Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

Last update: Oct 13, 2022

Related tags

Deep Learning DSN-IQA

Overview

DSN-IQA

Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

Requirements

Python >=3.8.0
Pytorch >=1.7.1

Usage with default setting

To train and test, directly use following code in 4 gpus situation:

CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 ddpWorker.py

Dataset

Most of them can be directly gained from Google.
For FLIVE, please visit here

Citation

To be continued...

Acknowledge

https://github.com/DensoITLab/ss-with-RIM

https://github.com/SSL92/hyperIQA

https://github.com/yichengsu/ICIP2020-WSP-IQA

Owner

No description~

GitHub Repository

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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