# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 4 Experimental Results and Discussion
InceptionV3 is another model that reached over 90% accuracy. The weakest performance among the models is demonstrated by MobileNet and ResNet, yet the score is above 89%. Figure 11: Fine-tuning loss of ConvNeXt and DenseNet models over the epochs. Figure 10: Fine-tuning accuracy of ConvNeXt and DenseNet models over the epochs. The pretrained models produced a different range of loss results in this study. The ConvNeXt model is the overachieving one among the pretrained models with a 25.98% score. Also, the loss score of DenseNet is close to the ConvNeXt model. VGG16 produced a less than 27% loss score. While InceptionV3 displayed a 29.12% loss score, the most dramatic score in the table was generated by MobileNet with 67.06%. The reason for this poor loss result for MobileNet arises from the fact that the input shape of 256 \(\times\) 256 is not suitable for it. Regarding precision, the models generated scores about between 30% and 36%. DenseNet, again, generated the best results with 35.85%. VGG16 was the second-best model regarding precision. The ConvNeXt model generated a 33.1% precision score which is slightly less than the score of VGG16. The performance of DenseNet is again the best one regarding the Hamming loss with 9.89%. ConvNeXt demonstrated the third-best result after the VGG16 model. InceptionV3 is one of the models that produced less than a 10% Hamming loss. MobileNet and ResNet displayed similar results for this metric. Table 2 also provides the F1-scores in which DenseNet demonstrated the best result with a score over 43% which is the best score by far. ConvNeXt and VGG16 reached a score over 40%. The weakest performance was produced by ResNet. The last metric on the table is the AUC which also supports the dominance of DenseNet. ConvNeXt is the second model which reached an AUC score above 75%. ResNet demonstrated the weakest performance regarding this metric. The confusion matrix of the novel model for this problem, ConvNeXt, can be seen in Figure 12. The multilabel nature of the problem makes it difficult to show in a conventional confusion matrix. Yet, it can be demonstrated in the way of the corresponding figure. The number indicated as 0 indicates all labels except the label present in that sub-figure. The numbers up to 19 represent the label present in the sub-figure. In the figure below, the drama and comedy labels draw attention. A total of 1426 comedy-labeled and 2537 drama-labeled movies were correctly classified. On the other hand, history, sport, music, and war-labeled movies were not predicted well which is caused by the few numbers of movies in the aforenamed genres. The comparison of the genre prediction of ConvNeXt, with the actual genre of the same movie on IMDB, is shown in Figure 13 below. As can be seen in the figure, three posters from movies that aired in 2022 have been used for the test and the model demonstrates satisfactory results. The Top Gun Maverick movie has two labels, namely action and drama which are in the top three predictions of the model. The Batman movie, on the other hand, has three labels. The ConvNeXt model correctly predicted the action label with 89%. However, the second predicted label does not exist in the labels for the movie. Drama is predicted as the fourth possible label. The last movie in the figure is Senior Year which has comedy and drama labels which are correctly predicted by the model.