# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 3 Methodology
### The Architecture of Pretrained Models
* A 4 \(\times\) 4 with a stride of four non-overlapping convolution \"patchify stem\" is used in the ConvNext architecture. Since the kernel size and stride size are the same, the patches do not overlap. * The ConvNeXt architecture incorporates an inverted bottleneck block design, which shares similarities with existing models but introduces layer normalization and Gaussian Error Linear Units (GELU) activation as additional components. * In ConvNeXt, a \(7\times 7\) with an increased kernel size depthwise convolutional layer is used in each block. Rearrange the order of the layers in the block such that the depthwise convolutional layer is positioned as the first layer. * The number of activation functions and normalization layers are reduced in the ConvneXt architecture. The Rectified Linear Units (ReLU) activation function is replaced with the GELU activation function. The model uses a single GELU before a 1 \(\times\) 1 convolutional layer in each block. Batch normalization (BN) has been replaced by layer normalization (LN). * ConvNeXt uses separate downsampling layers between stages. These layers have layer normalization and a 2 \(\times\) 2 convolutional layer with a stride of 2. Several crucial components that contribute to the observed performance disparity are identified during the course of their investigation. The ConvNeXt architecture and its blocks can be seen in Figures 5 and 6, respectively. The general structure of a model used in the study is shown in Figure 7. The pretrained model is loaded into the system with weights but without the classification layers. This pretrained model will be used as the feature extractor. As mentioned before, all the models used here have previously produced very successful results in image classification and their feature extraction power has been proven. But the top layers of these models are suitable for the problem they were trained for before. For this reason, the top layers are not  
Figure 5: Architecture of the ConvNeXt model. included. The output of the last layer before the fully connected layer is used as the input for our classifier. All the layers are frozen in order to prevent weight changes in the models with the new data. If the weights in these layers change, there is no difference compared to training the model from scratch. After this phase, fully connected layers suitable for our problem are appended. As mentioned above, six different pretrained models, which are described in general terms, VGG16, InceptionV3, Resnet50, MobileNet, DenseNet, and ConvNeXt, are used in this study. The input size of all the pretrained models was set up as 256 \(\times\) 256. As can be seen in Figure 7, 1,024,512, 256,128, and 20-neuron fully connected layers are employed, respectively. All fully connected layers except the final layer include ReLU activation functions. On the other hand, the output layer has a Sigmoid activation function which is a necessity for multilabel classification problems. Also, batch normalization layers have been added between the fully connected layer to handle the overfitting problem.