Jinjin Gu, Haoming Cai, Haoyu Chen, Haoyu Chen, Jimmy S.Ren, Chao Dong. In ECCV, 2020.

• ECCV 2020 Paper | Journal Version | Project Web | NTIRE 2021 Challenge.
• If you have any questions, please contact with [email protected]

• [2022-Jan-02] Update the README. I will recorrect those errors mentioned in the issues recently. Moreover, we will extend the PIPAL dataset with more degradations for the NTIRE 2022 (We are still waiting for further notes from the NTIRE organizer).

• [2021-Feb] We are organizing NTIRE 2021 Perceptual IQA Challenge !!.

• [2021-Feb] ❗️ ❗️ This codebase ONLY supports users to train LPIPS on PIPAL or BAPPS for now. The SWD module will be added in the future.

• Train [Google Drive]
• Valid [Google Drive]
• Test [Coming Soon]

• Python 3 (Recommend to use Anaconda)
• PyTorch >= 1.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb pyyaml
• TensorBoard:
  • PyTorch >= 1.1: pip install tb-nightly future
  • PyTorch == 1.0: pip install tensorboardX

• Train Your IQA
  1. Prepare IQA dataset PIPAL public training [NTIRE 2021] or BAPPS. More details are in codes/data.
  2. Modify the dataset format based on your need in codes/data/PairedTrain_dataset.py and ValidorTest_dataset.py
  3. Modify the configuration file codes/options/train_test_yml/train_our_IQA.yml
  4. Run command:

   python train.py -opt options/train_test_yml/train_IQA.yml

• Test your IQA
  1. Prepare IQA dataset PIPAL public validation [NTIRE 2021]. More details are in codes/data.
  2. Modify the dataset format based on your need in ValidorTest_dataset.py
  3. Modify the configuration file codes/options/train_test_yml/test_IQA.yml
  4. Run command:

   python test.py -opt options/train_test_yml/test_IQA.yml

• This code is based on mmsr.