Deep Learning for Snake Species Identification to Support Snakebite Treatment: A Comparative Study of Efficient CNN and Vision Transformer Architectures

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Snakebite envenoming is a neglected tropical disease that affects millions of people each year, and species identification is a critical component in choosing the correct antivenom. In this study, six convolutional neural network and vision transformer architectures are compared for the image-based snake species classification task on the SnakeCLEF 2024 dataset, which contains 1599 snake species observed in 209 countries. We use knowledge distillation to boost accuracy without increasing inference cost, and incorporate country-level geographic location through a closed-form Bayesian fusion of the classifier’s output with location-conditioned species-occurrence priors. Location fusion reduced the classification error rate by over 40% across all architectures, and knowledge distillation provided a further improvement. The best model, CAFormer-S18, obtained an accuracy of 81.71% using both image and country-level location. We also compare the single-image inference latency on a smartphone and a Raspberry Pi 5, revealing that the latency of real-world, on-device deployments is significantly influenced by the interplay of model architecture and inference toolchain. Our top-performing model can infer in about 1 s on both devices, demonstrating that fine-grained species identification of snakes is feasible for real-time use in snakebite treatment and, when combined with an additional object detection stage, in wildlife monitoring.