# ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
## 4 Experimental Results and Analysis
### Analysis of Results  
Experiment 1 is designed to verify the generality and robustness of the improved network and BCBAM on different datasets. Experiment 1 uses the two integration methods (EBA and SBA) and the attention framework proposed in this paper to conduct classification experiments on three fine-grained datasets: CUB200-2011, FGVC-Aircraft and Stanford Cars. The details are recorded in Table 2.  
Table 2 shows how the process of embedding different attention mechanisms into ConvNeXt-Tiny using different methods affects the fine-grained classification accuracy. According to the analysis of the experimental results, the integration of the BCBAM with  
the attention framework through SBA can more effectively improve the accuracy of the model's fine-grained classification. For different datasets, the accuracy is still the highest, which shows that the BCBAM ConvNeXt proposed in this paper has great generalization ability and good robustness. According to the experimental results indicated in the table above, the BCBAM proposed in this paper significantly improves ConvNeXt's ability to extract fine-grained features. It can be seen from the comparison of the results of the two integration methods that the SBA method can better utilize the fine-grained feature extraction ability of ConvNeXt. In addition, it takes nearly half of the time used by EBA in training. The Figure 15 shows the accuracy and loss variation curves of the model during the testing process. The analysis of the curve changes shows that the model converges quickly, with CUB200-2011 requiring only 44 epochs, FGVC-Aircraft requiring only 80 epochs to reach convergence, and Standford Cars reaching convergence at 88 epochs. The same effect is reflected in the change of the loss curve, where the loss decreases faster, which leads the network to reach the stable stage faster and shortens the training time significantly. Finally, we conclude that the model in this paper converges quickly and has high classification accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Integration Method**} & \multirow{2}{*}{**Improvement Method**} & \multicolumn{4}{c}{**Accuracy Rate/\%**} \\ \cline{4-5}  & & & **CUB200-2011** & **FGVC-Aircraft** & **Stanford Cars** \\ \hline \multirow{5}{*}{**ConvNeXt-Tiny**} & \multirow{2}{*}{EBA} & SE & 74.5 & 89.5 & — \\  & & CBAM & 82.9 & 91.0 & — \\  & & BCBAM & 83.7 & 91.7 & — \\ \hline \multirow{5}{*}{**ConvNeXt-Tiny**} & \multirow{2}{*}{SBA} & SE & 82.7 & 91.7 & 92.9 \\  & & CBAM & 84.7 & 92.0 & 92.7 \\ \cline{1-1}  & & BCBAM & **87.8** & **92.1** & **93.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fine-grained classification with different attention mechanisms based on EBA and SBA. Figure 15: (**a**) CUB-200-2011 Test Accuracy Curve. (**b**) FGVC-Aircraft Test Accuracy Curve. (**c**) Stanford Cars Test Accuracy Curve. (**d**) CUB-200-2011 Test Loss Curve. (**e**) FGVC-Aircraft Test Loss Curve. (**f**) Stanford Cars Test Loss Curve. Next, the model was evaluated in two ways: (1) confusion matrix, and (2) model evaluation parameters such as precision, recall, and specificity. We evaluated the model using both of these approaches. However, since the datasets used in this paper are CUB200-2011, FGVC-Aircraft, and Stanford Cars, the smallest dataset has 100 classes, so the smallest confusion matrix size is 100 \(\times\) 100, and the confusion matrix cannot be displayed properly. So, the table below shows the results of model evaluation using precision, recall, and specificity parameters. Using the evaluation parameters also suffers from the problem that cannot be displayed properly because of many categories; therefore, the precision, recall, and specificity of the model for different categories of each dataset are averaged to reflect the overall classification effect of the model on the dataset.