  The recent growth in data volumes produced by modern electron microscopes
requires rapid, scalable, and flexible approaches to image segmentation and
analysis. Few-shot machine learning, which can richly classify images from a
handful of user-provided examples, is a promising route to high-throughput
analysis. However, current command-line implementations of such approaches can
be slow and unintuitive to use, lacking the real-time feedback necessary to
perform effective classification. Here we report on the development of a
Python-based graphical user interface that enables end users to easily conduct
and visualize the output of few-shot learning models. This interface is
lightweight and can be hosted locally or on the web, providing the opportunity
to reproducibly conduct, share, and crowd-source few-shot analyses.
