# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 4 Discussion  
Following our experimentation, the incorporation of the three core modules--multi-scale feature fusion, feature enhancement, and Siamese network training--significantly improves the model's performance. Evaluation results unequivocally demonstrate that our model outperforms the compared methods in both accuracy and F1 score, underscoring the effectiveness of our approach in fine-grained bird classification. Notably, the multi-scale feature fusion module enables the model to capture comprehensive information from bird images across various scales and perspectives, thereby enhancing classification accuracy by providing a more nuanced feature representation. The feature enhancement module plays a crucial role in mitigating the impact of noise and occlusion within images, thereby strengthening the model's overall robustness. Additionally, the Siamese network training module effectively learns feature interactions and optimizes the model during training, resulting in a better balance between precision and recall. In addition to superior classification accuracy and F1 score, the experimental results also highlight that our proposed model exhibits faster execution times compared to HSnet,  
Figure 4: Comparison of bird species recognition accuracy under number of train samples. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Model Size** & **Params** & **Running Time** \\ \hline HSnet [31] & 293.85 MB & 24.62 MB & 1.64 s \\ Mask-CNN [36] & 634.53 MB & 48.74 MB & 3.47 s \\ RA-CNN [40] & 3.06 GB & 265.94 M & 1.79 s \\ ADKD_T [41] & 1.32 GB & 6.95 M & 1.1 s \\ FS-NET(Proposed Model) & 193.58 MB & 5.17 M & 234 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of the model size and running time of the model. Mask-CNN, RA-CNN, and ADKD_T. This efficiency is primarily attributed to the utilization of the ShuffleNetV2 backbone, renowned for its computational efficiency. Moreover, our model achieves faster execution times compared to all benchmarks, rendering it suitable for real-time applications where speed is paramount. Furthermore, our proposed model achieves significantly better recognition accuracy than other methods, particularly when trained with limited data (as depicted in Figure 4). Even with only 5.0% of the training data, our method achieves a remarkable 10% higher recognition accuracy compared to the second-best performing model. This suggests that our approach is exceptionally efficient in learning effective feature representations even with a scarcity of training examples. Moreover, as the volume of training data increases, our model maintains its performance lead, indicating its superior utilization of available data and consistent performance across varying data volumes. This robustness underscores the scalability and effectiveness of our model in real-world applications. In summary, our proposed model featuring three modules has demonstrated superior performance in terms of accuracy, F1 score, and efficiency, underscoring its efficacy in fine-grained bird classification. The research outcomes presented in this study carry substantial implications and opportunities for advancement across various domains. Accurately classifying bird species holds promise for driving progress in wildlife conservation endeavors, biodiversity research, and ecosystem health assessments. Researchers, ornithologists, and environmentalists can leverage these findings for studying bird behavior, ecology, and evolution. Additionally, industries involved in construction and tourism stand to benefit from informed decision-making enabled by precise species identification. However, the current study highlights potential concerns, including data bias and class imbalance within the training dataset, which could affect performance, particularly for underrepresented species. Generalizing to unseen scenarios and optimizing default hyperparameter values are also noteworthy considerations.