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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 data set (∼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 per cent 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 data set 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 that are expected to detect hundreds of thousands of new radio galaxies in the near future. [ABSTRACT FROM AUTHOR]