NA DAtabase: generator of probabilistic synthetic geometrical shape dataset

Volpini F., Caudai C., Del Corso G., Colantonio S.

Synthetic database, Shapes, Uncertainty quantification 

In this technical report, we detail NA DA (Not-A-DAtabase), an open-source software writ- ten in Python that generates datasets of regular two-dimensional geometric shapes based on probabilistic distributions (https://github.com/GDelCorso/NA DAtabase.git). NA DA comes with an intuitive GUI (Graphical User Interface) that allows users to define shapes, colors, and distributions of features of datasets consisting of image sets and CSV files containing metadata for each element. These databases can be saved to provide a unique identifier of the dataset, allowing perfect reproducibility or easy modification of the dataset using the GUI or directly by calling the generator class. Therefore, NA DA is a tool to help and support the investigation of trustworthiness, overconfidence, uncertainty, and computation time of machine learning and deep learning models.

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