A Comparison of Deep Learning Architectures for Optical Galaxy
  Morphology Classification
  The classification of galaxy morphology plays a crucial role in understanding
galaxy formation and evolution. Traditionally, this process is done manually.
The emergence of deep learning techniques has given room for the automation of
this process. As such, this paper offers a comparison of deep learning
architectures to determine which is best suited for optical galaxy morphology
classification. Adapting the model training method proposed by Walmsley et al
in 2021, the Zoobot Python library is used to train models to predict Galaxy
Zoo DECaLS decision tree responses, made by volunteers, using EfficientNet B0,
DenseNet121 and ResNet50 as core model architectures. The predicted results are
then used to generate accuracy metrics per decision tree question to determine
architecture performance. DenseNet121 was found to produce the best results, in
terms of accuracy, with a reasonable training time. In future, further testing
with more deep learning architectures could prove beneficial.
