fffffarmer / ranknet_iqa

A simple IQA model implemented in Pytorch.

IQA with RankNet

The repo includes a simple framework to train a RankNet model for IQA tasks. It is the final project of CS386 (Digital Image Processing), SJTU.

Dataset

In this project, we use dataset ICME2020. Please refer to the sample dataset folder for data organization.

In experiments, we split the dataset into two parts: 1-80 scenes are used for training and 81-100 scenes are used for validation.

Train

Using config files

Modify the configurations in .json config files, then run:

python train.py --config config.json

Resuming from checkpoints

You can resume from a previously saved checkpoint by:

python train.py --resume path/to/checkpoint

Using Multiple GPU

You can enable multi-GPU training by setting n_gpu argument of the config file to larger number. If configured to use smaller number of gpu than available, first n devices will be used by default. Specify indices of available GPUs by cuda environmental variable.

python train.py --device 2,3 -c config.json

This is equivalent to

CUDA_VISIBLE_DEVICES=2,3 python train.py -c config.py

Test

You can test trained model by running test.py passing path to the trained checkpoint by --resume argument.

Reference

• Burges, Chris, et al. "Learning to rank using gradient descent." Proceedings of the 22nd international conference on Machine learning. 2005.
• Burges, Christopher JC. "From ranknet to lambdarank to lambdamart: An overview." Learning 11.23-581 (2010): 81.
• Ma, Kede, et al. "dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs." IEEE Transactions on Image Processing 26.8 (2017): 3951-3964.
• Su, Shaolin, et al. "Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

ranknet_iqa's Issues

求代码论文

请问您这个代码的论文是那篇论文呢？ 谢谢