A billion-dollar collectibles company seeks to augment their triage process of identifying rare coins. The objective was to shift time team spends inspecting low value coins to high value coins, speeding up the process and increasing revenue by expanding the markets they can address.
We trained a machine vision algorithm (CNN) on a subset of 500,000 images of rare coins. We built a prototype application where users upload images of rare coins for identification.
In two weeks of development, our machine vision application could identify a subset of rare coins with over 90% accuracy with a roadmap to higher accuracy and multiple business use cases for deployment.
A billion-dollar collectibles company seeks to augment their triage process of identifying rare coins. The objective was to shift time team spends inspecting low value coins to high value coins, speeding up the process and increasing revenue by expanding the markets they can address.
We trained a machine vision algorithm (CNN) on a subset of 500,000 images of rare coins. We built a prototype application where users upload images of rare coins for identification.
In two weeks of development, our machine vision application could identify a subset of rare coins with over 90% accuracy with a roadmap to higher accuracy and multiple business use cases for deployment.
