Galaxy Morphology Classification using EfficientNet Architectures
  We study the usage of EfficientNets and their applications to Galaxy
Morphology Classification. We explore the usage of EfficientNets into
predicting the vote fractions of the 79,975 testing images from the Galaxy Zoo
2 challenge on Kaggle. We evaluate this model using the standard competition
metric i.e. rmse score and rank among the top 3 on the public leaderboard with
a public score of 0.07765. We propose a fine-tuned architecture using
EfficientNetB5 to classify galaxies into seven classes - completely round
smooth, in-between smooth, cigarshaped smooth, lenticular, barred spiral,
unbarred spiral and irregular. The network along with other popular
convolutional networks are used to classify 29,941 galaxy images. Different
metrics such as accuracy, recall, precision, F1 score are used to evaluate the
performance of the model along with a comparative study of other state of the
art convolutional models to determine which one performs the best. We obtain an
accuracy of 93.7% on our classification model with an F1 score of 0.8857.
EfficientNets can be applied to large scale galaxy classification in future
optical space surveys which will provide a large amount of data such as the
Large Synoptic Space Telescope.
