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.
The optimizer is chosen as Adam here, instead of the SGD with momentum in the paper.
CUDA_VISIBLE_DEVICES=0 python main.py --exp_id=0 --database=LIVE --model=CNNIQAplusplusBefore training, the im_dir in config.yaml must to be specified.
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 PCconda 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.
- Simplify the code
- Report results on some common databases
- etc.
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