Release Notes: - Allows label smoothing classification - Modifies cleaning transform to enable cleaning mask as a channel in data - Modifies the experiment setting examples to allow domain adaptation. This includes the creation of a data module train and test regrouping the corresponding dataset parameters, a new target for the domain classifier, a hand-designed LR Scheduler matching the pytorch API. - Implements the DANN (Ganin et al., 2016) method for domain adaptation - Changes pytorch lightning to version 1.6 - Upgrades network definition by taking backbone out of GammaPhysNet - Add a merge option in the experiment setting file to merge dl2 files after training and testing - Use both obs_id and event_id instead of solely event_id to select unique events while loading data - Add a progress bar to file loading

GammaLearn is a collaborative project to apply deep learning to the analysis of low-level Imaging Atmospheric Cherenkov Telescopes such as CTA. It provides a framework to easily train and apply models from a configuration file. Learn more at https://purl.org/gammalearn