In the modern-day exploration of novel materials, synthesis is no longer the bottleneck process due to the paradigm shift towards autonomous and self-driving laboratories . For example, in this paper, we synthesize 80 unique perovskite semiconductor materials along the FA 1-x MA x PbI 3 sequence in only 16.5 seconds. Instead, the bottleneck for novel material discovery is now the manual characterization of each sample individually . Although strides have been made with some autocharacterization methods of materials, there is a lack of scalability to these methods to arbitrarily many samples, as produced by high-throughput synthesis hardware, thus, inhibiting the throughput of materials discovery . Integration of computer vision in tandem with autocharacterization has the promise of improving this throughput. However, conventional computer vision approaches for materials-specific tasks have been used predominately for morphological analyses in combination with specialized microscopy methods . In this paper, we proposed the design of three autocharacterization tools to compute (i) composition, (ii) band gap, and (iii) stability of perovskite semiconductors and integrate each of these tools with computer vision segmentation of optical reflectance data to further automate and accelerate the characterization pipeline.