## 5 Discussion  
This research analyses two object identification models and their classification into cardboard, glass, plastic, and metal categories. The biggest issue with these classifications is the distinction between glass and plastic. The research shows that although the ACVS model has high accuracy on the trained data, it demonstrates a value of 95.13% in real-time applications, which means 5% of objects are deployed in the wrong compartment. The paper also analyses the issue of the number of images used in training the model. From the tests, it is found that an increase in the number of images for a specific waste category can destabilize the model's accuracy to the detriment of others. Also, from the tests, it was found that some classes, such as glass and plastic, share common characteristics that make it impossible to distinguish between the two classes. Moreover, adding additional images for these two categories does not solve the problem, as the model becomes even more confused in the distinction, becoming overestimated. Conversely, using too few images will cause the model to be undertrained. A second model, the CWSM, which had an accuracy of 96.25% before the proposed _WCCS_, has demonstrated real-time usability by increasing the accuracy to 99.75% after applying the indicator. The CWSM is designed through descriptive identification of the object in the image, and then the category is identified through a second model. Initially, the description is obtained using the GVAS model, which analyses the object at the image processing level. Then, the CWSM is used, a custom model based on predicates and thus text-level processing. The algorithm used by the CWSM is LbfgsMaximumEntropyMulti, which achieved an accuracy of 92.79% during training. Furthermore, the model is evaluated through a unique tool specially designed for this type of classification, specifically for this research. This indicator, the _WCCS_, varies between 84.2% and 100%. A value of 84.2% indicates a certain classification in a specific category, with no doubts regarding allocating to the class. On the opposite side, a value of 100% indicates an uncertain capacity of the CWSM. RBin uses the CWSM in residential waste sorting. Several studies have been analyzed in the specialized literature that aim to classify waste automatically. The paper by [PERSON] and [PERSON] [62] presents the WaDaBa model for the classification of plastic waste, achieving an accuracy of 87.44%. The paper by [PERSON] and [PERSON] [60] presents the TrashNet prototype, which achieved an accuracy of over 90%. In comparison with these models, the CWSM offers an innovative approach through the use of text-based labels. Classification algorithms enabled an accuracy of 99.75% to be reached. This value demonstrates, on the one hand, the superiority of the developed algorithm and, on the other hand, the feasibility of using textual descriptions instead of direct image-based training. The innovative aspect of this research lies in demonstrating the use of text descriptions extracted from images, as opposed to training directly on images. The TrashNet model used 2527 images, the WaDaBa model used 4000 images, and the CWSM used only 1000 images due to the superiority of the text extraction services. Using this reduced dataset in relation to the model's high performance highlights the advantage of this approach. The research highlights the importance of developing a custom model based on the description of the identified object. For the data engineer, using models that utilize unknown features makes pattern management unpredictable. The paper outlines that the model based on ACVS cannot be used in practice. At the same time, GVAS was used to build the customized model further, representing a successful formula. Also, this research underlines that the theory stating that 75% of the data is used for training and the remaining 25% for model validation does not demonstrate the possibility of practical use.