Tremendous progress has been made on face detection in recent years using convolutional neural networks. While many face detectors use designs designated for the detection of face, we treat face detection as a general object detection task. We implement a face detector based on YOLOv5 object detector and call it YOLO5Face. We add a five-point landmark regression head into it and use the Wing loss function. We design detectors with different model sizes, from a large model to achieve the best performance, to a super small model for real-time detection on an embedded or mobile device. Experiment results on the WiderFace dataset show that our face detectors can achieve state-of-the-art performance in almost all the Easy, Medium, and Hard subsets, exceeding the more complex designated face detectors. The code is available at \url{https://www.github.com/deepcam-cn/yolov5-face}.

翻译：近些年来,在使用进化神经网络进行面部检测方面取得了巨大进展。许多面部探测器使用指定用于检测脸部的设计,但我们将面部检测作为一般物体检测任务处理。我们根据YOLOv5天体检测器实施面部检测器,并称之为YOLO5天体。我们在其中添加了一个五点标志性回归头,并使用翅膀丢失功能。我们设计了不同型号的探测器,从一个大模型到最佳性能,到一个嵌入或移动设备实时检测的超级小模型。大面部数据集的实验结果显示,我们的面部探测器在几乎所有易用、中、硬分集都能达到最先进的性能,超过更复杂的指定面部探测器。该代码可在以下https://www.github.com/deepcam-cn/yolov5-face}查阅。