Metadata-Version: 2.2
Name: SynthVal
Version: 0.2.0a0
Summary: Package designed to validate the quality of synthetically generated data, with a focus on medical images like chest x-rays and mammographies, by providing tools for feature extraction and similarity metric calculations to compare original and synthetic datasets.
Home-page: https://github.com/AIMet-Lab/SynthVal
Author: Dario Guidotti, Laura Pandolfo, Luca Pulina
Author-email: dguidotti@uniss.it, lpandolfo@uniss.it, lpulina@uniss.it
License: GNU General Public License with Commons Clause License Condition v1.0
Classifier: Programming Language :: Python :: 3.10
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
License-File: LICENSE
Requires-Dist: pillow
Requires-Dist: pydicom
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: timm
Requires-Dist: torch
Requires-Dist: transformers
Requires-Dist: scipy
Requires-Dist: dcor
Requires-Dist: pynever
Requires-Dist: scikit-learn
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SynthVal
========

**SynthVal** is a Python package developed to validate and verify the quality of synthetically generated data by comparing it to original data. The project focuses primarily on medical images, such as chest x-rays and mammographies, offering tools to compute similarity measures between original and synthetic datasets.

Purpose
-------

With the growing use of synthetic data in fields like healthcare and AI, it is essential to have reliable methods to evaluate how closely synthetic data resembles real data. SynthVal addresses this need by providing a straightforward framework for comparing original and synthetic data, enabling users to assess the quality and fidelity of synthetic datasets.

Key Features
------------

SynthVal is built around two main modules:

1. **Feature Extraction**: The ``features_extraction.py`` module extracts vectors of features from images, capturing their essential characteristics to serve as the basis for similarity comparison.
   
2. **Similarity Metrics**: The ``metrics.py`` module provides the capabilities to calculates several metrics to determine the similarity between original and synthetic datasets.

Links
-------------

- `SynthVal Documentation <https://aimet-lab.github.io/SynthVal/index.html>`_
- `SynthVal Repository <https://github.com/AIMet-Lab/SynthVal?tab=readme-ov-file>`_
- `SynthVal PyPI Project <https://pypi.org/project/SynthVal/>`_
