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@denproc denproc released this 06 Jun 23:27
· 43 commits to master since this release
v0.5.5
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PyTorch Image Quality (PIQ) v0.5.5 Release Notes

New Features

Precision-Recall (#247)

In this release, we added new Precision-Recall metric. The metric was introduced for assessing generative models, i.e. estimate the quality and coverage of the generated samples. The metric can separately and reliably measure both of these aspects in image generation tasks by forming explicit, non-parametric representations of the manifolds of real and generated data.

Documentation (#217)

With this release we introduce piq.readthedocs.io documentation to provide assistance using our library. Installation and usage guides help to start using PIQ framework in your projects. The documentation includes relevant information about metric interfaces and metric specific differences. In order to keep the documentation up-to-date, we use automatic pipeline for generation and deployment to piq.readthedocs.io. In addition, we updated in #233 pipeline allows to update README.rst and documentation simultaneously.

Enhancements

  • Updated README with benchmarking results and references (#211);
  • Updated PR template with check list for new metrics (#219);
  • Added BibTex Citation (#220);
  • Added guide for documentation upgrades (#229);
  • Unified tensor names for all metrics, measures and losses (#230);
  • Upgraded documentation pipeline to keep both documentation and README.rst up-to-date (#233);
  • Upgraded input validation to simplify the interface and allow advanced users to turn off validation (#234);

Bug Fix

  • Fixed missing average pooling in SSIM, implementation enhancements (#213);
  • Removed rarely used library dependencies from requirements.txt (#237);
  • Added exceptional import to MSID due to updated requirements.txt (#244);
  • Added downsampling to DISTS boosting the performance (#238);
  • Fixed use of FFT due to interface changes introduced in torch==1.8.1 (#246);
  • Fixed the condition for FFT due to interface changes introduced in torch==1.8.1(#250);
  • Unified documentation style across all docstrings (#243);

Contributors: @zakajd, @snk4tr, @denproc, @hecoding.

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