There are two methods for identifying and classifying regions of interest in images (e.g., solids, solution, residue): instance segmentation and semantic segmentation. We chose to use an instance segmentation CNN model due to its superior capability to distinguish regions of the same class (e.g., recognizing different homogenous liquid layers as would be present in liquid ̶ liquid extraction). By utilizing an instance segmentation approach, we can effectively separate the tasks of segmentation (identifying object regions) from classification (categorizing objects), thereby enabling segmentation for objects beyond the training set. To achieve this instance segmentation, we implemented a Mask R-CNN 22 architecture (the leading method) 23 using a pre-trained model (R101-FPN) from the detectron2 library. Training the model