Face detection has received intensive attention in recent years. Many works
present lots of special methods for face detection from different perspectives
like model architecture, data augmentation, label assignment and etc., which
make the overall algorithm and system become more and more complex. In this
paper, we point out that \textbf{there is no gap between face detection and
generic object detection}. Then we provide a strong but simple baseline method
to deal with face detection named TinaFace. We use ResNet-50 \cite{he2016deep}
as backbone, and all modules and techniques in TinaFace are constructed on
existing modules, easily implemented and based on generic object detection. On
the hard test set of the most popular and challenging face detection benchmark
WIDER FACE \cite{yang2016wider}, with single-model and single-scale, our
TinaFace achieves 92.1\% average precision (AP), which exceeds most of the
recent face detectors with larger backbone. And after using test time
augmentation (TTA), our TinaFace outperforms the current state-of-the-art
method and achieves 92.4\% AP. The code will be available at
https://github.com/Media-Smart/vedadet.

该研究使用现有的模块和通用物体检测模型构建 TinaFace，实现人脸检测。TinaFace 在 WIDER FACE 挑战赛上实现了 92.1% 的精度。使用测试时增强后，TinaFace 的结果超过了当前最先进的方法，达到 92.4% 的平均精度（AP）。