Automated fashion product attribution: A case study in using custom vision services vs. manually developed machine learning models
Many providers offer AI-based vision callabilities that perform common tasks like facial recognition, object detection, and text extraction; but for more specialized computer vision applications, companies have historically had to build their own machine learning models from scratch. Training, testing and deploying these models requires machine learning and software engineering expertise that many companies do not possess. In the last few years, custom vision services have become available that promise to democratize computer vision by automating the creation, evaluation, and deployment of these models. Google AutoML Vision, Microsoft Azure Custom Vision Service, Clarifai Custom Models, and IBM Watson Visual Recognition Custom Models all offer such services. In this talk, we will present a case study in using custom vision services to automate the attribution of fashion products, e.g. dress neckline, length, pattern. We will discuss the benefits and challenges associated with using custom vision services, and compare performance of these services to our own custom-built models.
Tom Szumowski is a Data Scientist at URBN, a portfolio of global consumer brands comprised of Urban Outfitters, Anthropologie, Free People, BHLDN, Terrain and the Vetri Family with total annual sales over 3.5 Billion dollars. Tom’s work seeks to applying machine learning algorithms to drive business value in a wide range of applications, from logistics optimization and fraud detection to product recommendations and personalization. Prior to joining URBN, Tom spent eleven years at Lockheed Martin, where he developed conventional and ML-based algorithms for various military applications. Tom holds a B.S. from Rutgers University and a M.S. from University of Pennsylvania, both in electrical engineering.