A robust snake species classifier could aid in the treatment
of snake bites. In this report, the technique of transfer learning is revisited to understand the significance of the underlying pre-trained network
and the supervised datasets used for pre-training. In low data regime,
the methodology of transfer learning has been instrumental in building
reliable image classifiers. Comparisons are made between the pre-trained
networks trained on datasets of different sizes and classes. Performance
improves significantly when the pre-trained network is trained on a much
larger supervised dataset. Using country metadata improves the performance considerably. In SnakeCLEF2020 challenge, an F1-score of 0.625
was achieved.