Similarity Learning lacks a framework. So we built one. Many general-purpose frameworks allow you to train Computer Vision or NLP tasks quickly. However, Similarity Learning has peculiarities, which usually require an additional layer of complexity on top of the usual pipelines. So, for example, the batch size in the training of similarity models has a much greater role than in other models. Labels either do not exist or are handled in a completely different way. In many cases, the model is already pre-trained, which also adjusts the process. Developing Similarity Learning models one after another, we began to notice patterns that helped us generalize and bring all our experience with training and fine-tuning such models into one package. Yesterday we published Quaterion — an open-source, blazing-fast, customizable, scalable framework for training and fine-tuning similarity learning models.