Within the domain of machine learning, “garbage in, garbage out” is affirmed even stronger. Whatever the model had received during its training, in terms of quality and quantity, influences the performance and accuracy of its models. This is particularly important in computer vision, where the models unlearn to perceive and interpret the visual world. Thus, in this blog entry, we will concisely present some of the main aspects to consider when creating useful image datasets for machine learning.