Inception* models

Inception-v3

C.Szegedy, V.Vanhoucke, S.Ioffe, J.Shlens, and Z.Wojna,“Rethinking the inception architecture for computer vision,” in Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016, pp. 2818– 2826.

@inproceedings{szegedy2016rethinking,
  title={Rethinking the inception architecture for computer vision},
  author={Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jon and Wojna, Zbigniew},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={2818--2826},
  year={2016},
  organization={IEEE}
}

Inception-v4 and Inception-ResNet

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in International Conference Learning Representations (ICLR) Workshop, 2016.

@inproceedings{szegedy2016inc,
title	= {Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
author	= {Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex A. Alemi},
year	= {2016},
booktitle	= {International Conference Learning Representations (ICLR) Workshop}
}

Source: TensorFlow Slim repo and Pytorch/Vision repo for inceptionv3

• inceptionresnetv2(num_classes=1000, pretrained='imagenet')
• inceptionresnetv2(num_classes=1001, pretrained='imagenet+background')
• inceptionv4(num_classes=1000, pretrained='imagenet')
• inceptionv4(num_classes=1001, pretrained='imagenet+background')
• inceptionv3(num_classes=1000, pretrained='imagenet')