Computer vision (CV) allows for capturing, processing, and analyzing visual inputs (i.e., digital images). This cost-effective and non-invasive technique can be seamlessly integrated into experimental workflows, providing spatially and temporally resolved data. Traditional CV algorithms have predominantly relied on image analysis techniques based on color, grayscale, or edge detection for visual recognition and reasoning. While these algorithms excel at extracting information from visual images, they encounter difficulties when confronted with unideal visual conditions, such as glare, unexpected solid formations, or changes in lighting. Furthermore, classical CV systems often focus on specific tasks, reducing images to one or two quantifiable variables. However, such reductionist approaches limit the "full-scene" understanding that can be achieved. Recent advancements in CV models have showcased improved utility by incorporating real-time quantification capabilities. Additionally, advancements in machine learning (ML) have facilitated the development of more flexible CV methods, overcoming the limitations associated with task-specific approaches. Convolutional neural networks (CNNs) in particular can achieve near-human classification accuracy when trained with large datasets, enabling comprehensive analysis of diverse objects. Thus far, CNNs have mainly focused on identification and classification rather than variable quantification. A CV system that combines both image analysis (i.e., quantification) and classification capabilities (via CNNs) would serve as a significant milestone toward the advancement of automated systems for chemical Research and Development (R&D).