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Creating Computer Vision (CV) models remains a complex practice, despitetheir ubiquity. Access to data, the requirement for ML expertise, and modelopacity are just a few points of complexity that limit the ability of end-usersto build, inspect, and improve these models. Interactive ML perspectives havehelped address some of these issues by considering a teacher in the loop whereplanning, teaching, and evaluating tasks take place. We present and evaluatetwo interactive visualizations in the context of Sprite, a system for creatingCV classification and detection models for images originating from videos. Westudy how these visualizations help Sprite's users identify (evaluate) andselect (plan) images where a model is struggling and can lead to improvedperformance, compared to a baseline condition where users used a querylanguage. We found that users who had used the visualizations found more imagesacross a wider set of potential types of model errors.

Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes