Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs


  • Lanz Anthonee A. Lagman Data Science Program, College of Science, University of the Philippines Diliman
  • Prospero C. Naval, Jr. Department of Computer Science, University of the Phillippines Diliman
  • Reinabelle C. Reyes National Institute of Physics, University of the Philippines Diliman


Image data regarding galactic morphology is expected to increase both in quantity and quality for the next foreseeable years; thus it is important to explore which deep learning architectures adapted for image classification tasks are cost-effective. Residual and Inception networks are ideal for exploring classification convolutional neural networks (CNNs) due to their computational efficiency, achieved through techniques such as residual connections and parallelized inception modules, enabling deeper networks without excessively increasing computational complexity. In this work, we analyze the performance of ResNet101 and InceptionV4 on a spatially-augmented Galaxy10 DECals dataset. Retaining the ten-class classification of galaxies, we modify the image count of each class. We find that ResNet101 and InceptionV4 models achieved accuracies of ~90%, comparable with reported performance in the literature. In terms of performance metrics, ResNet101 is superior to InceptionV4. Our results indicate that either of these CNN architectures could serve as a robust foundation for specialized pipelines for classification of galaxy images from upcoming surveys.



Article ID



Gravitational Physics and Astrophysics



How to Cite

LAA Lagman, PC Naval, and RC Reyes, Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs, Proceedings of the Samahang Pisika ng Pilipinas 42, SPP-2024-2E-05 (2024). URL: