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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.

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AUTHORS

Written by

Sean Bell

Yiqun Liu

Sami Alsheikh

Yina Tang

Ed Pizzi

M. Henning

Karun Singh

Omkar Parkhi

Fedor Borisyuk

Publisher

KDD

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