Conv2Former

The official implementation of the paper “Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition“. Our code is based on timm and ConvNeXt.

Our paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

Usage

Requirements

• torch>=1.12.1
• torchvision>=0.13.1
• timm>=0.9.12

Examples

from models.conv2former import conv2former_n

# create a Conv2Former model with 1000 classes
model = conv2former_n(num_classes=1000) 

Input image should be normalized as follows:

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                  std=[0.229, 0.224, 0.225])

We also provide the Jittor version of the model:

import jittor as jt
from jittor_models.conv2former import conv2former_n
   
# create a Conv2Former model with 1000 classes
model = conv2former_n(num_classes=1000) 

Training

bash distributed_train.sh 8 $DATA_DIR --model $MODEL -b $BS --lr $LR --drop-path $DROP_PATH

# DATA_DIR: path to the dataset
# MODEL: name of the model
# BS: batch size
# LR: learning rate
# DROP_PATH: drop path rate

validation

python validate.py $DATA_DIR --model $MODEL --checkpoint $CHECKPOINT

# DATA_DIR: path to the dataset
# MODEL: name of the model
# CHECKPOINT: path to the saved checkpoint

Results

Training on ImageNet-1k

Model	Parameters	FLOPs	Image resolution	Top 1 Acc.	Model File
Conv2Former-N	15M	2.2G	224	81.5%	baiduyun
SwinT-T	28M	4.5G	224	81.5%	-
ConvNeXt-T	29M	4.5G	224	82.1%	-
Conv2Former-T	27M	4.4G	224	83.2%	baiduyun
SwinT-S	50M	8.7G	224	83.0%	-
ConvNeXt-S	50M	8.7G	224	83.1%	-
Conv2Former-S	50M	8.7G	224	84.1%	baiduyun
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%	baiduyun

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%	baiduyun
SwinT-B	88M	15.4G	224	85.2%	-
ConvNeXt-B	89M	15.4G	224	85.8%	-
Conv2Former-B	90M	15.9G	224	86.2%	baiduyun
SwinT-B	88M	47.0G	384	86.4%	-
ConvNeXt-B	89M	45.1G	384	86.8%	-
Conv2Former-B	90M	46.7G	384	87.0%	-
SwinT-L	197M	34.5G	224	86.3%	-
ConvNeXt-L	198M	34.4G	224	86.6%	-
Conv2Former-L	199M	36.0G	224	87.0%	baiduyun
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%	baiduyun

Citation

If you find this work or code is helpful in your research, please cite:

@article{hou2024conv2former,
  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={IEEE TPAMI},
  year={2024},
  doi={10.1109/TPAMI.2024.3401450}, 
}

Reference

You may want to cite:

@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},
}