During the last decade, the scientific research community has made astonishing steps in the development and improvement of algorithms that exploit huge amount of data with the aim of making machines perform tasks such as classification, object detection and semantic segmentation. Usually, the strategy used to train this models is the supervised-learning technique that requires labeled datasets. In fact, the progresses made in the Machine Learning and Deep Learning field where enabled by a key factor: the presence of famous benchmark datasets already labeled. Those famous dataset usually contains a large amout of data. The issue is that those dataset does not allow to create models useful for real application.