CV Backbones
including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab.
- GhostNet Code
- TinyNet Code
- TNT Code
- PyramidTNT Code
- LegoNet Code
- Versatile Filters Code
- Citation
- Other versions
News
2022/01/05 PyramidTNT: An improved TNT baseline is released.
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.
2021/08/26 The codes of LegoNet and Versatile Filters has been merged into this repo.
2021/06/15 The code of TNT (Transformer in Transformer) has been released in this repo.
2020/10/31 GhostNet+TinyNet achieves better performance. See details in our NeurIPS 2020 paper: arXiv.
2020/06/10 GhostNet is included in PyTorch Hub.
GhostNet Code
This repo provides GhostNet pretrained models and inference code for TensorFlow and PyTorch:
- Tensorflow: ./ghostnet_tensorflow with pretrained model.
- PyTorch: ./ghostnet_pytorch with pretrained model.
- We also opensource code on MindSpore Hub and MindSpore Model Zoo.
For training, please refer to tinynet or timm.
TinyNet Code
This repo provides TinyNet pretrained models and inference code for PyTorch:
- PyTorch: ./tinynet_pytorch with pretrained model.
- We also opensource training code on MindSpore Model Zoo.
TNT Code
This repo provides training code and pretrained models of TNT (Transformer in Transformer) for PyTorch:
- PyTorch: ./tnt_pytorch.
- We also opensource code on MindSpore Model Zoo.
The code of PyramidTNT is also released:
- PyTorch: ./tnt_pytorch.
LegoNet Code
This repo provides the implementation of paper LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ICML 2019)
- PyTorch: ./legonet_pytorch.
Versatile Filters Code
This repo provides the implementation of paper Learning Versatile Filters for Efficient Convolutional Neural Networks (NeurIPS 2018)
- PyTorch: ./versatile_filters.
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}
}
Other versions of GhostNet
This repo provides the TensorFlow/PyTorch code of GhostNet. Other versions and applications can be found in the following:
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10 Dec 10, 2022
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