# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 4 Results and Discussion
### Experiment and Analysis of Ancient Towers Dataset  
Globally, the ancient buildings of a city are a reflection of its history, culture, and urban landscape. The ancient tower, as a traditional Chinese building with a specific form and style, has become an important type of Chinese architectural art. In many Chinese cities, ancient towers have become easily identifiable physical objects in the landscape and landmark buildings of a city. For example, the Leifeng Tower in Figure 1, a landmark of Hangzhou's West Lake, has become a landmark building in Hangzhou, embodying the history and culture of this ancient city. However, there are inevitably many similar buildings to these towers, and many of them are subject to \"face-collision\". As shown in Figure 1, vertically, different types of towers have very similar shapes and textures, making them very confusing to the layman, while horizontally, images of the same type of tower from different angles vary greatly. The above situation is very appropriate for fine-grained tasks. To solve this problem, we have collected many ancient tower buildings in many provinces in China and constructed a fine-grained, rich, and realistic dataset of ancient towers. The dataset of the ancient towers image stays close to the reality of environmental interference, such as the presence of surrounding objects, including only the local main situation, the target building showing multi-angle shooting differences, and differences of day and night views of the same building, as shown in Figure 6. Using this dataset with a fine-grained algorithm enables accurate identification  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **Accuracy** \\ \hline Resnet152 & Resnet152 & 224 \(\times\) 224 & 61.1\% \\ Densenet161 & Densenet161 & 224 \(\times\) 224 & 62.6\% \\ IG-CMAN [35] & Densenet161 & 224 \(\times\) 224 & 67.5\% \\ ViT [26] & ViT-B\_16 & 448 \(\times\) 448 & 67.4\% \\ TPSKG [45] & ViT-B\_16 & 448 \(\times\) 448 & 69.5\% \\ CBiAFormer-B [53] & Swin-B & 384 \(\times\) 384 & 70.9\% \\ Ours & ConvvNeXt-B & 448 \(\times\) 448 & **72.8\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison of different algorithms on ISIA Food-200. Bold values represent the best performing data for each column.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **Accuracy** \\ \hline TPSKG [45] & ViT-B\_16 & 448 \(\times\) 448 & 90.1\% \\ TransFG [46] & ViT-B\_16 & 448 \(\times\) 448 & 90.8\% \\ IELT [49] & ViT-B\_16 & 448 \(\times\) 448 & 90.8\% \\ MP-FGVC [50] & ViT-B\_16 & 448 \(\times\) 448 & 91.0\% \\ ACC-ViT [21] & ViT-B\_16 & 448 \(\times\) 448 & 91.4\% \\ Ours & ConvvNeXt-B & 448 \(\times\) 448 & **91.5\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._of ancient towers and enhances urban landscape analysis. This has important research implications in urban planning and provides a new perspective on the application area of fine-grained tasks.  
For the dataset of ancient towers, we collected 13,815 images of ancient towers, including 229 categories, of which 6915 images were used for training and 6900 for validation. On this dataset, we first experimented with several backbone networks commonly used for fine-grained tasks. As shown in Table 5, Resnet50, Resnet101, Densenet161, and VIT achieved 92.3%, 93.4%, 93.6%, and 93.8% accuracy on the towers dataset, respectively. According to the experiments on the three datasets, this paper selected several algorithms with excellent performance to undertake the contrast experiments on the dataset, including FBSD, PMG, FFVT, and TransFG. The accuracy of this method is 95.2%, which is better than other algorithms. This aligns with the conclusions drawn from the three different public datasets mentioned earlier, demonstrating the advantages of the proposed LDGNet in discriminative feature extraction and reflecting its effectiveness and innovation.