Deep Learning image classifiers represent a breakthrough in image recognition and classification tasks, however their practical use in industry remains limited: A “clean” product image can be easily classified, but what about a context-dependent stock image of “a man jogging on the beach”? Is it a pair of shorts? His shoes? His watch which hardly shows up in the picture? While results can be improved by retraining and extending existing architectures, this exercise is very resource intensive (labeling, training, tuning, etc.). Shopify was tasked with mapping a catalogue of products that had highly problematic images and inconsistent metadata. In this talk Yizhar will describe their practical approach to the solution: Why they ended up using only some outputs of pre-trained image classifiers, how they combine these outputs with domain knowledge and graph theory to achieve our goals, and how this process allows them to avoid some of the typical pitfalls and expensive re-training.