TinaFace: Strong but Simple Baseline for Face Detection
This work provides a strong and simple baseline for researchers and practitioners in face detection, simplifying the approach by showing no inherent gap with generic object detection.
This paper demonstrates that face detection can be effectively addressed using generic object detection techniques, achieving 92.1% AP on the WIDER FACE hard test set with a single-model and single-scale setup, and 92.4% AP with test time augmentation, surpassing previous state-of-the-art methods.
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 \url{https://github.com/Media-Smart/vedadet}.