论文标题
tinaface：强壮但简单的面部检测基线
TinaFace: Strong but Simple Baseline for Face Detection
论文作者
论文摘要
近年来，面部检测受到了密集的关注。许多作品从不同的角度出现了许多特殊方法，例如模型架构，数据增强，标签分配等，这使整个算法和系统变得越来越复杂。在本文中，我们指出\ textbf {面部检测和通用对象检测之间没有差距。然后，我们提供了一种强大但简单的基线方法来处理名为Tinaface的面部检测。我们将Resnet-50 \ Cite {He2016-Deep}用作骨干，并且在现有模块上构建了Tinaface中的所有模块和技术，易于实现并基于通用对象检测。在最受欢迎和最具挑战性的面部检测基准较宽的脸部\ cite {yang2016wider}的硬测试中，带有单模型和单尺度，我们的tinaface达到了92.1 \％的平均精度（AP），超过了大多数最近的面部探测器，具有较大的反向探测器。在使用测试时间扩展（TTA）之后，我们的tinaface优于当前的最新方法，并实现92.4 \％AP。该代码将在\ url {https://github.com/media-smart/vedadet}上提供。
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}.
