  With the rapid development of deep convolutional neural network, face
detection has made great progress in recent years. WIDER FACE dataset, as a
main benchmark, contributes greatly to this area. A large amount of methods
have been put forward where PyramidBox designs an effective data augmentation
strategy (Data-anchor-sampling) and context-based module for face detector. In
this report, we improve each part to further boost the performance, including
Balanced-data-anchor-sampling, Dual-PyramidAnchors and Dense Context Module.
Specifically, Balanced-data-anchor-sampling obtains more uniform sampling of
faces with different sizes. Dual-PyramidAnchors facilitate feature learning by
introducing progressive anchor loss. Dense Context Module with dense connection
not only enlarges receptive filed, but also passes information efficiently.
Integrating these techniques, PyramidBox++ is constructed and achieves
state-of-the-art performance in hard set.
