Giter Club home page Giter Club logo

efficient-ai-backbones's Introduction

Efficient AI Backbones

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

News

2022/12/01 The code of NeurIPS 2022 (Spotlight) GhostNetV2 is released at ./ghostnetv2_pytorch.

2022/11/13 The code of IJCV 2022 G-Ghost RegNet is released at ./vig_pytorch.

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

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

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

GhostNetV2 Code

This repo provides the implementation of paper GhostNetV2: Enhance Cheap Operation with Long-Range Attention (NeurIPS 2022 Spotlight)

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}
}
@inproceedings{han2022vig,
  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},
  booktitle={NeurIPS},
  year={2022}
}
@article{tang2022ghostnetv2,
  title={GhostNetV2: Enhance Cheap Operation with Long-Range Attention},
  author={Tang, Yehui and Han, Kai and Guo, Jianyuan and Xu, Chang and Xu, Chao and Wang, Yunhe},
  journal={arXiv preprint arXiv:2211.12905},
  year={2022}
}

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

efficient-ai-backbones's People

Contributors

iamhankai avatar yehuitang avatar gaffey avatar ggjy avatar yitongh avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.