# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 5 Experiments
### Experiments and Results Analysis  
#### 5.3.1 Experiments on the Self-Constructed KRHO Dataset  
To validate the effectiveness of the proposed algorithm, we first conducted comparative experiments on the self-constructed KRHO dataset against four widely used classification models: DenseNet121 [37], InceptionV3 [38], MobileNetV2 [39], and ResNet50 [40]. They were chosen because DenseNet121, InceptionV3, MobileNetV2, and ResNet50, re \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Parameter** & **Symbols** & **Parameter** \\ \hline Batch\_size & 64 & Epochs & 100 \\ Num\_workers & 8 & Lr & \(2\times 10^{-4}\) \\ Knn\_k & 20 & Epochs & 100 \\ Cnn\_feat\_dim & 1024 & Num\_ heads & 8 \\ Hist\_feat\_dim & 512 & Dropout & 0.3 \\ Num\_classes & 4 & Weight\_decay & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model hyperparameter settings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Configuration/Operation** & **Input Size** & **Output Size** \\ \hline Data Preprocessing & Data enhancement & Graphics 224 \(\times\) 224 \(\times\) 3 & Graphics 224 \(\times\) 224 \(\times\) 3 \\  & & Histogram 512-D \\ Feature Extraction & CNN & Graphics 224 \(\times\) 224 \(\times\) 3 & Feature 1024-D \\ Feature Fusion & CNN feature and LAB & Feature 1024-D & Feature 1536-D \\ Graph Construction & Dynamic KNN & Fusion feature N \(\times\) 1536 & - \\ Dynamic GNN & GATv2 Conv & Node feature 1536-D & Renewal feature 256-D \\ Classification & FC classifier & Node feature 256-D & Category prediction 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Our proposed method structure and parameter settings. Figure 9: Detailed information of the self-constructed KRHO dataset. spectively, represent four mainstream design paradigms, namely feature reuse, multi-scale extraction, lightweight, and deep residual, which can comprehensively evaluate the performance of the proposed methods under different architectures. The structural configurations of these four models are detailed in Table 4, while the classification accuracy and loss trends for all five methods are illustrated in Figure 10. As shown in the accuracy and loss curves in Figure 10, our proposed method demonstrates a clear performance advantage on the KRHO dataset. It achieves a rapid increase in accuracy during the early stages of training and consistently maintains a leading position throughout the training process. Likewise, the loss curve shows a sharp initial decline and remains significantly lower than those of DenseNet121, InceptionV3, MobileNetV2, and ResNet50 throughout the entire training phase. Our method not only surpasses other architectures in terms of convergence speed and final accuracy but also exhibits smaller fluctuations on the test set, highlighting its superior feature learning capability and generalization performance for the garbage classification task. To further demonstrate the contribution of each improvement to the network's performance, we conducted ablation experiments assessing the effects of the attention module applied to CNN-extracted features, the use of histogram features for multimodal fusion, and the implementation of a dynamic K value. In these experiments, when employing the dynamic K strategy, K is set to 20; in contrast, when not using dynamic K, K is fixed at 10 since a fixed K of 20 can lead to graph construction issues, with the maximum fixed value being 14. The comparative numerical results of the ablation experiments are presented in Table 5. The results clearly indicate that our method, which integrates the attention mechanism, histogram feature fusion, and dynamic K selection, achieves the highest test accuracy, significantly outperforming approaches that incorporate only some of these components. Specifically, the inclusion of an attention mechanism enhances the network's ability to focus on discriminative target regions; the fus