Welcome to PIQ’s documentation!
Note
PyTorch Image Quality (PIQ) is not endorsed by Facebook, Inc.
PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.
PyTorch Image Quality (PIQ) is a collection of measures and metrics for image quality assessment. PIQ helps you to concentrate on your experiments without the boilerplate code. The library contains a set of measures and metrics that is continually getting extended. For measures/metrics that can be used as loss functions, corresponding PyTorch modules are implemented.
We provide:
Unified interface, which is easy to use and extend.
Written on pure PyTorch with bare minima of additional dependencies.
Extensive user input validation. Your code will not crash in the middle of the training.
Fast (GPU computations available) and reliable.
Most metrics can be backpropagated for model optimization.
Supports python 3.7-3.10.
PIQ was initially named PhotoSynthesis.Metrics.
Citation
If you use PIQ in your project, please, cite it as follows.
@misc{kastryulin2022piq,
title = {PyTorch Image Quality: Metrics for Image Quality Assessment},
url = {https://arxiv.org/abs/2208.14818},
author = {Kastryulin, Sergey and Zakirov, Jamil and Prokopenko, Denis and Dylov, Dmitry V.},
doi = {10.48550/ARXIV.2208.14818},
publisher = {arXiv},
year = {2022}
}
@misc{piq,
title={{PyTorch Image Quality}: Metrics and Measure for Image Quality Assessment},
url={https://github.com/photosynthesis-team/piq},
note={Open-source software available at https://github.com/photosynthesis-team/piq},
author={Sergey Kastryulin and Dzhamil Zakirov and Denis Prokopenko},
year={2019}
}