Large Kernel Frequency-enhanced Network for Efficient Single Image Super-Resolution
Jiadi Chen, Chunjiang Duanmu and Huanhuan Long
pip install -r requirements.txt
python setup.py develop
./options/test/LKFN
for the configuration file of the model to be tested, and prepare the testing data and pretrained model../experiments/pretrained_models/LKFN
.LKFN_x4.pth
as an example):python basicsr/test.py -opt options/test/LKFN/test_LKFN_x4.yml
The testing results will be saved in the ./results
folder.
./options/train
for the configuration file of the model to train.python basicsr/train.py -opt options/train/LKFN/train_LKFN_x4.yml
More training commands can refer to this page.
The training logs and weights will be saved in the ./experiments
folder.
If you have any question, please email zjnucjd@163.com.