# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 3 Methodology
### The Architecture of Pretrained Models
The model integrates auxiliary classifiers at intermediate layers to mitigate the vanishing gradient problem during training. It employs factorization to reduce the computational cost by substituting large convolutions with a combination of smaller convolutions. Batch normalization is utilized by InceptionV3 to stabilize and expedite the training process. The model completed the ILSVRC'15 competition with a top five error rate of 3.58. The DenseNet architecture, in which each layer in the network is connected with all other layers, is presented as a model that allows for the reuse of features, has fewer parameters, and is easy to train. For each layer in the network, the feature maps of the previous layers are used as the input and the combined feature maps are sent as the input to the next layer. Combining feature maps increases the diversity and efficiency of the inputs. The architecture generally consists of dense block, transition, and classification layers. In a dense block layer, a layer is directly connected to all subsequent layers. This is to improve the flow of information between layers. DenseNet employs bottleneck layers within dense blocks in order to reduce computational complexity. Typically, a bottleneck layer consists of a 1 \(\times\) 1 convolution followed by a 3 \(\times\) 3 convolution. Transition layer is the name given to the layers between two dense layers. This layer, which has a 1 \(\times\) 1 convolutional layer and 2 \(\times\) 2 avg pooling layer, changes the size of the feature map and reduces the spatial dimensions. The DenseNet architecture incorporates a hyperparameter referred to as the \"growth rate,\" which governs the quantity of additional feature maps generated by each layer. When the architecture was evaluated using four benchmark datasets, it was seen that it required a higher performance and fewer parameters than the previous architectures. The MobileNet architecture is a model designed for mobile and embedded vision applications, which has proven successful in many applications in the field of computer vision. MobileNets take advantage of depthwise separable convolutions to build smaller and accelerate deep neural networks. A depthwise separable convolution consists of depthwise convolution and pointwise convolution. The depthwise convolution applies a single convolutional filter to each input channel individually. When compared to conventional convolutions, in which each input channel is combined with all of the filters, the computational cost of this method is greatly reduced. The pointwise convolution operation involves the application of a 1 \(\times\) 1 convolution to the output obtained from the depthwise convolution. It allows the model to capture complex spatial patterns by mixing and combining the channels. The model consists of a total of 28 layers, including depthwise and pointwise. After each layer, batch normalization and ReLU layer were used. The network is completed with a fully connected and softmax layer. The ConvNeXt model which is proposed by [36] exhibits enhanced accuracy, performance, and scalability compared to vision transformers, while retaining the design simplicity characteristics of convolutional neural networks. The effectiveness of conventional ConvNets is preserved by ConvNeXt, which also features a fully convolutional nature for both training and testing, making it very easy to put into practice. The group that created ConvNeXt progressively modernized the ResNet architecture in stages so that it can accommodate the building of a hierarchical vision transformer. Adjustments made with the aim of modernizing are grouped under the headings of macro design, ResNeXt, inverted bottleneck, large kernel size, and micro design. The details of the modernizing ResNet to ConvNeXt adjustments are as follows:  
* The compute ratio of ConvNext is set to (3:3:9:3). The ConvNext architecture consists of four stages. Stage one, two, and four consist of three blocks and stage three consists of nine blocks.