Combining the three vision-driven autocharacterization processes developed in this paper accelerates the identification of perovskite semiconductors with desirable properties relative to conventional human-evaluated characterization. Using the segmented optical data, Φ, of each sample composition, band gap, and stability are all computed automatically. This combination is a sufficient number of characterizations for a pipeline that initially screens perovskite materials properties, which we demonstrate can be implemented fully via computer vision techniques. In this section, we visualize the characterization results output by each automated process and benchmark them against human evaluation.