  Materials characterization remains a labor-intensive process, with a large
amount of expert time required to post-process and analyze micrographs. As a
result, machine learning has become an essential tool in materials science,
including for materials characterization. In this study, we perform an in-depth
analysis of the prediction of crystal coverage in WSe$_2$ thin film atomic
force microscopy (AFM) height maps with supervised regression and segmentation
models. Regression models were trained from scratch and through transfer
learning from a ResNet pretrained on ImageNet and MicroNet to predict monolayer
crystal coverage. Models trained from scratch outperformed those using features
extracted from pretrained models, but fine-tuning yielded the best performance,
with an impressive 0.99 $R^2$ value on a diverse set of held-out test
micrographs. Notably, features extracted from MicroNet showed significantly
better performance than those from ImageNet, but fine-tuning on ImageNet
demonstrated the reverse. As the problem is natively a segmentation task, the
segmentation models excelled in determining crystal coverage on image patches.
However, when applied to full images rather than patches, the performance of
segmentation models degraded considerably, while the regressors did not,
suggesting that regression models may be more robust to scale and dimension
changes compared to segmentation models. Our results demonstrate the efficacy
of computer vision models for automating sample characterization in 2D
materials while providing important practical considerations for their use in
the development of chalcogenide thin films.
