  Two-dimensional materials are a class of atomically thin materials with
assorted electronic and quantum properties. Accurate identification of layer
thickness, especially for a single monolayer, is crucial for their
characterization. This characterization process, however, is often
time-consuming, requiring highly skilled researchers and expensive equipment
like atomic force microscopy. This project aims to streamline the
identification process by using machine learning to analyze optical images and
quickly determine layer thickness. In this paper, we evaluate the performance
of three machine learning models -- SegNet, 1D U-Net, and 2D U-Net -- in
accurately identifying monolayers in microscopic images. Additionally, we
explore labeling and image processing techniques to determine the most
effective method for identifying layer thickness in this class of materials.
