# Local Diversity-Guided Weakly Supervised Fine-Grained Image Classification Method
## 4 Results and Discussion
### Comparisons with State-of-the-Art Methods
#### 4.1.2 Results on NABirds  
NABirds, which contains 48,562 images of birds in 555 categories, each containing about 100 images, is a larger, finer-grained dataset than CUB-200-2011, and more challenging for fine-grained tasks. Table 3 shows that the proposed method achieves an accuracy of 91.5% on this dataset, which is 0.5% and 0.1% higher than that of MP-FGVC and ACC-ViT. On the one hand, this reflects the advantage of combining local detail extraction with global modeling, as well as the important role of the complementary relationship be-tween high-level and low-level features in enhancing the robustness of classification features. On the other hand, the data in the table show that this method still performs well on large datasets, which reflects the robustness and good applicability of the LDGNet across different scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **CUB-200-2011** \\ \hline Part-based R-CNN [2] & VGG & 227 \(\times\) 227 & 76.4\% \\ PoseNorm [38] & VGG & - & 75.7\% \\ KERL [37] & VGG & 224 \(\times\) 224 & 87.0\% \\ BCNN [6] & VGG & 224 \(\times\) 224 & 84.1\% \\ MAMC [39] & Resnet50 & 448 \(\times\) 448 & 86.2\% \\ NTS [15] & Resnet50 & 448 \(\times\) 448 & 87.5\% \\ LIO [40] & Resnet50 & 448 \(\times\) 448 & 88.0\% \\ FDL [17] & Resnet50 & 448 \(\times\) 448 & 88.6\% \\ CSC-Net [41] & Resnet50 & 224 \(\times\) 224 & 89.2\% \\ SCAPNet [42] & Resnet50 & 224 \(\times\) 224 & 89.5\% \\ PMG [16] & Resnet50 & 448 \(\times\) 448 & 89.6\% \\ MAMC [39] & Resnet101 & 448 \(\times\) 448 & 86.5\% \\ CIN [43] & Resnet101 & 448 \(\times\) 448 & 88.1\% \\ FBSD [47] & Densenet161 & 448 \(\times\) 448 & 89.8\% \\ API-Net [44] & Densenet161 & 448 \(\times\) 448 & 90.0\% \\ ViT [26] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 90.2\% \\ TPSKG [45] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 91.3\% \\ FFVT [28] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 91.6\% \\ I2-HOFI [48] & Xception & 224 \(\times\) 224 & 91.6\% \\ TransFG [46] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 91.7\% \\ IELT [49] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 91.8\% \\ MP-FGVC [50] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 91.8\% \\ ACC-ViT [21] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 91.8\% \\ Ours & ConvNeXt & 448 \(\times\) 448 & **92.3\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy rates of different algorithms on CUB-200-2011. 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 PC-CNN [20] & Densenet161 & 224 \(\times\) 224 & 82.8\% \\ MaxEnt [51] & Densenet161 & - & 83.0\% \\ Cross-X [22] & Resnet50 & 448 \(\times\) 448 & 86.4\% \\ HGNet [52] & Resnet50 & 448 \(\times\) 448 & 86.4\% \\ DSTL [23] & Inception-v3 & 560 \(\times\) 560 & 87.9\% \\ GaRD [19] & Resnet50 & 448 \(\times\) 448 & 88.0\% \\ ViT [26] & ViT-B\({}_{16}\) & 448 \(\times\) 448 & 89.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of accuracy rates of different algorithms on NABirds. Bold values represent the best performing data for each column.