A Study on Small-Scale Snake Image Classification Based on Improved SimCLR - Lingyan Li; Ruiqing Kang; Wenjie Huang; Wenhui Feng (Applied Sciences)
The exotic pet trade is a major driver of alien species invasions. Improper introductions or a lack of management can result in severe ecological consequences. Therefore, accurate identification of exotic pets is essential for the prevention and early warning of species invasions. This paper proposes a novel recognition method for fine-grained images of small-scale exotic pet snakes in complex backgrounds based on an improved SimCLR framework. A hierarchical window attention mechanism is introduced into the encoder network to enhance feature extraction. In the loss function, a supervised contrastive mechanism is introduced to exclude false negative samples using label information, which helps reduce representation noise and enhance training stability. The training strategy incorporates random erasing and random grayscale data augmentation techniques to improve performance further. The projection head is constructed using a two-layer multilayer perceptron (MLP), and the cosine annealing schedule combined with the AdamW optimizer is adopted for learning rate adjustment. Experimental results on a self-constructed dataset demonstrate that the proposed model achieves a recognition accuracy of 97.5%, outperforming existing baseline models. This study fills a gap in exotic pet snake classification and provides a practical tool for species invasion prevention and early detection.