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

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

Related tags

Overview

DSN-IQA

Requirements

Usage with default setting

Dataset

Citation

Acknowledge

Owner

functorch is a prototype of JAX-like composable function transforms for PyTorch.

USAD - UnSupervised Anomaly Detection on multivariate time series

An example of Scatterbrain implementation (combining local attention and Performer)

Automatic Differentiation Multipole Moment Molecular Forcefield

A few stylization coreML models that I've trained with CreateML

This repository contains small projects related to Neural Networks and Deep Learning in general.

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Learning where to learn - Gradient sparsity in meta and continual learning

Repository for the AugmentedPCA Python package.

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This repository is the official implementation of Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models

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