## 4 Experiments
### Experimental Results  
The ResNet50+ArcFace and ResNet50 models were trained on three datasets (Data1, Data2, and Data3), with sampling of training loss and accuracy during the process. Validation was performed every 100 iterations. As shown in Figure 5, the ResNet50+ArcFace model exhibits a slower rate of loss reduction with increasing training epochs compared to ResNet50 across the Data1, Data2, and Data3 datasets. However, it achieves higher training accuracy and consistently outperforms ResNet50 in validation accuracy. (training-validation:test), as shown in Table 3. For the ResNet50 model, Top1 accuracy refers to the proportion of samples where the class with the highest predicted probability matches the ground truth label. For the ResNet50+ArcFace model, Top1 accuracy represents the proportion of samples where the class with the highest predicted similarity score matches the ground truth label. Regarding Top5 accuracy, in the ResNet50 model, it indicates the proportion of samples where the ground truth label is contained within the Top5 classes with the highest predicted probabilities. Similarly, in the ResNet50+ArcFace model, Top5 accuracy denotes the proportion of samples where the ground truth label appears among the Top5 classes with the highest predicted similarity scores. The Precision, Recall, and F1-Score are defined by Equations (3), (4), and (5), respectively. The Top1-\(t\) metric is specific to the ResNet50+ArcFace model. The calculation formula is shown in 6 of the equation, representing the proportion of samples where the molecular value corresponds to samples whose highest predicted similarity score class matches the ground truth label, and the denominator comprises cases where this similarity score is no less than threshold \(t\). The threshold \(t\) can assume any value within the range \([0,1]\). The threshold \(t\) can be any value between \([0,1]\). In our study, we selected four threshold values: 0.7, 0.8, 0.90, and 0.95, corresponding to Top1-0.7, Top1-0.8, Top1-0.9, and Top1-0.95, respectively. \[\text{Precision} =\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\] \[\text{Recall} =\frac{\text{TP}}{\text{TP}+\text{FN}}\] (4) \[F1\text{-Score} =2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{5}\]  
where TP denotes true positives, FP false positives, and FN false negatives. The Top1-\(t\) Accuracy is defined as  
\[\text{Top1-}t\text{ Accuracy}=\frac{|\{\text{correct top-1 predictions}\geq t\}|}{|\{\text{highest similarity}\geq t\}|} \tag{6}\] \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 & 0.937 & 0.986 & 0.934 & 0.929 & 0.931 & - & - & - \\  & ResNet50+ArcFace & 0.943 & 0.978 & 0.94 & 0.936 & 0.938 & 0.967 & 0.976 & 0.986 & 0.988 \\ \hline \multirow{2}{*}{Data2} & ResNet50 & 0.842 & 0.958 & 0.848 & 0.842 & 0.843 & - & - & - \\  & ResNet50+ArcFace & 0.857 & 0.936 & 0.864 & 0.857 & 0.857 & 0.943 & 0.966 & 0.985 & 0.993 \\ \hline \multirow{2}{*}{Data3} & ResNet50 & 0.886 & 0.969 & 0.636 & 0.574 & 0.587 & - & - & - \\  & ResNet50+ArcFace & 0.902 & 0.96 & 0.66 & 0.6 & 0.615 & 0.969 & 0.981 & 0.991 & 0.996 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compare with two methods on different test data (training:validation:test = 6:2:2).