Multiple datasets and open challenges for object detection have been
introduced in recent years. To build more general and powerful object detection
systems, in this paper, we construct a new large-scale benchmark termed
BigDetection. Our goal is to simply leverage the training data from existing
datasets (LVIS, OpenImages and Object365) with carefully designed principles,
and curate a larger dataset for improved detector pre-training. Specifically,
we generate a new taxonomy which unifies the heterogeneous label spaces from
different sources. Our BigDetection dataset has 600 object categories and
contains over 3.4M training images with 36M bounding boxes. It is much larger
in multiple dimensions than previous benchmarks, which offers both
opportunities and challenges. Extensive experiments demonstrate its validity as
a new benchmark for evaluating different object detection methods, and its
effectiveness as a pre-training dataset.

本文介绍了一个新型的大规模基准数据集 BigDetection，它通过整合多个现有数据集并提供更大样本数量、更统一的标注体系，可以用于改进物体检测算法的预训练效果，并已被证明是一个有效的检测方法评估标准。