# ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
## 4 Experimental Results and Analysis
### Analysis of Results
This is shown in Table 3.  
From the analysis in Table 3, it can be obtained that the precision, recall and specificity values of the model for classifying birds are 88.2%, 87.9% and 99.9%, respectively, and the above values indicate that the percentage of samples correctly classified among all samples predicted by the model as this target is 88.2%; the number of samples successfully recalled and correctly classified from all samples is 87.9% of the total number of samples in this category; and the number of samples classified by the model into other categories for samples not in this category is 99.9% of the rest of the total number of samples, except for samples in this category. The indicators of the evaluation parameters for the remaining two datasets were the same as the above analysis. Therefore, in general, after analyzing the evaluation parameters of the model, the robustness and reliability of the model for fine-grained classification are relatively high and meet most of the fine-grained classification requirements. To present the optimization result more intuitively, in Figure 16, we show the visualized attention result predicted by the model. Note: The red area in the image is the attention focus area when the network model predicts the image type, and the darker the color means the more attention is gathered. According to Figure 16, in the process of prediction, the network utilizes the GradCAM algorithm to call the output of the last convolutional layer of the last block of the network for visualization. It can be seen from the comparison results that for fine-grained classification, the network attention no longer focuses on the overall features of the target. Instead, it captures different characteristics of subclasses through training. As shown in the car pictures (third column), upon optimization of the three attention mechanisms, the network can focus on the features that are helpful for classification and unique to the subclass, such as the front of the car, and successfully filter the features that are not conducive to fine-grained classification and are common between subclasses, such as the wheels. Compared with the other two mechanisms, the BCBAM mechanism further pays attention to the different features of the front and body of the car and focuses on more comprehensive fine-grained features. Therefore, it is more helpful for subclass classification. The same is also reflected in the other two datasets. In order to verify the superiority and effectiveness of the proposed attention framework, the ablation experiment is designed as follows. The proposed attention frame is different from the traditional attention frame in the following three aspects: the main network used to train the attention parameters, the presence or absence of ECA mechanism, and the nonlinear activation of ReLU. The above differences are set, and ablation conditions are conducted on the CUB200-2011 dataset to study the influence on the performance \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Model Evaluation Parameters/\%**} \\ \cline{2-4}  & **Precision** & **Recall** & **Specificity** \\ \hline CUB200-2011 & 88.2 & 87.9 & 99.9 \\ FGVC-Aircraft & 92.5 & 92.1 & 99.9 \\ Stanford Cars & 93.5 & 93.2 & 99.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters evaluated by the model for different datasets (mean values). of the frame so as to better understand the behavior of the frame. Table 4 below is the experimental results. The experimental results show that the classification accuracy of the attention framework composed of removing some components is different on CUB200-2011. The comparison shows that the nonlinear activation of the output feature map through the ReLU activation function seriously affects the network's fine-grained classification, and the highest accuracy rate of classification is only 84.9%.