From TrashCan to UNO: Deriving an Underwater Image Dataset to Get a More Consistent and Balanced Version

Cyril Barrelet, Marc Chaumont, Gérard Subsol, Vincent Creuze, Marc Gouttefarde

Abstract: The multiplication of publicly available datasets makes it possible to develop Deep Learning models for many real-world applications. However, some domains are still poorly explored, and their related datasets are often small or inconsistent. In addition, some biases linked to the dataset construction or labeling may give the impression that a model is particularly efficient. Therefore, evaluating a model requires a clear understanding of the database. Moreover, a model often reflects a given dataset’s performance and may deteriorate if a shift exists between the training dataset and real-world data.