Accepted Papers

GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

Sean Bell: Facebook; Yiqun Liu: Facebook; Sami Alsheikh: Facebook; Yina Tang: Facebook; Ed Pizzi: Facebook; Michael Henning: Facebook; Karun Singh: Facebook; Omkar Parkhi: Facebook; Fedor Borisyuk: Facebook


In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.

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