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As a vital step in modern deep learning pipelines, data augmentation can significantly increase the internal variation of a dataset. With many different combination of tasks and applications (e.g. instance segmentation, contrastive learning), the data augmentation strategies variate from one task to another. This session presents Kornia augmentation with aim at providing a simple and integral augmentation pipeline for different tasks and applications.