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huawei-noah · May 22, 2023 · 1836d22 · 1836d22
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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](#news)
 - [Model zoo](#model-zoo)
 - [Citation](#citation)
 - [Other versions](#other-versions-of-ghostnet)
 
-**News**
+## News
 
 2022/12/01 The code of NeurIPS 2022 (Spotlight) [GhostNetV2](https://arxiv.org/abs/2211.12905) is released at [./ghostnetv2_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv2_pytorch).
 
@@ -22,25 +23,24 @@ including GhostNet, TNT (Transformer in Transformer), AugViT, WaveMLP and ViG de
 
 2020/10/31 GhostNet+TinyNet achieves better performance. See details in our NeurIPS 2020 paper: [arXiv](https://arxiv.org/abs/2010.14819).
 
----
 
 ## Model zoo
 
-| Model | Paper | Pytorch code | Tensorflow code | MindSpore code |
-| - | - | - | - | - |
-| GhostNet | GhostNet: More Features from Cheap Operations. [[CVPR 2020]](https://arxiv.org/abs/1911.11907) | [./ghostnet_pytorch](https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch) | [./ghostnet_tensorflow](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnet_tensorflow) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ghostnet) |
-| GhostNetV2 | GhostNetV2: Enhance Cheap Operation with Long-Range Attention. [[NeurIPS 2022 Spotlight]](https://arxiv.org/abs/2211.12905) | [./ghostnetv2_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv2_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ghostnetv2) |
-| G-GhostNet | GhostNets on Heterogeneous Devices via Cheap Operations. [[IJCV 2022]](https://arxiv.org/abs/2201.03297) | [./g_ghost_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/g_ghost_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ghostnet_d) |
-| TinyNet | Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets. [[NeurIPS 2020]](https://arxiv.org/abs/2010.14819) | [./tinynet_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tinynet_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/tinynet) |
-| TNT | Transformer in Transformer. [[NeurIPS 2021]](https://arxiv.org/abs/2103.00112) | [./tnt_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/TNT) |
-| PyramidTNT | PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture. [[CVPR 2022 Workshop]](https://arxiv.org/abs/2201.00978)| [./tnt_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/TNT) |
-| CMT | CMT: Convolutional Neural Networks Meet Vision Transformers. [[CVPR 2022]](https://arxiv.org/pdf/2107.06263.pdf) | [./cmt_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/cmt_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/CMT) |
-| AugViT | Augmented Shortcuts for Vision Transformers. [[NeurIPS 2021]](https://proceedings.neurips.cc/paper/2021/file/818f4654ed39a1c147d1e51a00ffb4cb-Paper.pdf) | [./augvit_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/augvit_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/augvit) |
-| SNN-MLP | Brain-inspired Multilayer Perceptron with Spiking Neurons. [[CVPR 2022]](https://arxiv.org/pdf/2203.14679.pdf) | [./snnmlp_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/snnmlp_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/snn_mlp) |
-| WaveMLP | An Image Patch is a Wave: Quantum Inspired Vision MLP. [[CVPR 2022]](https://arxiv.org/pdf/2111.12294.pdf) | [./wavemlp_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/wavemlp_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/wave_mlp) |
+| Model | Paper | Pytorch code | MindSpore code |
+| - | - | - | - |
+| GhostNet | GhostNet: More Features from Cheap Operations. [[CVPR 2020]](https://arxiv.org/abs/1911.11907) | [./ghostnet_pytorch](https://github.com/huawei-noah/CV-backbones/tree/master/ghostnet_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ghostnet) |
+| GhostNetV2 | GhostNetV2: Enhance Cheap Operation with Long-Range Attention. [[NeurIPS 2022 Spotlight]](https://arxiv.org/abs/2211.12905) | [./ghostnetv2_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/ghostnetv2_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ghostnetv2) |
+| G-GhostNet | GhostNets on Heterogeneous Devices via Cheap Operations. [[IJCV 2022]](https://arxiv.org/abs/2201.03297) | [./g_ghost_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/g_ghost_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ghostnet_d) |
+| TinyNet | Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets. [[NeurIPS 2020]](https://arxiv.org/abs/2010.14819) | [./tinynet_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tinynet_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/tinynet) |
+| TNT | Transformer in Transformer. [[NeurIPS 2021]](https://arxiv.org/abs/2103.00112) | [./tnt_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/TNT) |
+| PyramidTNT | PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture. [[CVPR 2022 Workshop]](https://arxiv.org/abs/2201.00978)| [./tnt_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/TNT) |
+| CMT | CMT: Convolutional Neural Networks Meet Vision Transformers. [[CVPR 2022]](https://arxiv.org/pdf/2107.06263.pdf) | [./cmt_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/cmt_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/CMT) |
+| AugViT | Augmented Shortcuts for Vision Transformers. [[NeurIPS 2021]](https://proceedings.neurips.cc/paper/2021/file/818f4654ed39a1c147d1e51a00ffb4cb-Paper.pdf) | [./augvit_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/augvit_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/augvit) |
+| SNN-MLP | Brain-inspired Multilayer Perceptron with Spiking Neurons. [[CVPR 2022]](https://arxiv.org/pdf/2203.14679.pdf) | [./snnmlp_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/snnmlp_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/snn_mlp) |
+| WaveMLP | An Image Patch is a Wave: Quantum Inspired Vision MLP. [[CVPR 2022]](https://arxiv.org/pdf/2111.12294.pdf) | [./wavemlp_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/wavemlp_pytorch) | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/wave_mlp) |
 | ViG | Vision GNN: An Image is Worth Graph of Nodes. [[NeurIPS 2022]](https://arxiv.org/abs/2206.00272) | [./vig_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/vig_pytorch) | - | [MindSpore Model Zoo](https://gitee.com/mindspore/models/tree/master/research/cv/ViG) |
-| LegoNet | LegoNet: Efficient Convolutional Neural Networks with Lego Filters. [[ICML 2019]](http://proceedings.mlr.press/v97/yang19c/yang19c.pdf) | [./legonet_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/legonet_pytorch) | - | - |
-| Versatile Filters | Learning Versatile Filters for Efficient Convolutional Neural Networks. [[NeurIPS 2018]](https://papers.nips.cc/paper/7433-learning-versatile-filters-for-efficient-convolutional-neural-networks) | [./versatile_filters](https://github.com/huawei-noah/CV-backbones/tree/master/versatile_filters) | - | - |
+| LegoNet | LegoNet: Efficient Convolutional Neural Networks with Lego Filters. [[ICML 2019]](http://proceedings.mlr.press/v97/yang19c/yang19c.pdf) | [./legonet_pytorch](https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/legonet_pytorch) | - |
+| Versatile Filters | Learning Versatile Filters for Efficient Convolutional Neural Networks. [[NeurIPS 2018]](https://papers.nips.cc/paper/7433-learning-versatile-filters-for-efficient-convolutional-neural-networks) | [./versatile_filters](https://github.com/huawei-noah/CV-backbones/tree/master/versatile_filters) | - |
 
 ## Citation
 ```