet and SENet

  Squeeze-and-Excitation Networks, 2017

  https://arxiv.org/pdf/1709.01507v4.pdf

• SK-ResNet and SKNet

  Selective Kernel Networks, 2019

  https://arxiv.org/pdf/1903.06586v2.pdf