  Mechanical exfoliation of graphene and its identification by optical
inspection is one of the milestones in condensed matter physics that sparked
the field of 2D materials. Finding regions of interest from the entire sample
space and identification of layer number is a routine task potentially amenable
to automatization. We propose supervised pixel-wise classification methods
showing a high performance even with a small number of training image datasets
that require short computational time without GPU. We introduce four different
tree-based machine learning algorithms -- decision tree, random forest, extreme
gradient boost, and light gradient boosting machine. We train them with five
optical microscopy images of graphene, and evaluate their performances with
multiple metrics and indices. We also discuss combinatorial machine learning
models between the three single classifiers and assess their performances in
identification and reliability. The code developed in this paper is open to the
public and will be released at github.com/gjung-group/Graphene_segmentation.
