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Creating Computer Vision (CV) models remains a complex and taxing practicefor end-users to build, inspect, and improve these models. Interactive MLperspectives have helped address some of these issues by considering ateacher-in-the-loop where planning, teaching, and evaluating tasks take place.To improve the experience of end-users with various levels of ML expertise, wedesigned and evaluated two interactive visualizations in the context of Sprite,a system for creating CV classification and detection models for imagesoriginating from videos. We study how these visualizations, as part of themachine teaching loop, help users identify (evaluate) and select (plan) imageswhere a model is struggling and improve the model being trained. We found thatusers who had used the visualizations found more images across a wider set ofpotential types of model errors, as well as in assessing and contrasting theprediction behavior of one or more models, thus reducing the potential effortrequired to improve a model.

Where does a computer vision model make mistakes? Using interactive visualizations to find where and how CV models can improve