TorchVision models

Source: Pytorch/Vision repo

AlexNet

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NIPS), 2012, pp. 1097–1105.

@inproceedings{krizhevsky2012imagenet,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  booktitle={Advances in Neural Information Processing Systems (NIPS)},
  pages={1097--1105},
  year={2012}
}

• alexnet(num_classes=1000, pretrained='imagenet')`

DenseNet*

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, no. 2. IEEE, 2017, p. 3.

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q and van der Maaten, Laurens},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  volume={1},
  number={2},
  pages={3},
  year={2017},
  organization={IEEE}
}

• densenet121(num_classes=1000, pretrained='imagenet')
• densenet161(num_classes=1000, pretrained='imagenet')
• densenet169(num_classes=1000, pretrained='imagenet')
• densenet201(num_classes=1000, pretrained='imagenet')

ResNet*

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016, pp. 770–778.

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={770--778},
  year={2016},
  organization={IEEE}

• resnet18(num_classes=1000, pretrained='imagenet')
• resnet34(num_classes=1000, pretrained='imagenet')
• resnet50(num_classes=1000, pretrained='imagenet')
• resnet101(num_classes=1000, pretrained='imagenet')
• resnet152(num_classes=1000, pretrained='imagenet')

SqueezeNet*

F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size,” arXiv preprint arXiv:1602.07360, 2016.

@article{iandola2016squeezenet,
  title={Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size},
  author={Iandola, Forrest N and Han, Song and Moskewicz, Matthew W and Ashraf, Khalid and Dally, William J and Keutzer, Kurt},
  journal={arXiv preprint arXiv:1602.07360},
  year={2016}
}

• squeezenet1_0(num_classes=1000, pretrained='imagenet')
• squeezenet1_1(num_classes=1000, pretrained='imagenet')

VGG*

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

@article{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}

• vgg11(num_classes=1000, pretrained='imagenet')
• vgg13(num_classes=1000, pretrained='imagenet')
• vgg16(num_classes=1000, pretrained='imagenet')
• vgg19(num_classes=1000, pretrained='imagenet')
• vgg11_bn(num_classes=1000, pretrained='imagenet')
• vgg13_bn(num_classes=1000, pretrained='imagenet')
• vgg16_bn(num_classes=1000, pretrained='imagenet')
• vgg19_bn(num_classes=1000, pretrained='imagenet')