## 4 Results
### Azure Custom Vision in PBIA Context
This is particularly concerning because it means the condition goes undetected. Recall is calculated using Equation (5). \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\;[\%]\] (5)
* Mean average precision (mAP) is a metric that evaluates a model's performance by calculating the average precision (AP) values across different confidence thresholds and Intersection over Union (IoU) levels. An mAP of 100% signifies that the model accurately identifies objects and has a high confidence level across varying detection scenarios. The AP and mAP are computed using Equations (6) and (7), respectively. \[\text{AP}=\int_{0}^{1}\text{Precision}(\text{Recall})\text{dRecall}\;[\%]\] (6) \[\text{mAP}=\frac{\frac{\sum\limits_{i}^{n}(\text{AP}_{i})}{n}}{[\%]}\] (7) where API is the average precision for class i; mAP is the mean average precision; n is the number of classes. * Accuracy represents the proportion of correct predictions made in terms of TPs and true negatives (TNs) in regard to the total number of predictions. This value represents the weight of correct predictions that identified the label \"PET\" and \"non-PET\" in regard to all the predictions. Accuracy is calculated using Equation (8). \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\;[\%]\] (8)  
Multiple tests were performed with the label \"PET\" to identify the optimal number. At this stage, the main objective was to develop a model that determines whether an object in an image is a PET object. After training, the model was tested with 50 images containing PET objects and 50 with non-PET objects. The test images for the model are unseen, meaning they differ from the training images. Table 1 presents a sample of the results from the training tests. The precision, recall, and mAP reflect the model's performance based on the training set and were measured at 100% for most tests. This indicates that the model performed flawlessly in classifying the PET objects, based on the data on which it was trained. The 99% probabilities during testing refer to the model's confidence in its predictions for the 100 unseen test data. The Azure Custom Vision service generated the precision, recall, and mAP values, which the authors modified only by changing the number of test images. The unseen test images were kept the same for all five tests. The images are analyzed using the pseudocode for the tests. In Test 1, 30 PET-labeled images were used for training for 15 min. The precision, recall, and mAP were all measured at 100%. These metrics indicate the model perfectly identified PET objects in the training set. However, when tested on 50 unseen PET images and 50 unseen non-PET images, the model classified all the images as PET images, assigning probabilities exceeding 99%. This misclassification points to overfitting, where the model fails to generalize to new data. Test 2 included training using 60 PET-labeled images for 15 min. The precision, recall, and mAP remained at 100%. The model continued to misclassify non-PET objects as PET objects with probabilities over 99%, indicating no improvement in generalization. In Test 3, 100 PET-labeled images were used for training for 30 min. The precision dropped to 90.9%, although the recall and mAP stayed at 100%. The slight drop in precision indicates some errors in identifying PET objects within the training set. Despite improved training performance, non-PET images were still misclassified as PET images, with over 99% probability, indicating the model's inability to correctly distinguish between the two categories in unseen data. The use of a balanced dataset with an increased number of images used for training resulted in a decrease in accuracy in some tests (e.g., Test 3). The emergence of common features in both PET and non-PET images explains this decrease. These characteristics are shape and transparency. These common characteristics introduce ambiguity into the classification process.