## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Data** & **Model** & **Top1** & **Top5** & **Precision** & **Recall** & **F1-Score** & **Top1-0.7** & **Top1-0.8** & **Top1-0.9** & **Top1-0.95** \\ \hline \multirow{2}{*}{Data1} & ResNet50 & 0.945 & 0.988 & 0.942 & 0.937 & 0.94 & - & - & - \\  & ResNet50+ArcFace & 0.951 & 0.983 & 0.949 & 0.946 & 0.947 & 0.967 & 0.974 & 0.983 & 0.984 \\ \hline \multirow{2}{*}{Data2} & ResNet50 & 0.866 & 0.965 & 0.873 & 0.866 & 0.867 & - & - & - & - \\  & ResNet50+ArcFace & 0.884 & 0.948 & 0.889 & 0.883 & 0.884 & 0.951 & 0.97 & 0.986 & 0.992 \\ \hline \multirow{2}{*}{Data3} & ResNet50 & 0.895 & 0.969 & 0.763 & 0.65 & 0.682 & - & - & - \\  & ResNet50+ArcFace & 0.911 & 0.961 & 0.751 & 0.7 & 0.709 & 0.975 & 0.986 & 0.994 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compare with two methods on different test data (training:validation:test = 8:1:1). where  
* \(t\) is the similarity threshold;
* \(|\{\cdot\}|\) denotes the size of a set. As shown in Table 2, the ResNet50+ArcFace model demonstrates consistent improvements over the baseline ResNet50 across all datasets. Specifically, it achieves accuracy gains of 0.6%, 1.5%, and 1.6% in Top1 accuracy on Data1, Data2, and Data3, respectively. Both models maintain high performance in Top-5 accuracy across all three datasets. The ResNet50+ArcFace model outperforms the baseline ResNet50 in Precision, Recall, and F1-score. The results demonstrate that ResNet50+ArcFace outperforms ResNet50 in reducing both false positives (FP) and false negatives (FN). Notably, ResNet50+ArcFace shows superior accuracy at various confidence thresholds (0.7, 0.8, 0.9, and 0.95) for Top1 classification on all datasets. These results are further supported by Table 4, which confirms that ResNet50+ArcFace consistently outperforms ResNet50 on Data1, Data2, and Data3. To validate the experimental outcomes under different data partitioning ratios, datasets Data1, Data2, and Data3 were each split into training, validation, and test sets at an 8:1:1 ratio per category. Each dataset comprised 272 dragonfly species. Subsequently, ResNet50+ArcFace and ResNet50 models were trained separately. Experimental results are presented in Table 3. Comparison with Table 2 reveals that the ResNet50+ArcFace model consistently outperformed the ResNet50 model under both partitioning strategies. The ResNet50+ArcFace model evaluates the Top1-\(t\) accuracy on the test data, where varying the threshold \(t\) yields different accuracy rates and simultaneously affects the proportion of samples satisfying the threshold condition. To investigate the relationship between these two metrics, we selected 101 discrete thresholds from the interval [0, 1] with a step size of 0.01. For each threshold \(t\), we recorded the corresponding Top1-\(t\) accuracy and the proportion of samples exceeding the threshold relative to the total sample size, as illustrated in Figure 6. Additionally, Table 4 presents the proportions of samples surpassing the thresholds of 0.7, 0.8, 0.85, 0.9, and 0.95. As shown in Figure 6, as the threshold \(t\) increases, the proportion of samples meeting or exceeding the threshold gradually decreases, with a more pronounced decline at higher \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data** & **Model** & **0.7** & **0.80** & **0.85** & **0.90** & **0.95** \\ \hline Data1 & ResNet50+ArcFace & 0.960 & 0.933 & 0.910 & 0.850 & 0.395 \\ Data2 & ResNet50+ArcFace & 0.842 & 0.759 & 0.695 & 0.572 & 0.176 \\ Data3 & ResNet50+ArcFace & 0.60 & 0.801 & 0.749 & 0.641 & 0.265 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The proportion of samples in which the model-predicted class with the highest similarity is not less than the threshold, across three test datasets. Figure 6: Accuracy and the proportion of samples satisfying the threshold condition \(t\) at different thresholds. thresholds.