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Along with current multi-scale based detectors, Feature Aggregation andEnhancement (FAE) modules have shown superior performance gains forcutting-edge object detection. However, these hand-crafted FAE modules showinconsistent improvements on face detection, which is mainly due to thesignificant distribution difference between its training and applying corpus,COCO vs. WIDER Face. To tackle this problem, we essentially analyse the effectof data distribution, and consequently propose to search an effective FAEarchitecture, termed AutoFAE by a differentiable architecture search, whichoutperforms all existing FAE modules in face detection with a considerablemargin. Upon the found AutoFAE and existing backbones, a supernet is furtherbuilt and trained, which automatically obtains a family of detectors under thedifferent complexity constraints. Extensive experiments conducted on popularbenchmarks, WIDER Face and FDDB, demonstrate the state-of-the-artperformance-efficiency trade-off for the proposed automatic and scalable facedetector (ASFD) family. In particular, our strong ASFD-D6 outperforms the bestcompetitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweightASFD-D0 costs about 3.1 ms, more than 320 FPS, on the V100 GPU withVGA-resolution images.

ASFD: Automatic and Scalable Face Detector