LKFMixer: Exploring Large Kernel Feature For Efficient Image Super-Resolution

Environment in our experiments

[python 3.8]

[Ubuntu 20.04]

BasicSR 1.4.2

PyTorch 1.13.0, Torchvision 0.14.0, Cuda 11.7

Installation

git clone https://github.com/Supereeeee/LKFMixer.git
pip install -r requirements.txt
python setup.py develop

How To Test

· Refer to ./options/test for the configuration file of the model to be tested and prepare the testing data.

· The pre-trained models have been palced in ./experiments/pretrained_models/

· Then run the follwing codes for testing:

python basicsr/test.py -opt options/test/test_LKFMixer_x2.yml
python basicsr/test.py -opt options/test/test_LKFMixer_x3.yml
python basicsr/test.py -opt options/test/test_LKFMixer_x4.yml

The testing results will be saved in the ./results folder.

How To Train

· 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 following:

python basicsr/train.py -opt options/train/train_LKFMixer_x2.yml
python basicsr/train.py -opt options/train/train_LKFMixer_x3.yml
python basicsr/train.py -opt options/train/train_LKFMixer_x4.yml

For more training commands and details, please check the docs in BasicSR

Inference time

· You can run ./inference/main_inference.py on your decive to test the inference time.

Acknowledgement

This paper is inspired by PLKSR and the code is based onBasicSR. Thanks for the awesome work.

Contact

If you have any question, please email quanwei1277@163.com.