Introduction to IQA-PyTorch
Overview
IQA-PyTorch is a sophisticated toolbox designed for Image Quality Assessment (IQA), developed entirely using pure Python and PyTorch. It supports both full reference (FR) and no reference (NR) metrics, providing implementations faster than traditional MATLAB scripts, especially when using GPU acceleration. This toolbox aims to aid researchers and developers in analyzing the quality of images efficiently. For anyone interested in broad IQA methodologies and datasets, the Awesome-Image-Quality-Assessment offers further exploration.
Key Features
- Comprehensive Metrics Support: IQA-PyTorch includes reimplementation of numerous mainstream IQA metrics, covering both FR and NR categories.
- GPU Acceleration: To save time and improve performance, it integrates GPU support making it significantly faster than many traditional approaches.
- Ease of Use: The toolbox aims to be user-friendly, providing both command line and advanced code-based interfaces for different usage scenarios.
Updates
The IQA-PyTorch toolbox is continuously updated. Notable recent updates include the introduction of new metrics like msswd for perceptual color difference and efficiency benchmarks where most metrics validate their execution under one second using NVIDIA V100 GPU. New functionalities, such as qalign_4bit and qalign_8bit, have also been integrated to reduce memory consumption while maintaining performance.
Installation
Getting started with IQA-PyTorch requires a straightforward installation process using Python's pip. You can install it from PyPI or directly from the GitHub repository. For more demanding functionalities, you may need to install specific versions by cloning the repository and setting it up manually.
Usage
Basic Usage
The toolbox can be utilized directly through command-line commands. You can list available metrics, test with default settings, and designate devices (GPU or CPU) for computation.
Advanced Usage
Within your Python scripts, IQA-PyTorch offers customizable metric testing. Users can create specific metric functions, conduct image quality assessments using tensors or image files as inputs, and even employ these metrics as loss functions in machine learning models if the gradient propagation is enabled.
Customization
For bespoke needs, users can modify settings and weights in IQA-PyTorch to retrain or fine-tune models, offering extensive flexibility.
Benchmark and Dataset Resources
IQA-PyTorch is stacked with performance benchmarks and a vast repository of benchmark datasets, available via Huggingface's IQA-Toolbox-Datasets. These benchmarks cover a variety of categories, including Face IQA and Aesthetic IQA, among others. Efficient testing and performance tracking of models are made straightforward, allowing for comprehensive evaluations using provided scripts.
Contribution and Support
The project encourages contributions from the community. It is licensed under NTU S-Lab License and Creative Commons, ensuring open-source collaboration while maintaining recognition of original creators. If users find the code useful, they're invited to cite the work in their research.
Contact Information
Questions and further inquiries can be directed to [email protected], welcoming feedback and discussions for continuous development and support.
With its comprehensive feature set and user-friendly interface, IQA-PyTorch stands as a pivotal tool for advancing image quality assessment tasks efficiently and effectively.