## 4 Results and Analysis
### Results on Scene Classification Dataset  
In our experimentation, we employed a dataset comprising a total of 3000 images, with 2400 images designated for training purposes and the remaining 600 images reserved for testing. To comprehensively assess the performance of our scene recognition model, we conducted several analyses and evaluations. Figure 5 shows the accuracy and loss curves, which provide critical insights into the training and validation performance of our scene recognition model. These curves, which illustrate the model's progress over epochs, serve as valuable tools for assessing its convergence, stability, and generalization capabilities.  
In the figure, the accuracy curves for both training and validation showcase the model's ability to correctly classify scenes across various categories. From the outset, at iteration 0, both training and validation accuracies were observed at approximately 20%. As training progressed, these accuracies demonstrated steady and consistent improvement. By epoch 10, the accuracy curves had reached remarkable stability, with the validation accuracy peaking at 100%. This high level of validation accuracy signifies the model's robustness and proficiency in recognizing scenes. Notably, the curves remained stable through epoch 14, with no substantial changes observed. This stability prompted manual termination of the curve analysis at epoch 14, as no further significant improvements were anticipated.  
The loss curves, indicative of the model's optimization progress, revealed a similar trend. At the inception of training, both training and validation losses began at approximately 3%. Subsequently, these losses gradually decreased, indicative of the model's capacity to minimize errors and enhance its scene recognition capabilities. By epoch 8, the loss curves had reached a state of stability, with both training and validation losses converging to 0%. This remarkable achievement underscores the model's effective learning and convergence capabilities, with minimal discrepancies between training and validation  
Figure 5: Training and validation progress of our method on the scene classification dataset.  
losses. It is worth noting that this remarkable training and validation performance was achieved efficiently. The training process, from start to finish, required a total elapsed time of 21 min and 40 s. This efficiency in achieving high accuracy with minimal loss underscores the effectiveness and computational efficiency of our scene recognition model.  
The confusion matrix presented in Figure 6 provides a detailed breakdown of the classification performance of our scene recognition model on the test sets across six distinct scene categories: Building, Forests, Glacier, Mountains, Sea, and Street. From the figure, the model achieved perfect classification, with all 101 samples correctly identified as such for the Building class. Similarly, the Forests class also yielded perfect classification, with all 103 samples correctly identified. The Glacier class exhibited impeccable performance, with all 124 samples correctly classified. For the Mountains class, the model achieved a high level of accuracy, with 83 out of 83 samples correctly classified. The Sea class was also accurately recognized, with all 102 samples correctly classified. In the Street class, the model displayed strong performance, with 87 out of 87 samples correctly classified.