## 3 Materials and Methods
### Azure Custom Vision Methodology  
The methodology used for category identification employs ACVS. The training process begins with collecting labeled images representing different categories. The model classifies waste, so plastic, metal, paper, and glass images are collected and labeled accordingly. The labeled images represent the entire training dataset. The dataset is uploaded to ACVS for training. The biggest problem of the dataset is finding the optimum number of images for each category. The construction of the dataset must adhere to the following rules:  
* The AI engineer must determine the number of images for each category so that the model does not overfit, meaning it does not adapt too much to the specific features of the class. Also, the engineer must ensure that the model is not undertrained, meaning there are not enough images for each category to identify the features that distinguish categories from one another;
* The data engineer must ensure the variability of each category through the data. This means that some categories require more training images if there is a wide variety of unique tuples within the class, such as plastic, which comes in many types and where objects may have different shapes;All environmental conditions must be represented by acquiring images from different angles, light types (natural and artificial), varying brightness levels, and shadows from various angles. To prevent the model from being biased toward a particular class, we impose a similar number of images for the training dataset across categories in ACVS. If one class has more images than another, the model may learn to favor that class. For this reason, we impose an equal number of images for each category. To demonstrate this statement, for ACVS, we initially built a dataset with 100 images per category. Even though a smaller number of images may be sufficient for very simple categories (like metal), we imposed the value of 100 to prevent bias. Later, in the second stage of training, ACVS is modified with a smaller number of images for the metal class and a larger number for the plastic class. In the third scenario, we employed 1000 images, 250 for each category. ACVS uses ML algorithms to train the model. The model learns to identify image patterns corresponding to the labeled categories. The training process involves using these images to teach the model to predict the category for new, unseen images. The process begins by capturing an image, which is then sent to ACVS. The service processes the image and classifies it into predefined categories based on the model trained using labeled data. The classification result is returned, indicating which category the image belongs to. If the model does not predict with the expected accuracy, it can be re-trained in a new iteration by adjusting the number of images for the discriminated category. After establishing 1000 images for each category, we validated the model's performance on a validation dataset. For testing, we used two types of validation data: one similar to the objects used in the training process and the second type consisting of completely different data from the training set but belonging to the same category. The significance of the second type of validation corresponds to the model's ability to distinguish between categories by discriminating features. The authors introduce the concept of discrimination when the model memorizes the specific objects it saw during training. The model needs to generalize distinctive features of the object. Based on these unique features, it must correctly identify new, unseen objects from the same category. By focusing on this validation, we evaluate the model's true capacity to differentiate between categories (such as identifying a plastic bottle versus a glass one) by recognizing and discriminating the key features defining each category. Suppose the model shows signs of overfitting (for example, excellent performance on the training set but poor performance on the validation set).