## 4 Results and Discussion  
This section provides a description of the experimental results, their interpretation, and the experimental conclusions. Please refer to Figure 7 for the methodology workflow for training, evaluation, and testing. In the CNN architectures, the classification models were trained and evaluated using fivefold cross-validation for each object. The distribution of data used to train the objects is shown in Figure 4.  
The CNN architectures evaluated were ResNet50, ResNet152, VGG16, VGG19, InceptionV3, EfficientNetV2B2, EfficientNetV2L, EfficientNetV2S, EfficientNetV2M, and Effi  
Figure 6: Data used for testing each object type (member, screen, property, map, hat, and pin). The blue color represents the number of images that are not defined as containing the specific object, while the pink color represents the number of images that were defined as containing it. Figure 7: A methodology workflow for training, evaluation, and testing of the CNN architectures, YOLO, and BLIP models. cientNetB0. For each object, Table 2 shows the best model based on the highest weighted F-score among all the architectures. Detailed results from the 10 CNN models on the validation set are presented in Appendix A in Table A1. Figure 8 illustrates the accuracy of BLIP, YOLO, and CNN (the top CNN models are detailed in Table 1), while their F-scores are depicted in Figure 9. Our findings align with those in the literature, indicating that EfficientNet and its variants, along with ResNet, outperform VGG16 and VGG19 on the ImageNet validation dataset [56; 64; 65]. The YOLOv5 and the BLIP model were tested on validation data, as presented in Figures 4 and 5. A comparison of the accuracy of the three models can be seen in Figure 8. \begin{table}
\begin{tabular}{c c} \hline \hline
**Object** & **CNN Model** \\ \hline Hat & ResNet152 \\ Pin & ResNet152 \\ Map & EfficientNetV2S \\ Property & EfficientNetV2B2 \\ Screen & ResNe50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of best CNN models based on testing data for each object type. Figure 8: An evaluation of the accuracy of the CNN, YOLOv5, and BLIP models based on the validation data. Figure 9: An evaluation of the F-scores of the CNN, YOLOv5, and BLIP models based on the validation data. For most of the objects--property, pin, hat, map, and screen--the CNN model is more accurate than the other two. In the case of the member objects, the BLIP model showed better performance. In Figure 9, there is a greater difference between the F-scores of the models, with the YOLOv5 exhibiting the lowest performance. According to the validation data, CNN models performed best for most objects. Therefore, we used it to classify all 252,628 images in the organization's Instagram dataset. Figure 10 indicates the number of occurrences of each object in our dataset. Most images did not contain the censored objects the organization had selected. Most of the objects found in the images were screens, members, and properties. From the 252,628 images classified by each of our models, 2007 random images were selected and manually assessed for the presence of each object. This was carried out to determine whether the models were successful at predicting new real-world data. The number of images classified manually as existing or not existing in the testing set is shown Figure 6. Table 3 presents the results of the best models on this test set. This table summarizes the accuracy, the F-score, and the weighted F-score received by each CNN object model. It can be observed that the measures for objects such as property and member were relatively high; however, for the test set for low-frequency objects such as hat, map, and pin, the measures were relatively low. For example, there was only one image with a pin that was not identified, resulting in a zero F-score. In addition to having a low frequency in the test set, the objects that received lower values were generally smaller. Member and property objects are larger than pins, hats, and screens.