  Scanning transmission electron microscopy (STEM) is an indispensable tool for
atomic-resolution structural analysis for a wide range of materials. The
conventional analysis of STEM images is an extensive hands-on process, which
limits efficient handling of high-throughput data. Here we apply a fully
convolutional network (FCN) for identification of important structural features
of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying
sulfur vacancies and polymorph types of ${MoS_2}$ from atomic resolution STEM
images. Efficient models are achieved based on training with simulated images
in the presence of different levels of noise, aberrations, and carbon
contamination. The accuracy of the FCN models toward extensive experimental
STEM images is comparable to that of careful hands-on analysis. Our work
provides a guideline on best practices to train a deep learning model for STEM
image analysis and demonstrates FCN's application for efficient processing of a
large volume of STEM data.
