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Conv2Former

Our code is based on timm and ConvNeXt.

More code will be released soon.

Results

Training on ImageNet-1k

Model Parameters FLOPs Image resolution Top 1 Acc. Model File
Conv2Former-N 15M 2.2G 224 81.5% Comming soom
SwinT-T 28M 4.5G 224 81.5% -
ConvNeXt-T 29M 4.5G 224 82.1% -
Conv2Former-T 27M 4.4G 224 83.2% Comming soom
SwinT-S 50M 8.7G 224 83.0% -
ConvNeXt-S 50M 8.7G 224 83.1% -
Conv2Former-S 50M 8.7G 224 84.1% Comming soom
RepLKNet-31B 79M 15.3G 224 83.5% -
SwinT-B 88M 15.4G 224 83.5% -
ConvNeXt-B 89M 15.4G 224 83.8% -
FocalNet-B 89M 15.4G 224 83.9% -
Conv2Former-B 90M 15.9G 224 84.4% Comming soom

Pre-Training on ImageNet-22k and Finetining on ImageNet-1k

Model Parameters FLOPs Image resolution Top 1 Acc. Model File
ConvNeXt-S 50M 8.7G 224 84.6% -
Conv2Former-S 50M 8.7G 224 84.9% Comming soom
SwinT-B 88M 15.4G 224 85.2% -
ConvNeXt-B 89M 15.4G 224 85.8% -
Conv2Former-B 90M 15.9G 224 86.2% Comming soom
SwinT-B 88M 47.0G 384 86.4% -
ConvNeXt-B 89M 45.1G 384 86.8% -
Conv2Former-B 90M 46.7G 384 87.0% Comming soom
SwinT-L 197M 34.5G 224 86.3% -
ConvNeXt-L 198M 34.4G 224 86.6% -
Conv2Former-L 199M 36.0G 224 87.0% Comming soom
EffNet-V2-XL 208M 94G 480 87.3% -
SwinT-L 197M 104G 384 87.3% -
ConvNeXt-L 198M 101G 384 87.5% -
CoAtNet-3 168M 107G 384 87.6% -
Conv2Former-L 199M 106G 384 87.7% Comming soom

Reference

You may want to cite:

@article{hou2022conv2former,
  title={Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition},
  author={Hou, Qibin and Lu, Cheng-Ze and Cheng, Ming-Ming and Feng, Jiashi},
  journal={arXiv preprint arXiv:2211.11943},
  year={2022}
}

@inproceedings{liu2022convnet,
      title={A ConvNet for the 2020s}, 
      author={Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
      booktitle=CVPR,
      year={2022}
}

@inproceedings{liu2021swin,
  title={Swin transformer: Hierarchical vision transformer using shifted windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle=ICCV,
  year={2021}
}

@inproceedings{tan2021efficientnetv2,
  title={Efficientnetv2: Smaller models and faster training},
  author={Tan, Mingxing and Le, Quoc},
  booktitle=ICML,
  pages={10096--10106},
  year={2021},
  organization={PMLR}
}

@misc{focalmnet,
  author = {Yang, Jianwei and Li, Chunyuan and Gao, Jianfeng},
  title = {Focal Modulation Networks},
  publisher = {arXiv},
  year = {2022},
}

@article{dai2021coatnet,
  title={Coatnet: Marrying convolution and attention for all data sizes},
  author={Dai, Zihang and Liu, Hanxiao and Le, Quoc and Tan, Mingxing},
  journal=NIPS,
  volume={34},
  year={2021}
}

@inproceedings{replknet,
  author = {Ding, Xiaohan and Zhang, Xiangyu and Zhou, Yizhuang and Han, Jungong and Ding, Guiguang and Sun, Jian},
  title = {Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs},
  booktitle=CVPR,
  year = {2022},
}