TinaFace: Strong but Simple Baseline for Face Detection

26 Nov 2020  ·  Yanjia Zhu, Hongxiang Cai, Shuhan Zhang, Chenhao Wang, Yichao Xiong ·

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}.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Face Detection WIDER Face (Easy) TinaFace(ResNet-50) AP 0.97 # 2
Face Detection WIDER Face (Hard) TinaFace(ResNet-50) AP 0.934 # 1
Face Detection WIDER Face (Medium) TinaFace(ResNet-50) AP 0.963 # 2