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Efficient AI Backbones

including GhostNet, TNT (Transformer in Transformer), AugViT, WaveMLP and ViG developed by Huawei Noah's Ark Lab.

News

2022/06/17 The code of Vision GNN (ViG) is released at ./vig_pytorch.

2022/02/06 Transformer in Transformer is selected as the Most Influential NeurIPS 2021 Papers.

2022/01/06 The extended version of GhostNet is accepted by IJCV.

2021/09/28 The paper of TNT (Transformer in Transformer) is accepted by NeurIPS 2021.

2021/09/18 The extended version of Versatile Filters is accepted by T-PAMI.

2021/08/30 GhostNet paper is selected as the Most Influential CVPR 2020 Papers.

2020/10/31 GhostNet+TinyNet achieves better performance. See details in our NeurIPS 2020 paper: arXiv.


GhostNet Code

This repo provides GhostNet pretrained models and inference code for TensorFlow and PyTorch:

For training, please refer to tinynet or timm.

TinyNet Code

This repo provides TinyNet pretrained models and inference code for PyTorch:

TNT Code

This repo provides training code and pretrained models of TNT (Transformer in Transformer) for PyTorch:

The code of PyramidTNT is also released:

LegoNet Code

This repo provides the implementation of paper LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ICML 2019)

Versatile Filters Code

This repo provides the implementation of paper Learning Versatile Filters for Efficient Convolutional Neural Networks (NeurIPS 2018)

AugViT Code

This repo provides the implementation of paper Augmented Shortcuts for Vision Transformers (NeurIPS 2021)

WaveMLP Code

This repo provides the implementation of paper An Image Patch is a Wave: Quantum Inspired Vision MLP (CVPR 2022)

ViG Code

This repo provides the implementation of paper Vision GNN: An Image is Worth Graph of Nodes

Citation

@inproceedings{ghostnet,
  title={GhostNet: More Features from Cheap Operations},
  author={Han, Kai and Wang, Yunhe and Tian, Qi and Guo, Jianyuan and Xu, Chunjing and Xu, Chang},
  booktitle={CVPR},
  year={2020}
}
@inproceedings{tinynet,
  title={Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets},
  author={Han, Kai and Wang, Yunhe and Zhang, Qiulin and Zhang, Wei and Xu, Chunjing and Zhang, Tong},
  booktitle={NeurIPS},
  year={2020}
}
@inproceedings{tnt,
  title={Transformer in transformer},
  author={Han, Kai and Xiao, An and Wu, Enhua and Guo, Jianyuan and Xu, Chunjing and Wang, Yunhe},
  booktitle={NeurIPS},
  year={2021}
}
@inproceedings{legonet,
  title={LegoNet: Efficient Convolutional Neural Networks with Lego Filters},
  author={Yang, Zhaohui and Wang, Yunhe and Liu, Chuanjian and Chen, Hanting and Xu, Chunjing and Shi, Boxin and Xu, Chao and Xu, Chang},
  booktitle={ICML},
  year={2019}
}
@inproceedings{wang2018learning,
  title={Learning versatile filters for efficient convolutional neural networks},
  author={Wang, Yunhe and Xu, Chang and Chunjing, XU and Xu, Chao and Tao, Dacheng},
  booktitle={NeurIPS},
  year={2018}
}
@inproceedings{tang2021augmented,
  title={Augmented shortcuts for vision transformers},
  author={Tang, Yehui and Han, Kai and Xu, Chang and Xiao, An and Deng, Yiping and Xu, Chao and Wang, Yunhe},
  booktitle={NeurIPS},
  year={2021}
}
@inproceedings{tang2022image,
  title={An Image Patch is a Wave: Phase-Aware Vision MLP},
  author={Tang, Yehui and Han, Kai and Guo, Jianyuan and Xu, Chang and Li, Yanxi and Xu, Chao and Wang, Yunhe},
  booktitle={CVPR},
  year={2022}
}
@misc{vig,
  title={Vision GNN: An Image is Worth Graph of Nodes}, 
  author={Kai Han and Yunhe Wang and Jianyuan Guo and Yehui Tang and Enhua Wu},
  year={2022},
  eprint={2206.00272},
  archivePrefix={arXiv}
}

Other versions of GhostNet

This repo provides the TensorFlow/PyTorch code of GhostNet. Other versions and applications can be found in the following:

  1. timm: code with pretrained model
  2. Darknet: cfg file, and description
  3. Gluon/Keras/Chainer: code
  4. Paddle: code
  5. Bolt inference framework: benckmark
  6. Human pose estimation: code
  7. YOLO with GhostNet backbone: code
  8. Face recognition: cavaface, FaceX-Zoo, TFace