PyTorch Image Quality (PIQ)

Active

Collection of measures and metrics for automatic image quality assessment in various image-to-image tasks such as denoising, super-resolution, image generation etc. This easy to use yet flexible and extensive library is developed with focus on reliability and reproducibility of results. Use your favorite measures as losses for training neural networks with ready-to-use PyTorch modules.

Check out our GitHub repository for more information.

Getting started

PyTorch Image Quality helps you to concentrate on your experiments without the boilerplate code. The library contains a set of measures and metrics that is constantly getting extended. For measures/metrics that can be used as loss functions, corresponding PyTorch modules are implemented.

Installation

$ pip install piq
$ conda install piq -c photosynthesis-team -c conda-forge -c pytorch

Roadmap

See the open issues for a list of proposed features and known issues.

Contributing

We appreciate all contributions. If you plan to:

  • contribute back bug-fixes, please do so without any further discussion
  • close one of open issues, please do so if no one has been assigned to it
  • contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us

Please see the contribution guide for more information.

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