# 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
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **DenseNet121** & **InceptionV3** & **MobileNetV2** & **ResNet50** \\ \hline Backbone Network & Dense Block & Inception Module & Inverted Residual & Residual Block \\ Input Size & 224 \(\times\) 224 & 229 \(\times\) 229 & 224 \(\times\) 224 & 224 \(\times\) 224 \\ Batch\_size & 64 & 64 & 64 & 64 \\ Lr & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) & \(1\times 10^{-3}\) \\ Dropout & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Structural configurations of the four baseline models. Figure 10: Test accuracy and loss curves of the five models on the self-constructed KRHO dataset. further enriches the model's feature representation; and the dynamic K strategy adaptively adjusts the classification decision boundaries, thereby effectively improving the overall performance. The synergistic effect of these components leads to the optimal performance on the test set, underscoring the critical contribution of each module to the network's enhanced performance. During the experimental process, to ensure fair comparison with existing methods, we employed detailed evaluation metrics including Precision, Recall, F1 Score, Accuracy, Macro Average (MA), Weighted Average (WA), and Support. Precision represents the proportion of true positive instances among all samples predicted as positive. Recall measures the proportion of actual positive instances that are correctly identified by the model. The F1 Score, defined as the harmonic mean of Precision and Recall, prevents overemphasis on a single metric by ensuring that a low value in either Precision or Recall results in a correspondingly low F1 Score. Accuracy indicates the proportion of samples that the model correctly predicts, irrespective of class. Macro Average reflects the model's overall balance across all categories, while Weighted Average more accurately represents the model's performance on dominant classes (those with a larger number of samples) in cases of imbalanced class distributions. Support denotes the number of true samples for each category in the test set. The computation formulas for each evaluation metric are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{23}\]  
\[Recall=\frac{TP}{TP+FN} \tag{24}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{25}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{26}\]  
\[MA=\frac{1}{N}\sum_{c=1}^{N}Precision_{c} \tag{27}\]  
\[WA=\frac{\sum_{c=1}^{N}(Precision_{c}\times Support_{c})}{\sum_{c=1}^{N}Support _{c}} \tag{28}\]  
Here, TP denotes true positives, i.e., instances predicted as positive where the actual label is also positive; FP denotes false positives, i.e., instances predicted as positive while the actual label is negative; FN denotes false negatives, i.e., instances predicted as negative despite the actual label being positive; and TN denotes true negatives, i.e., instances predicted as negative where the actual label is also negative. Table 6 presents the evaluation report for the five models on the self-constructed KRHO dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment Name** & **Attention** & **Histogram Features** & **Dynamic K** & **Training Accuracy/\%** & **Test Accuracy/\%** \\ \hline Our Method & \(\surd\) & \(\surd\) & \(\surd\) & **99.49** & **99.33** \\ No Attention & \(\times\) & \(\surd\) & \(\surd\) & 99.30 & 98.12 \\ No Hist & \(\surd\) & \(\times\) & \(\surd\) & 99.19 & 98.51 \\ Fixed K & \(\surd\) & \(\surd\) & \(\times\) & 99.37 & 97.52 \\ No Attention and No & \(\times\) & \(\times\) & \(\times\) & 99.02 & 97.13 \\ Hist and Fixed K & & & & & \\ \hline \hline \end{tabular}  
* The best results are marked in bold. “\(\surd\)” indicates the existence of this module, “\(\times\)” represents None.  
\end{table} 
Table 5: Ablation experiment test results. As evidenced by the results presented in Table 6, our proposed method significantly outperforms the comparative approaches across all evaluation metrics.