## 3 Results  
### Positive Augmentations  
In Table 3, the model with the best performance appeared to be the \"Baseline Rotated\" model, obtaining an accuracy of 0.655, Precision of 0.621, Recall of 0.645, and an F1-Score of 0.618 (The names of the models represent various augmentation approaches, indicating that the base data was transformed in accordance with the methods outlined in Table 2. The results presented correspond to the performance of these models when evaluated on the test set after the training process.). The high accuracy indicated that this model was successful in correctly predicting the classification most of the time, while the substantial F1-Score, which is a harmonic mean of precision and recall, indicated a balanced high performance in both these areas. This suggested that the model could retrieve a high proportion of relevant instances (high recall), while ensuring the proportion of instances it claimed to be relevant were indeed relevant (high precision). In contrast, the model with the lowest performance in the evaluation was the \"Horizontal Split\" model. With an Accuracy of 0.560, Precision of 0.497, Recall of 0.563, and an F1-Score of 0.501, this model consistently fell behind the other models across all performance metrics, indicating lower overall performance. It can be observed that there was a clear downward trend in performance metrics from the \"Baseline Rotated\" model to the \"Horizontal Split\" model. Interestingly, models using the \"Flip\" modification, such as \"Horizontal Split Flip\" and \"ROI Split Flip\", tended to have a lower performance than their non-flip counterparts. The only exception to this was the \"Horizontal Split 20% Flip\" model, which slightly outperformed the \"ROI Split\" and \"Horizontal Split\" models, suggesting that the impact of the \"Flip\" modification could be influenced by broader image overlap (0.20 in that case). The provided confusion matrices in Figure 6 revealed the performance of nine distinct models: Baseline, Baseline Rotated, Horizontal Split, Horizontal Split Flip, ROI Split, ROI Split Flip, Horizontal Split 20%, and Horizontal Split 20% Flip. The Baseline model performed well for KL0 and KL4 but encountered difficulties with the intermediate classes. This issue was partly alleviated in the Baseline Rotated model for KL0. The Horizontal Split and Flip models demonstrated variability in performance across classes, with the Flip version slightly improving the accuracy for KL2 and KL3. The Region of Interest (ROI) models exhibited improvements for the intermediate classes, especially KL2. The ROI Split and Flip models offered a more balanced performance across classes, particularly for KL0 and KL1. Lastly, the Horizontal Split 20% and its Flip variant showed high misclassification rates between KL0 and KL1, although the Flip version brought some improvement. However, KL4 was well classified across all models, suggesting distinct features that differentiated it from other classes. Models with the \"Flip\" modification seemed to have a more evenly distributed confusion matrix, indicating a more balanced prediction across different classes. However, this did not always result in overall higher performance, as seen in the \"Horizontal Split Flip\" model. The \"Baseline Rotated\" model seemed to perform well for the first and last class, but its performance decreased notably for the other classes. This behavior was shared across models, where models often performed better for the first and last class. The \"ROI\" and \"ROI Split\" models presented a similar pattern, with typical performance in the first and last classes. Reviewing the results from the different model iterations, it was quite unexpected to find that the ROI (Region of Interest) models did not manage to outperform the Baseline models. Given that the Baseline models leveraged the entire image and the ROI models focused on the specific region expected to contain more relevant information for the task, it was anticipated that the ROI models would perform better.