## 4 Results
### Azure Custom Vision in PBIA Context
For example, training with various PET bottles alongside objects allows the model to recognize the unique attributes of PET items compared to the characteristics of non-PET items. The model, based on two labels, was trained using 60 images for PET objects and 60 images for non-PET objects, as shown in Figure 9. It achieved a precision of 96%, a recall of 100%, and an mAP of 100%. After conducting 100 tests, Azure Custom Vision labeled the objects as follows:  
Figure 9: Training performance of the model, with PET and non-PET labels. * Eleven FNs (PET classified as non-PET). These errors indicate that the model struggled to correctly identify 11 PET items, instead misclassifying them as non-PET items;
* Two FPs (non-PET classified as PET). This means that two objects not made of PET were incorrectly identified as PET. This error could arise from visual similarities in the object's features, such as texture or shape, which may have confused the model. In this case, the overall accuracy was 87%, but the PET identification accuracy was 96%, as only 2 out of 50 items were identified as non-PET objects. The system improved its accuracy after applying the _PIC_ index Equation (1), which integrates the _RecFactor_ from Azure Custom Vision, and the _CorFactor_ using Equation (2), based on the object's weight. Only two items out of 100 were ultimately misclassified, labeled explicitly as PET objects when they were non-PET objects, after applying Equation (1). Reducing the misclassifications from 13 (2 non-PET and 11 PET) to just 2 demonstrates the utility of the proposed mathematical formalism introduced by the _PIC_ index. The initial error rate from Azure Custom Vision was 13%, but after applying the _PIC_ index, it dropped significantly to just 2%, equating to an overall accuracy rate of 98%. This improvement highlights the importance of incorporating additional parameters, such as weight, into the decision-making process. The PBIA incorporates the _PIC_ index to help filter misclassifications by adding an extra validation layer beyond visual object recognition. The model's visual accuracy and object weight, used as a secondary criterion, led to a much more reliable classification system. Subsequently, we conducted tests in Azure Custom Vision to improve the precision parameter, utilizing 100 training images for non-PET items and 60 for PET items. The values were chosen to be unequal, accounting for the complexity of the non-PET class compared to the PET class. The results yielded a precision of 69.6%, a recall of 100%, and an mAP of 95.0%, as presented in Figure 10. In a different scenario, when we employed 100 images for both PET and non-PET items, the outcomes were a precision of 62.5%, a recall of 100%, and an mAP of 97.5% (Figure 11). Figure 10: Performance metrics of Azure Custom Vision model trained on 60 images of PET items and 60 images of non-PET items. Interestingly, the combination of 60 images for each category produced the most favorable results, achieving a precision of 96%, a recall of 100%, and an mAP of 100%. The superior performance of the 60-image scenario can be attributed to several factors. First, a balanced dataset of 60 images for each class likely allowed the model to better learn the distinguishing features of PET and non-PET items without being overwhelmed by the noise from excess photos. When using 100 images of non-PET items, the model focused too much on the distinguishing features, thereby diluting the training signal of the PET items. Additionally, a smaller training set can sometimes lead to a more effective training process by forcing the model to generalize better from fewer examples, which can result in higher precision without sacrificing recall. This indicates that while the model could accurately identify all instances of the PET class (resulting in a recall of 100%), it was also able to significantly improve its precision when trained using 60 images per category, thus striking an optimal balance between the two metrics.