Residual-Attention-Network-for-Image-Classification-2D-3D-pytorch-implement

2D and 3D(volume) verison of Residual Attention Network

Residual Attention Network for Image Classification (CVPR-2017 Spotlight) By Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Chen Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang* (https://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Residual_Attention_Network_CVPR_2017_paper.pdf)

Use Examples:

2D:

• from attentionnet import attention56, attention92
• attention56(num_classes=1000)
• attention92(num_classes=1000)

3D:

• from attentionnet3D import attention3d56, attention3d92
• attention3d56(num_classes=1000)
• attention3d92(num_classes=1000)

For the usage in a library, please refer to my fork on pretorched (https://github.com/moyiliyi/pretorched-x)

Only the network architectures implemented here. You need to write your own train/test scripts.

Reference

This code is based on the following repos:

• https://github.com/weiaicunzai/pytorch-cifar100
• https://github.com/pytorch/vision/tree/master/torchvision
• https://github.com/Tencent/MedicalNet