Most of existing object detectors usually adopt a small training batch size (e.g. 16), which severely hinders the whole community
from exploring large-scale datasets due to the extremely long training
procedure. In this paper, we propose a versatile large batch optimization framework for object detection, named LargeDet, which successfully
scales the batch size to larger than 1K for the first time. Specifically,
we present a novel Periodical Moments Decay LAMB (PMD-LAMB)
algorithm to effectively reduce the negative effects of the lagging historical gradients. Additionally, the Synchronized Batch Normalization
(SyncBN) is utilized to help fast convergence. With LargeDet, we can not
only prominently shorten the training period, but also significantly improve the detection accuracy of sparsely annotated large-scale datasets.
For instance, we can finish the training of ResNet50 FPN detector on
COCO within 12 minutes. Moreover, we achieve 12.2% mAP@0.5 absolute improvement for ResNet50 FPN on Open Images by training with
batch size 640.