# A Study on Small-Scale Snake Image Classification Based on Improved SimCLR
## 4 Conclusions  
This paper proposes an improved supervised contrastive learning model based on the SimCLR framework for fine-grained snake image classification under high-interference background conditions in a small-scale dataset. Our method introduces enhanced data augmentation strategies, including grayscale enhancement and random erasing, to simulate \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Different Methods** & **Accuracy** & **Training Loss** & **Data Augmentation** \\ \hline ResNet34 & 0.8277 & 0.0012 & 10\(\times\) \\ MobileNetV3-L & 0.8570 & 0.0059 & 10\(\times\) \\ Efficientnetb04 & 0.9228 & 0.0043 & 10\(\times\) \\ SimCLR & 0.7662 & 4.3517 & 1\(\times\) \\ Methodology of this paper (single split) & 0.9748 & 2.1211 & 1\(\times\) \\ Methodology of this paper (5-fold CV) & 0.9811 \(\pm\) 0.0015 & – & 1\(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with classical models. Figure 8: Contrastive loss. complex background interference and improve robustness. In the feature encoding module, we replace the traditional ResNet50 backbone with a Swin Transformer that leverages global self-attention for better spatial context modelling. Furthermore, a two-layer MLP projection head is designed to strengthen representation learning. The loss function adopts a supervised contrastive mechanism, and training uses cosine annealing scheduling with the AdamW optimizer. Experimental results demonstrate that the proposed method achieved an accuracy of 97.48%, representing a significant improvement of 6.63% over traditional CNN models like EfficientNet-b4 while requiring fewer computational resources and no reliance on multimodal data or aggressive data augmentation. Additionally, we constructed a novel exotic pet snake image dataset of 17 species, providing a valuable benchmark for future research on small-sample, high-interference snake image classification. Despite achieving good experimental results, we recognize that there are still directions for optimization in the current research. Firstly, due to the imbalance in the number of classes in the dataset, some classes contain as few as 30-40 images, which may limit the generalizability and robustness of the research results. Although data augmentation has somewhat alleviated this issue, more systematic class imbalance handling strategies can be explored in future work. Secondly, potential label errors in the dataset may affect the performance of supervised contrastive learning, and future research should consider incorporating anti-noise contrastive targets. Thirdly, although we assume this method may apply to other reptiles such as lizards and turtles, considering their similar data scarcity and complex image backgrounds, this hypothesis is still speculative and requires more experiments to verify. In summary, our method offers an effective and efficient technical solution for fine-grained snake recognition in constrained and noisy environments, with potential applications in customs inspection, biosecurity, and exotic pet regulation. Future work will focus on expanding the dataset, improving robustness, and validating the method's generalization to other species and domains. L.L. : Formal analysis, investigation, methodology, writing--original draft and editing. R.K.: Supervision, review, and editing. W.H. and W.F. : Formal analysis and review. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available in this article and from the corresponding author upon reasonable request. We would like to thank all the people who have helped make this study possible, especially [PERSON] and the members of the subject team, who were an important part of this study. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: