Galaxy Morphology Classification with DenseNet

Galaxy classification is crucial in astronomy, as galaxy types reveal information on how the galaxy was formed and evolved. While manually conducting the classification task requires extensive background knowledge and is time-consuming, deep learning algorithms provide a time-efficient and expedient way of accomplishing this task. Hence, this paper utilizes transfer learning from pre-trained CNN models and compares their performances on the Galaxy10 DECals Dataset. This paper applies opening operation, data augmentation, class weights, and learning rate decay to further improve the models' performance. In our experiments, DenseNet121 outperforms the other models and achieved approximately 89% test-set accuracy within 30 minutes. The second best-performing model, EfficientNetV2S, takes double the time achieving 2.43% lower test set accuracy.