CNNIQAplusplus

PyTorch 1.3 implementation of the following paper: Kang, Le, et al. "Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks." IEEE International Conference on Image Processing IEEE, 2015:2791-2795.

Note

The optimizer is chosen as Adam here, instead of the SGD with momentum in the paper.

Training

CUDA_VISIBLE_DEVICES=0 python main.py --exp_id=0 --database=LIVE --model=CNNIQAplusplus

Before training, the im_dir in config.yaml must to be specified.

Visualization

tensorboard --logdir=tensorboard_logs --port=6006 # in the server (host:port)
ssh -p port -L 6006:localhost:6006 user@host # in your PC. See the visualization in your PC

Requirements

conda create -n reproducibleresearch pip python=3.6
source activate reproducibleresearch
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
source deactive

• Python 3.6.8
• PyTorch 1.3.0
• TensorboardX 1.9, TensorFlow 2.0.0
• pytorch/ignite 0.2.1

Note: You need to install the right CUDA version.

TODO (If I have free time)

• Simplify the code
• Report results on some common databases
• etc.