## 4 Results
### Azure Custom Vision in PBIA Context
Although the theoretical increase in data volume should improve the model's performance, in this case, the introduction of additional examples led to a partial \"saturation\" of the model with similar features, causing it to be less discriminative in certain complex scenarios. The phenomenon requires a detailed analysis of the distinct characteristics of the objects, possibly through the introduction of additional filters in the training process or through the manual refinement of the dataset. Test 4 involved training based on 150 PET-labeled images for 30 min. The precision, recall, and mAP returned to 100%. However, the model still misclassified all the test images (both PET and non-PET) as PET images with over 99% probability, indicating that it had not learned to generalize beyond the training data. In Test 5, 150 PET-labeled images were used for training, with an extended training time of 2 h (120 min). The precision, recall, and mAP remained at 100%. Even with the additional training time, the model continued to misclassify non-PET objects as PET objects, with probabilities exceeding 99%, indicating that the misclassification issue persisted. In Test 6, 345 PET labels and 625 non-PET labels (including cardboard, metal, and glass objects) were used for training. As Table 1 shows, the results for this test show lower performance metrics compared to Tests 1 and 2. Test 6 confirms that increasing the dataset will not solve the issue of confusion between PET and glass bottles. Increasing the number of objects in the training stage will increase the model's confusion. Conversely, maintaining a moderate number of objects in the dataset will decrease the model's classification ability. To solve this issue, this paper introduces a new algorithm, combined with hardware facilities. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Test** & **The Number of** & **Average Training** & **Precision** & **Recall** & **mAP** & **Accuracy** & **The Number of Correct Labels** \\
**No.** & **Training Images** & **Time (min)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **for 100 Unseen Images (\%)** \\ \hline
1 & 30 & 15 & 100 & 100 & 100 & 99 & 99 \\
2 & 60 & 15 & 100 & 100 & 100 & 99 & 99 \\
3 & 100 & 30 \({}^{\star}\) & 90.9 & 100 & 100 & 99 & 99 \\
4 & 150 & 30 & 100 & 100 & 100 & 99 & 99 \\
5 & 150 & 120 & 100 & 100 & 100 & 99 & 99 \\
6 & 970 & 30 \({}^{\star}\) & 94.3 & 94.3 & 99 & 99 & 99 \\ \hline \end{tabular} \({}^{\star}\) Time imposed by the authors.  
\end{table} 
Table 1: Sample of the training test results. The loop runs 100 iterations and constructs the file path for each unseen test image. Next, each image is asynchronously sent to the Azure Custom Vision API for prediction. The response (resPET) is compared with the expected label, as follows:  
* If the prediction is not \"PET\" but the image is one of the first 50 images (assumed to be PET), then wrongPET is incremented;
* If the prediction is \"PET\" but the image index is above the first 50 images (assumed to be non-PET), then wrongPET is incremented. A delay is added between requests to avoid API overload. The primary issue observed throughout these experiments is that the model consistently misclassifies non-PET objects as PET objects, even after adjustments in the dataset size and training conditions. An important finding from the tests was that training exclusively with PET items can lead to overfitting, where the model memorizes training examples rather than learning to generalize. Using a balanced dataset that includes various examples from both categories minimizes the risk of overfitting. This encourages the model to learn consistent features across PET and non-PET categories. Consequently, the model was trained using a balanced dataset that included both PET and non-PET items. This will help improve its ability to distinguish between the two categories. We expect the model to learn to identify distinguishing features that differentiate these categories by including PET and non-PET items in the training set.