# ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
## 4 Experimental Results and Analysis
### Analysis of Results
The attention framework without ReLU activation has a significant effect on the overall improvement of the network, no matter what mechanism is used to optimize the network; the accuracy rate has been improved. In the framework structure that does not use ECA and only uses the BCBAM mechanism, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **Dataset** & \begin{tabular}{c} **ReLU Activate** \\ **Function** \\ \end{tabular} & \begin{tabular}{c} **ECA Mechanisms** \\ \end{tabular} & \
\begin{tabular}{c} **BCBAM** \\ **Mechanisms** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy Rate/\%** \\ \end{tabular} \\ \hline \multirow{4}{*}{ConvNeXt-Tiny} & & & & & 85.1 \\  & & & & 85.8 \\  & & & & 86.0 \\ \cline{1-1}  & CUB200-2011 & \(\surd\) & \(\surd\) & & 84.9 \\ \cline{1-1}  & & \(\surd\) & & 79.3 \\ \cline{1-1}  & & & \(\surd\) & \(\surd\) & **87.8** \\ \cline{1-1}  & & \(\surd\) & \(\surd\) & 84.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments on CUB200-2011 based on our attention framework. Figure 16: Thermodynamic diagrams of the three attention mechanisms. the accuracy rate reaches 86.0%; the attention framework combining ECA and BCBAM (proposed in this article) can fully demonstrate the advantages of each attention. The classification accuracy rate on the CUB200-2011 data and above is as high as 87.8%, which exceeds the original unoptimized ConvNeXt-Tiny by 2.7%. The ablation experiment proves that the attention framework proposed in this paper not only improves the overall network's ability to control and capture fine-grained feature details, but also can combine multi-attention to achieve all-round and multi-perspective attention parameter training. Experiment 3 is designed to verify the superiority and applicability of ConvNeXt with BCBAM embedded in implementing fine-grained classification tasks. Using the improved network model BCBAM ConvNext (SBA method) with the highest classification accuracy in experiment 1, the current commonly used fine-grained classification algorithm is introduced to form a comparison experiment with the network classification results proposed in this paper. The Table 5 lists the experimental comparison results. It is worth noting that choosing different and optimal training strategies for different models in the training process is the key to obtain the best accuracy, the fine-grained classification experiments on ConvNeXt all use the training strategy introduced in Section 4.2 above, while for other typical fine-grained classification networks, in order to obtain their own best accuracy for fine-grained classification, a training strategy optimal for itself and different from introduced in Section 4.2 is used to train the model. The experimental results mentioned above show that in the feature extraction model based on ConvNeXt, embedding the attention framework proposed in this paper into the attention mechanism through SBA can allow the final accuracy rate to exceed that of the traditional fine-grained classification network model in implementing fine-grained classification tasks. Additionally, good classification results are indicated in the three types of fine-grained classification tasks. The classification accuracies on the three datasets were improved by 2.7%, 0.3%, and 0.4%, respectively, compared to the original network without the addition of optimization. The main reasons for the significant improvement of the optimized model on the bird dataset are the following two: (1) birds generally have lower classification results due to their complex background environment and smaller target size, and because of this, the bird dataset provides more samples of previous classification errors to the improved model than aircraft and automobiles, and therefore, the most significant improvement in network accuracy.