Awesome
CKDN
The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment"
Our trained model can be found in Model
The PIPAL dataset can be found in Here; our matched degraded images can be downloaded in Here. Please put all images into one folder.
To train the model, please run:
bash train.sh
To evaluate the model, please run:
bash val.sh
To predict the quality score for an image/folder, please:
- put degraded images into 'data_folder/degraded' and restored images into 'data_folder/restored' (with the same file name).
- run: bash predict_score.sh
Credits
This code is based on pytorch-image-models
Citation
@article{zheng2021learning,
title={Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment},
author={Zheng, Heliang and Fu, Jianlong and Zeng, Yanhong and Zha, Zheng-Jun and Luo, Jiebo},
journal={ICCV},
year={2021}
}