RMNet: Equivalently Removing Residual Connection from Networks

This repository is the official implementation of "RMNet: Equivalently Removing Residual Connection from Networks".

Requirements

To install requirements:

pip install torch
pip install torchvision

Training

To train the models in the paper, run this command:

python train.py -a rmrep_69 --dist-url 'tcp://127.0.0.1:23333' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 --workers 32 [imagenet-folder with train and val folders]

Our Pre-trained Models

You can download pretrained models here:

• Download our pre-trained models trained on ImageNet from Google Drive or Baidu Cloud(提取码:0mto).

Evaluation

To evaluate our pre-trained models trained on ImageNet, run:

python train.py -a rmrep_69 -e checkpoint/rmrep_69.pth.tar [imagenet-folder with train and val folders]

Results

Our model achieves the following performance on :

Help RepVGG achieve better performance even when the depth is large

Image Classification on ImageNet

Citation

If you find this code useful, please cite the following paper:

@misc{meng2021rmnet,
      title={RMNet: Equivalently Removing Residual Connection from Networks}, 
      author={Fanxu Meng and Hao Cheng and Jiaxin Zhuang and Ke Li and Xing Sun},
      year={2021},
      eprint={2111.00687},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Contributing

Our code is based on RepVGG