  RetinaNet proposed Focal Loss for classification task and improved one-stage
detectors greatly. However, there is still a gap between it and two-stage
detectors. We analyze the prediction of RetinaNet and find that the
misalignment of classification and localization is the main factor. Most of
predicted boxes, whose IoU with ground-truth boxes are greater than 0.5, while
their classification scores are lower than 0.5, which shows that the
classification task still needs to be optimized. In this paper we proposed an
object confidence task for this problem, and it shares features with
classification task. This task uses IoUs between samples and ground-truth boxes
as targets, and it only uses losses of positive samples in training, which can
increase loss weight of positive samples in classification task training. Also
the joint of classification score and object confidence will be used to guide
NMS. Our method can not only improve classification task, but also ease
misalignment of classification and localization. To evaluate the effectiveness
of this method, we show our experiments on MS COCO 2017 dataset. Without
whistles and bells, our method can improve AP by 0.7% and 1.0% on COCO
validation dataset with ResNet50 and ResNet101 respectively at same training
configs, and it can achieve 38.4% AP with two times training time. Code is at:
http://github.com/chenzuge1/RetinaNet-Conf.git.
