# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 5 Experiments
### Experiments and Results Analysis
#### 5.3.2 Experiments on the Public Dataset TrashNet  
To comprehensively validate the generalization capability of the proposed method, we conducted rigorous experiments on the public TrashNet dataset following initial verification on our self-constructed dataset designed to simulate real-world application scenarios. Public datasets provide an objective basis for evaluating model robustness and transfer performance under varying data distributions. As shown in Figure 12, the proposed method continues to demonstrate superior classification performance on the public dataset, with both accuracy and loss metrics significantly outperforming state-of-the-art models such as DenseNet121, InceptionV3, MobileNetV2, and ResNet50. These results conclusively highlight the excellent generalization ability of our approach in diverse data environments. Figure 11: Confusion matrices of the five models on the self-constructed KRHO dataset. Figure 12: Test accuracy and loss curves of the five models on the public TrashNet dataset. To further validate the broad applicability of the proposed model algorithm, we conducted a more in-depth evaluation on the public TrashNet dataset. The evaluation results are presented in Table 7.  
As shown in Table 7, our experiments on the public TrashNet dataset further highlight the robustness and generalization capabilities of the proposed method. In the four primary categories--Cardboard, Glass, Paper, and Plastic--the model achieved precision, recall, and F1 scores approaching 1.00, demonstrating significant performance advantages over DenseNet121, InceptionV3, MobileNetV2, and ResNet50. Notably, even in the most challenging category (e.g., Plastic), our method maintained a high level of accuracy, with an overall accuracy of 0.99, markedly outperforming the alternative networks. This not only underscores the strong discriminative power of the proposed model in diverse scenarios but also confirms that its feature extraction and classification strategies are both universally applicable and robust, thereby ensuring outstanding performance even under rigorous public dataset evaluations. To further validate the general applicability of the proposed method in broader domains, we conducted a confusion matrix analysis on the public TrashNet dataset, with the detailed results presented in Figure 13. Our approach exhibits a noticeably purer diagonal distribution across the four major garbage categories, with nearly all predictions highly concentrated at the intersections corresponding to the correct classes, and minimal cross-category misclassifications. In contrast, other methods still show significant confusion between some highly similar classes--for example, a considerable number of misclassifications occur between Glass and Paper--whereas our method maintains consistently high accuracy even for challenging samples. These findings further corroborate the robustness and generalization ability of our proposed feature extraction strategy across diverse sample types, underscoring its potential to rapidly and accurately perform classification tasks in a variety of real-world scenarios.