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GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
August 22, 2020
Abstract
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.
AUTHORS
Written by
Yiqun Liu
Sami Alsheikh
Yina Tang
Ed Pizzi
M. Henning
Karun Singh
Omkar Parkhi
Fedor Borisyuk
Publisher
KDD
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