The problem of image segmentation is a challenging task that involves assigning each pixel in an image a class label, taking into account the context and interactions between adjacent pixels. The state-of-the-art model for this task is conditional random fields (CRFs), which uses unary and pairwise terms to penalize misclassifications and incoherent labeling. However, convolutional neural networks (CNNs) have recently shown promising results in image segmentation due to their Markovian nature, allowing them to learn important features and local interactions between these features. The U-Net architecture is a variant of the fully convolutional network that uses deconvolution to up-sample the output, which allows for more accurate object localization. Deconvolution can be used to avoid the checkerboard artifact that occurs when using filter sizes not divisible by the stride. The U-Net architecture has been widely studied and has gained success in applications beyond image segmentation, including image-to-image translation tasks.