## 3 Materials and Methods
### Azure Custom Vision Methodology
In that case, the model has too many images and is overfitted. If the model is underfitted (for example, poor performance on the training and validation sets), more images are added. The standard metrics used to assess the model performance are accuracy, precision, recall, AP (average precision), and F\({}_{1}\)-Score [6, 58]. * Accuracy is the weight of true predictions out of all predictions and is computed with Equation (1). \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\;[\%]\] (1)
* Precision is determined by Equation (2) as a true positive (TP) prediction of all positive predictions. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\;[\%]\] (2)
* Recall represents the effective identification of true predictions. It is calculated by Equation (3). \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\ [\%] \tag{3}\]
* AP is calculated by Equation (4) and consists of the area beneath the precision-recall curve. \[\text{AP}=\int_{0}^{1}\text{Precision}(\text{Recall})\text{dRecall}\ [\%]\] (4)
* F\({}_{1}\)-Score is determined based on precision and recall with Equation (5): \[\text{F}_{1}-\text{Score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}}\times 100\ [\%]\] (5)  
At this stage, we computed four of the above-mentioned metrics, namely, accuracy, precision, recall, and AP, to assess whether the model performed well enough to classify waste correctly. The ACVS performance parameters of the model are presented in Figure 4. The obtained values indicate that the model for classifying waste categories (glass, plastic, metal, and cardboard) has performed exceptionally well in a multi-class classification task with 100 images per category. The model achieved a perfect 100% precision. At the same time, the model identifies 98.8% of all true instances of the categories in the validation dataset. This is a strong result, though it suggests that there may be a very small number of missed predictions. The 100% AP score suggests that across all categories, the model's predictions were well calibrated. The model has 100% precision, meaning every glass image was classified correctly. The recall is also 100%, indicating that all glass instances were detected. In Figure 4, the glass category's AP is also perfect. The precision for plastic is again 100%, but the recall drops slightly to 95.2%, meaning the model missed a small number of plastic items. However, the AP is still 100%, reflecting well-calibrated predictions for this category. Similar to glass and plastic, metal is classified with 100% precision and a very high recall of 100%, indicating perfect performance. The cardboard category also performs flawlessly with 100% precision, 100% recall, and 100% AP. Each category was tested with 100 images, ensuring the model was evaluated across a balanced number of examples for all categories. The model has performed excellently across all categories  
Figure 4: Performance evaluation of ACVS using 100 images per category. with near-perfect scores in precision, recall, and average precision. Considering the overall high performance, the slight dip in recall for the plastic category (95.2%) should not raise concerns. In a second iteration of the training process, we try to increase the recall by adjusting the number of images. The results of the performance parameters obtained are presented in Figure 5. The minor decrease in performance (precision and recall dropping from 100% to 97.9%) indicates that as the dataset expands, the model starts to make more general predictions based on a broader set of features. The slightly lower recall for plastic indicates that the model has to deal with more varied plastic types, reducing its sensitivity to certain plastic objects. In the third iteration, the model was re-trained using 250 images for each category. In this third iteration, despite the number of images being the same, the obtained precision was 96.4%, the recall was 95.9%, and the AP was 98.5%.