  Although tremendous strides have been made in uncontrolled face detection,
efficient face detection with a low computation cost as well as high precision
remains an open challenge. In this paper, we point out that training data
sampling and computation distribution strategies are the keys to efficient and
accurate face detection. Motivated by these observations, we introduce two
simple but effective methods (1) Sample Redistribution (SR), which augments
training samples for the most needed stages, based on the statistics of
benchmark datasets; and (2) Computation Redistribution (CR), which reallocates
the computation between the backbone, neck and head of the model, based on a
meticulously defined search methodology. Extensive experiments conducted on
WIDER FACE demonstrate the state-of-the-art efficiency-accuracy trade-off for
the proposed \scrfd family across a wide range of compute regimes. In
particular, \scrfdf{34} outperforms the best competitor, TinaFace, by $3.86\%$
(AP at hard set) while being more than \emph{3$\times$ faster} on GPUs with
VGA-resolution images. We also release our code to facilitate future research.
