  Along with current multi-scale based detectors, Feature Aggregation and
Enhancement (FAE) modules have shown superior performance gains for
cutting-edge object detection. However, these hand-crafted FAE modules show
inconsistent improvements on face detection, which is mainly due to the
significant distribution difference between its training and applying corpus,
COCO vs. WIDER Face. To tackle this problem, we essentially analyse the effect
of data distribution, and consequently propose to search an effective FAE
architecture, termed AutoFAE by a differentiable architecture search, which
outperforms all existing FAE modules in face detection with a considerable
margin. Upon the found AutoFAE and existing backbones, a supernet is further
built and trained, which automatically obtains a family of detectors under the
different complexity constraints. Extensive experiments conducted on popular
benchmarks, WIDER Face and FDDB, demonstrate the state-of-the-art
performance-efficiency trade-off for the proposed automatic and scalable face
detector (ASFD) family. In particular, our strong ASFD-D6 outperforms the best
competitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweight
ASFD-D0 costs about 3.1 ms, more than 320 FPS, on the V100 GPU with
VGA-resolution images.
