▎ 摘　　要

NOVELTY - The method involves forming a micrograph i.e. visible optical micrograph, of a surface. The micrograph is classified into one of a set of categories, where the set of categories includes a first category comprising micrographs including a monolayer flake, and a second category, consisting of micrographs including bilayer flakes, the first category includes a third category, consisting of micrographs including an oligolayer flake and a fourth category, a neural network is formed as a residual neural network and convolutional neural network and the neural network is a neural network selected from a group consisting of ResNet18, ResNet152, ResNet101, ResNet50, and ResNet34. USE - Method for classifying images for forming oligolayer flakes of a substrate. Uses include but are not limited to monolayer or bilayer flakes such as graphene or graphene material, information processing systems, transson junctions and sensitive bolometers. ADVANTAGE - The method enables effectively assessing performance of candidate process for forming thin flakes of material. DETAILED DESCRIPTION - INDEPENDENT CLAIMS are included for: (1) a method for classifying exfoliation techniques; (2) a system for classifying attempts to form oligolayer flakes. DESCRIPTION OF DRAWING(S) - The drawing shows a schematic view of a hybrid schematic process micrograph chart.