TorchVision models

Source: Pytorch/Vision repo

(inceptionv3 included in Inception*)

AlexNet

``
- `alexnet(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')`

## 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')`
## SqueezeNet*
- `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')`