AstroYOLO: Learning Astronomy Multi-Tasks in a Single Unified Real-Time Framework

In this paper, we proposed a single unified real-time pipeline that jointly performs two tasks: star vs. galaxy detection and smooth type vs. disk type galaxy classification. To achieve the goal, we introduced a model which have two different classification heads sharing the same backbone: The first classification head is used to detect useful objects from the universe images and classify them into star vs. galaxy; while the second classification head is used to further classify whether the galaxy is smooth or disk type. As the backbone, we used YOLOX architecture, add two classification heads upon it and train them using two heterogeneous datasets: the star vs. galaxy detection dataset which have images including star and galaxy objects and corresponding bounding box and class labels and the smooth vs. disk type classification dataset having galaxy images and their corresponding labels. To prevent the catastrophic forgetting when learning two heads and a backbone, we performed the alternative training between two tasks and also applied data augmentation such as mosaic and mix-up methods. The final model achieved 73.4\% accuracy on the smooth vs. disk type classification task, and 65.6 mAP score on star vs. galaxy detection task.