## 3 The U2-Net Architecture
The contracting path catches the information around the objects to offer a more accurate representation of the object, which is the reason behind this design. The U-Net design serves as the foundation for our U2-net. Figure 1 shows the architecture design of the U2-net model, which is created to automatically identify distracted drivers using input image. In the U2-net model, we modify the unique U-net architecture by adding four convolution layers and two max pooling coatings to the contracting pathway. In addition, in the escalating path, we add four up-sampling (transpose convolution) layers and two max pooling layers. These new convolution layers add more spatial information and context to the classifier. Additionally, we use full-color images (RGB color channels) instead of gray-scale images, as in the U-net architecture. The transpose convolution layers upsurge the tenacity of the output. To keep sound localization and reduce the time set for training, we decrease the dimension of each input picture, making them 256 \(\times\) 256 pixels in size and fodder as the input to the proposed architecture. For each input image, convolutional operations are performed to extract image features, followed by pooling operators to reduce the image dimension. These convolutions and pooling operators are performed for the successive layers to reach the minimum image dimension that is full of features. Then the transpose convolution is accomplished to up-sample the input feature map to an anticipated output feature map. After that, concatenating the layers generated from the pooling operators with layers generated from the up-sampling operators is performed to increase the resolution of the results and to help extract the prevailing features that are essential for operational arrangement and exposure. A significant array of feature systems enable the web to transmit contextual data to more excellent firmness layers during the up-sampling process. The U2-net design, such as the U-net architecture, has both a contracting route (Figure 1: left side) and an expanding path (Figure 1: right side). The narrowing path is measured in the manner of a convolutional system. It comprises two 3 \(\times\) 3 convolution layers applied repeatedly and one max pooling process. Each convolution employs the same Rectified Linear Unit (ReLU) and buffering. The stride of 2 is used for down-sampling using the identical buffer in the 2 \(\times\) 2 max pooling procedure. The U2-net architecture takes a set of images as input to generate a list of probabilities based on the number of classes as an output. Each image from each dataset represents an input to the contracting operation. The contracting operation begins with 16 filters (feature channels) and ends with 1024 filters, while the expansive operation begins with 1024 filters and ends with 16 filters. At each step in the contracting path, we drop 10% to avoid model overfitting and we double the number of feature channels to reach 1024 feature channels. Figure 1: The architecture for the U2-net model, starting from the input image (**left side**) to the output classifier (**right side**). Figure 2 provides more details about these layers. The feature map's up-sampling (transpose convolutions) is the first stage in the expanding path. This is monitored by a 2 \(\times\) 2 convolution, which cuts the number of feature channels in half, a concatenation with the corresponding feature map from the contracting path, and two 3 \(\times\) 3 convolutions, each followed by ReLU activation function with the stride of 1 and with the same padding. In addition, we drop 10% each time to avoid model overfitting. A successive dense layer is used to gather a correct output, enhancing the accuracy. In total, the U2-net architecture has 1 input layer, 32 convolutional layers, 6 max-pooling layers, 6 transpose convolution layers, 6 concatenating operations, 12 dropout layers, and 1 dense layer, as follows and shown in Figure 2.  
1. _Input layer_. Each image from the dataset represents an input to the U2-model.