Deep Learning (DL) has made remarkable progress in tasks such as face and lip recognition or cancer detection in medical images, achieving super-human performance. However, when it comes to classifying a large number of classes, such as in fine-grained recognition, there is still much room for improvement, especially for groups of classes that are easily confused. Additionally, DL relies on greedy methods that require thousands of annotated images, which can be a time-consuming and tedious process. To address these issues, self-supervised learning offers an efficient way to leverage a large amount of non-annotated images and make DL models more robust and accurate. In this talk, we will present our work on self-supervised learning and fine-grained recognition, highlighting how this approach can help solve complex computer vision problems like food image recognition. Food classes have high variability, significant similarity between classes, and a vast number of unannotated images. By using self-supervised learning and fine-grained recognition, we demonstrate how these challenges can be overcome.