  Most state-of-the-art object detection systems follow an anchor-based
diagram. Anchor boxes are densely proposed over the images and the network is
trained to predict the boxes position offset as well as the classification
confidence. Existing systems pre-define anchor box shapes and sizes and ad-hoc
heuristic adjustments are used to define the anchor configurations. However,
this might be sub-optimal or even wrong when a new dataset or a new model is
adopted. In this paper, we study the problem of automatically optimizing anchor
boxes for object detection. We first demonstrate that the number of anchors,
anchor scales and ratios are crucial factors for a reliable object detection
system. By carefully analyzing the existing bounding box patterns on the
feature hierarchy, we design a flexible and tight hyper-parameter space for
anchor configurations. Then we propose a novel hyper-parameter optimization
method named AABO to determine more appropriate anchor boxes for a certain
dataset, in which Bayesian Optimization and subsampling method are combined to
achieve precise and efficient anchor configuration optimization. Experiments
demonstrate the effectiveness of our proposed method on different detectors and
datasets, e.g. achieving around 2.4% mAP improvement on COCO, 1.6% on ADE and
1.5% on VG, and the optimal anchors can bring 1.4% to 2.4% mAP improvement on
SOTA detectors by only optimizing anchor configurations, e.g. boosting Mask
RCNN from 40.3% to 42.3%, and HTC detector from 46.8% to 48.2%.
