## 4 Results
### Azure Custom Vision in PBIA Context  
Azure Custom Vision is a cloud-based service offered by Microsoft Azure that enables developers to build, deploy, and improve custom image classifiers and object detection models using ML. This study examines the factors that influence the number of images required for training a practical object detection model using the Microsoft Azure Custom Vision tool. Object detection involves identifying and pinpointing objects within an image. Microsoft Azure Custom Vision offers an intuitive interface that enables users to train models for detecting specific objects. Users upload images, label them accordingly, and assess the model's performance. The platform facilitates classification (categorizing images) and object detection (locating objects within the images). This article focuses on object detection, as the primary objective is the identification of PET bottles. The model trained using Azure Custom Vision is influenced by several factors, including the complexity of the analyzed object (e.g., different shapes, colors, and sizes), image quality, and environmental conditions (lighting, background, angles, and occlusions). A significant challenge faced by object detection models is the number of images utilized during the training process. An inadequate number of training data can result in underfitting, a scenario in which the model fails to capture the underlying patterns present in the data. This often leads to suboptimal performance when evaluating unseen images, as the model lacks the necessary exposure to learn from diverse examples. Conversely, excessive photos can lead to overfitting, where the model becomes too tailored to the training dataset and loses its ability to generalize to new, unseen data. The primary challenge, therefore, lies in determining the optimal number of images necessary for training, which is contingent upon the project's specific requirements. Achieving a balance between having sufficient data to capture the diversity of the target objects and avoiding the pitfalls of underfitting and overfitting is paramount for developing a production-ready model for real-time object detection. The model performance indicators are the following:  
* Precision quantifies the ratio of true positive (TP) predictions to the overall number of positive predictions made by the model, which includes both TP and false positive (FP) predictions. In this context, TP refers to the characteristics and details that enable the model to accurately identify the positive class and successfully predict a positive outcome. This metric evaluates the model's ability to distinguish relevant information from irrelevant data. For example, in the PET recycling model, if the model predicts that a bottle is a PET and the object is a PET, that is considered a TP. Precision is computed using Equation (4). \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\;[\%]\] (4)
* Recall measures the ratio of TP predictions to the total number of actual positive characteristics in the dataset. It indicates how effectively the model identifies positive outcomes among all the relevant factors. A high recall value indicates that the model accurately captures most of the actual positives, which is crucial in scenarios where a missing positive characteristic could have significant consequences. A recall of 100% means that the model successfully identified all instances of the label \"PET\" present in the training images. This ideal outcome indicates that there were no false negatives (FNs), ensuring that the model did not overlook any relevant objects. An FN arises when a model inaccurately predicts a negative outcome for an instance that is positive. In other words, it is a type of error according to which the model fails to identify a positive case and instead classifies it as negative. In a recycling context designed to detect PET objects, an FN occurs when the system predicts that an object is non-PET (negative), but in reality, the object is PET (positive).