  The continuum emission from radio galaxies can be generally classified into
different morphological classes such as FRI, FRII, Bent, or Compact. In this
paper, we explore the task of radio galaxy classification based on morphology
using deep learning methods with a focus on using a small scale dataset ($\sim
2000$ samples). We apply few-shot learning techniques based on Twin Networks
and transfer learning techniques using a pre-trained DenseNet model with
advanced techniques like cyclical learning rate and discriminative learning to
train the model rapidly. We achieve a classification accuracy of over 92\%
using our best performing model with the biggest source of confusion being
between Bent and FRII type galaxies. Our results show that focusing on a small
but curated dataset along with the use of best practices to train the neural
network can lead to good results. Automated classification techniques will be
crucial for upcoming surveys with next generation radio telescopes which are
expected to detect hundreds of thousands of new radio galaxies in the near
future.
