  Creating Computer Vision (CV) models remains a complex practice, despite
their ubiquity. Access to data, the requirement for ML expertise, and model
opacity are just a few points of complexity that limit the ability of end-users
to build, inspect, and improve these models. Interactive ML perspectives have
helped address some of these issues by considering a teacher in the loop where
planning, teaching, and evaluating tasks take place. We present and evaluate
two interactive visualizations in the context of Sprite, a system for creating
CV classification and detection models for images originating from videos. We
study how these visualizations help Sprite's users identify (evaluate) and
select (plan) images where a model is struggling and can lead to improved
performance, compared to a baseline condition where users used a query
language. We found that users who had used the visualizations found more images
across a wider set of potential types of model errors.
