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

TinaFace: Strong but Simple Baseline for Face Detection