## 4 Results  
The models are implemented for evaluation using the C# programming language. The same training and testing images were used to ensure fairness in the comparative analysis of the two proposed models, ACVS and the CWSM. This way, the obtained results can be compared without discriminating against one of the methods. The training dataset includes 1000 images, and the testing dataset has 400 images, uniformly distributed among the four analyzed categories. This size allows for evaluating the models' ability to classify objects correctly. Increasing the number of training images is not a solution regarding the generalization of a model. The model's performance should come precisely from its ability to extract a category's basic features and generalize for all objects in that class. From this context, the problem of waste classification also arises. It is very difficult to build a datasetcontaining all existing objects in reality, and these objects must be subjected to all possible contexts of angle, lighting, and quality contexts. Building such a dataset is a utopic ideal.  
The dataset was constructed for training and validating the models by balancing the distribution across the four waste categories: plastic, glass, cardboard, and metal. The images used to build the dataset were collected from multiple sources to ensure the variability needed for the model to generalize to new objects. These sources include the following:  
* Public databases with images of recyclable objects [59];
* Images captured using a mobile phone, including damaged or deformed objects, to mimic real-life scenarios;
* Images captured using the camera of the proposed prototype.  
The dataset includes images from different angles and under various lighting conditions (natural, artificial, low lighting, partial shading). This strategy allows for assessing the generalization capability of the two models in a comparative analysis. In this way, the models are not trained exclusively on a limited type of image and can make predictions about unknown objects.  
Figure 8 presents a statistical demonstration of the training dataset obtained after applying GVAS. In this figure, the unique words that ensure differentiation between categories have been retained. This demonstration ensures the CWSM's ability to distinguish between categories correctly. Figure 9 shows the unique words identified for each category in the validation set that are also found in the training set. The authors note that the training set may also contain words not found in the training set, but they rely on the description containing other words that allow differentiation, so it does not rely exclusively on that word. In Figure 8, statistics were calculated regarding the uniqueness of words that allow differentiation between categories in the analysis of descriptions generated by GVAS for the 1000 images. This GVAS model ensures the textual extraction of image descriptions from the training set of 1000 elements. To visualize this statistic in Figure 8, common descriptive elements identified in two or more categories were removed. For example, the word transparency is recognized in the plastic and glass categories. The word's presence in both categories causes the model to become confused in the second training layer. However, unique elements ensure a clear distinction between categories, alongside these common elements that irrevocably exclude other classes. Narrowing the domain of classes and subsequently firmly distinguishing them through distinctive elements allows the model to identify the class to which an object belongs accurately.  
Figure 8: The CWSM uses unique word stats to differentiate among categories in the training process.