Transferability exploration of weight based on ImageNet for galaxy classification

Abstract

The accurate classification of galaxies is essential for understanding the structure of the cosmos and establishing connections with host haloes. Manual classification methods are time-consuming, subjective, and prone to errors. This study investigates the efficiency of automated galaxy categorization utilising transfer learning with pre-trained models from ImageNet by utilising deep learning techniques. The Galaxy10 DECals Dataset is used in the study, and the DenseNet-121 network architecture is used for transfer learning. Experiments are used to assess the effects of various ImageNet weight configurations on the performance of the model. The findings reveal that the model trained with 20% ImageNet data achieves the highest classification accuracy of 79% for galaxies. Grad-CAM visualisation further highlights the impact of weight initialization on categorisation by displaying the different focal points of models trained with various ImageNet weights. The results indicate that using higher weights from ImageNet transfers advanced features, whereas using fewer weights transfers basic features, potentially producing inaccurate results. Prior to transfer learning, the study emphasises the significance of choosing the best weights from ImageNet. The knowledge acquired aids in the classification of galaxies and offers direction for further study.