# ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
## 4 Experimental Results and Analysis
### Preconditions of Experiment and Environment Description  
To verify the feasibility and reliability of the proposed method, the following experiments were conducted:  
1. Experiment 1: For ConvNext-Tiny network, EBA and SBA were used to embed the traditional attention mechanism (SE, CBAM) and the proposed BCBAM mechanism, respectively. The applicability and robustness of the proposed method were verified on different fine-grained datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of Categories** & **Training Set/Picture** & **Test Set/Picture** \\ \hline CUB200-2011 & 200 & 5994 & 5794 \\ FGVC-Aircraft & 100 & 6667 & 3333 \\ Stanford Cars & 196 & 8144 & 8041 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset information. Figure 14: Some pictures from the three types of datasets. 2. Experiment 2: On the basis of Experiment 1, the CUB200-2011 dataset was used to conduct an ablation experiment on the optimal optimization method to remove the influence of some components in the attention mechanism on the performance of the mechanism so as to better understand the behavior of the frame. 3. Experiment 3: The traditional fine-grained classification network is compared, and the superiority and practicality of the ConvNeXt-Tiny network embedded into BCBAM in implementing fine-grained classification tasks are verified. To ensure the better robustness of the network obtained in the experiment, the dataset is further processed. The Cutmix data enhancement algorithm is used to enhance the data of the training set to improve the model's ability to extract fine-grained features of the target during the process of training. Then, during the process of network training optimization, the exponential moving average (EMA) is used. This enables the network to maintain shadow weights when the backpropagation gradient is decreasing, preventing the jitter of the model parameters at the global optimum and making the network more robust during training. The above training strategy can improve the model accuracy slightly with a stable training process. The learning rate is decayed by a cosine function according to the training period. The decay period is the epoch of the entire training, and the initial learning rate is set to 0.00008. The network is trained according to the cross-entropy loss function. The performance comparisons between individual models in the experiments were built on three datasets, CUB201-2011, FGVC-Aircraft, and Stanford Cars, respectively, each with a large number of categories, as shown in Table 1. Therefore, due to the limitation of space, it is not possible to show the confusion matrix or the data comparison of many individual category model evaluation parameters (e.g., precision and recall), and it is not convincing to evaluate the model by averaging the precision and recall of many categories with such a large number of categories in the fine-grained dataset. Therefore, this paper uses the accuracy rate metric to compare and validate the performance of each model for fine-grained classification. The classification accuracy rate is obtained by averaging the accuracy rates of each batch size data to obtain the classification accuracy rate of this round of testing (the number of test rounds coincides with the epoch of network training). The highest accuracy is recorded as the final classification accuracy of the model. In summary, the accuracy rate is the most convincing metric to validate the model performance for this study. The model accuracy is calculated as shown in Equation (6). \[\text{Accuracy Rate}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\times 100\% \tag{6}\]  
where TP means true positive; FP means false positive; TN means true negative; and FN means false negative.