# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
## 4 Experiment
### Conducted Experiments and Results
#### 4.3.4 Experiment 4  
To assess the robustness of the proposed method, Experiment 4 measures the performance of various tree-structured classifiers using different CNN models on different datasets, including aircraft [28], birds [29], and cars [30]. The performance of the proposed method is compared with different traditional CNN models, including ResNet [26], Inception [33], and MobileNet [34]. While holding the following variables constant, SVM with a linear kernel function is used as the machine-learning matcher, and the k-means++ algorithm with Euclidean distance is utilized for clustering. Table 7 illustrates the classification accuracy obtained using different structures across diverse datasets. Initially, to demonstrate the effect of training the proposed model on diverse datasets, Figure 12 depicts the parts of training and validation loss figures.  
Table 7 displays the classification accuracy achieved with different structures on various datasets. For instance, using MobileNet to build a tree-structured classifier yields a 2.01% improvement in accuracy. The results indicate that, compared to traditional ResNet, MobileNet, and Inception models, the proposed classifier enhances the classification accuracy of multiple datasets by 1.17%, 2.01%, and 0.59% on the bird, aircraft, and car datasets, respectively. Overall, the proposed tree-structured framework improves classification accuracy in comparison to traditional CNN models. Furthermore, in the airplane dataset, the number of child clusters in the tree-structured classifier is three, resulting in a 2.01% improvement in accuracy. However, in the car dataset, the number of child clusters is two, and the improved accuracy is only 0.59%, which is not particularly significant. This supports the conclusion of **Experiment 1**, where the use of three child clusters exhibits better classification results. Additionally, the result shows that the proposed tree-structured  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Applied** & \begin{tabular}{c} **Number** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Proposed** \\ **Classifier** \\ **Clusters** \\ \end{tabular} & \begin{tabular}{c} **Traditional** \\ **CNN** \\ **Classifier** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Proposed** \\ **Classifier** \\ **Training** \\ **Time** \\ \end{tabular} &
\begin{tabular}{c} **Traditional** \\ **CNN** \\ **Training** \\ **Time** \\ \end{tabular} \\ \hline Bird & ResNet & 3 & 70.15\% & 68.63\% & 993.25 s & 1480.41 s \\ Aircraft & MobileNet & 3 & 74.20\% & 72.19\% & 1962.21 s & 3060.01 s \\ Car & Inception & 2 & 82.83\% & 83.42\% & 3435.48 s & 4017.29 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of proposed method on various datasets with different child clusters.  
Figure 12: Training and validation loss of proposed classifiers using different datasets.  
classifier can considerably reduce the training time compared to the traditional CNN model. This is reflected in a 32.91%, 35.87%, and 14.48% reduction in training time of the bird, aircraft, and car datasets, respectively, compared to the traditional ResNet, MobileNet, and Inception models.  
In summary, the aforementioned four experimental outcomes suggest that the proposed approach improves the accuracy and training time of classification in various datasets relative to conventional CNN models. Furthermore, the tree-structured classifier with three clusters yields superior accuracy compared to other configurations, while the training time of the model decreases as the number of child clusters increases. Moreover, the experiments indicate that the accuracy of a tree classifier rises to a certain limit as the number of clusters increases and subsequently decreases, emphasizing the importance of the proposed pruning criterion. Finally, the experimental results show that SVMs with linear kernel functions attain higher classification accuracy, which aligns with Cover's Theorem.