Predicting galaxy morphology using attention-enhanced ResNets

The practice of categorizing the galaxies according to morphologies exists and offers crucial details on the creation and development of the universe. The conventional visual inspection techniques have been very subjective and time-consuming. However, it is now possible to classify galaxies with greater accuracy owing to advancements in deep learning techniques. Deep Learning has demonstrated considerable potential in the research of galaxy classification and offers fresh perspectives on the genesis and evolution of galaxies. The suggested methodology employs Residual Networks for variety in a transfer learning-based method. To improve the accuracy of ResNet, an attention mechanism has been included. In our investigation, we used two relatively shallow ResNet models, ResNet18 and ResNet50 by incorporating a soft attention mechanism into them. The presented approach is validated on the Galaxy Zoo dataset from Kaggle. The accuracy increases from 60.15% to 80.20% for ResNet18 and from 78.21% to 80.55% for ResNet50, thus, demonstrating that the proposed work is now on a level with the accuracy of the far more complex, ResNet152 model. We have found that the attention mechanism can successfully improve the accuracy of even shallow models, which has implications for future studies in image recognition tasks.