Blueprint Separable Residual Network for Efficient Image Super-Resolution
Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Jinjin Gu, Yu Qiao, Chao Dong
BibTex
@InProceedings{Li_2022_CVPR,
author = {Li, Zheyuan and Liu, Yingqi and Chen, Xiangyu and Cai, Haoming and Gu, Jinjin and Qiao, Yu and Dong, Chao},
title = {Blueprint Separable Residual Network for Efficient Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2022},
pages = {833-843}
}
PyTorch >= 1.7
BasicSR >= 1.3.4.9
pip install -r requirements.txt
python setup.py develop
· Refer to ./options/test for the configuration file of the model to be tested, and prepare the testing data and pretrained model.
· The pretrained models are available in ./experiments/pretrained_models/
· Then run the follwing codes (taking net_g_BSRN_x4.pth as an example):
python basicsr/test.py -opt options/test/benchmark_BSRN_x4.yml
The testing results will be saved in the ./results folder.
· Refer to ./options/train for the configuration file of the model to train.
· Preparation of training data can refer to this page. All datasets can be downloaded at the official website.
· Note that the default training dataset is based on lmdb, refer to docs in BasicSR to learn how to generate the training datasets.
· The training command is like
CUDA_VISIBLE_DEVICES=0 python basicsr/train.py -opt options/train/train_BSRN_x4.yml
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 basicsr/train.py -opt options/train/train_BSRN-S_x4.yml --launcher pytorch
For more training commands and details, please check the docs in BasicSR
The inference results on benchmark datasets are available at Google Drive or Baidu Netdisk (access code: VISU).
If you have any question, please email [email protected] or join in the Wechat group of BasicSR to discuss with the authors.