  In this paper, we present Generic Object Detection (GenOD), one of the
largest object detection systems deployed to a web-scale general visual search
engine that can detect over 900 categories for all Microsoft Bing Visual Search
queries in near real-time. It acts as a fundamental visual query understanding
service that provides object-centric information and shows gains in multiple
production scenarios, improving upon domain-specific models. We discuss the
challenges of collecting data, training, deploying and updating such a
large-scale object detection model with multiple dependencies. We discuss a
data collection pipeline that reduces per-bounding box labeling cost by 81.5%
and latency by 61.2% while improving on annotation quality. We show that GenOD
can improve weighted average precision by over 20% compared to multiple
domain-specific models. We also improve the model update agility by nearly 2
times with the proposed disjoint detector training compared to joint
fine-tuning. Finally we demonstrate how GenOD benefits visual search
applications by significantly improving object-level search relevance by 54.9%
and user engagement by 59.9%.
