Impact of Pretrained Networks For SnakeSpecies Classification

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.

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