## 4 Experiments
### Comparison of Different Experiments
#### 4.3.4 Comparison of State-of-the-Art Algorithms  
We intended to prove that our model is more versatile and advanced in different fine-grained datasets. Therefore, we compared our method in the different datasets, CUB-200-2001, Stanford Dogs, and FGVC-Aircraft, with current state-of-the-art methods. Considering the existing methods have significant differences in the performance on different datasets, we chose to use the classification results \begin{table}
\begin{tabular}{c c} \hline \hline
**Loss Functions** & **CUB200-2011 (Accuracy)** \\ \hline \(L_{P}\) & 81.12\% \\ \(L_{P}+aL_{P_{4}}\) & 82.43\% \\ \(L_{P}+aL_{P_{A}}+\beta L_{P_{B}}\) & 83.81\% \\ \(L_{P}+aL_{P_{A}}+\beta L_{P_{B}}+\text{CentralLoss}\) & **84.12\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of different loss functions. Figure 11: Accuracies of different networks on the CUB200-2011 dataset. on the corresponding datasets recorded in the relevant papers during our comparison. The comparison results are shown in Tables 2-4. Because the CUB200-2011 dataset provides essential points data, when we compared the performance on the dataset, we chose to compare our method with strongly supervised methods. Table 5 shows the classification labels of some of the images in the CUB 200-2011 dataset by advanced methods. The text in red indicates an incorrect classification result. As we can see from the typical test images, without the corresponding injection of manually supervised information, our classification remains accurate for images with small inter-class differences and large intra-class differences. Additionally, there are fewer instances of classification failures due to differences in the perspective and background. From the tables above, we can see that our method has a great performance on the Stanford Dogs and FGVC datasets; also, our method reaches an accuracy of 85.1% on the CUB-200-2001 dataset, which is better than some strongly supervised algorithms, indicating that weakly supervised methods can reduce the dependence on manual data labelling and improve the practicability of the algorithm while ensuring a certain accuracy. Our accuracy is higher than the OPAM proposed by [PERSON] et al. on the FGVC-Aircraft dataset [1]. On the CUB 200-2011 dataset, our accuracy is very similar to the OPAM method proposed by to [PERSON] et al. However, OMPA runs with roughly 35 M parameters, which is seven times the number we used, and only achieves a classification speed of 4 frames per second. We reduced the number of parameters while increasing the speed of detection and ensuring classification accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Label Box** & **Essential Points** & **Accuracy** \\ \hline Part-Based R-CNN [13] & \(\surd\) & \(\surd\) & 73.5\% \\ Pose Normalized CNN [15] & \(\surd\) & \(\surd\) & 75.7\% \\ Deep LAC [14] & \(\surd\) & \(\surd\) & 84.1\% \\ PG Alignment [39] & \(\surd\) & & 82.8\% \\ VGG-BGLm [40] & \(\surd\) & & 80.4\% \\ PIR [38] & & & 79.3\% \\ SCDA [37] & & & 80.5\% \\ B-CNN [25] & & & 84.1\% \\ OPAM [1] & & & 85.83\% \\ Multi-scale Granularity [17] & & & 82.5\% \\ PD [36] & & & 84.6\% \\ Ours & & & **85.1\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy on the CUB 200-2011 dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline B-CNN [25] & 83.76\% \\ OPAM [1] & 84.01\% \\ Multi-scale Granularity [17] & 81.71\% \\ Ours & **84.23\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy on the FGVC-Aircraft dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline PD [36] & 72.0\% \\ SCDA [37] & 78.8\% \\ B-CNN [25] & 81.1\% \\ PIR [38] & 80.4\% \\ Ours & **80.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy on the Stanford Dogs dataset. To verify that the proposed model has good performance on the challenges faced in the project, we also tested our model on the CompCars dataset.