YOLO5Face: Why Reinventing a Face Detector

• URL: http://arxiv.org/abs/2105.12931v1
• Date: Thu, 27 May 2021 03:54:38 GMT
• Title: YOLO5Face: Why Reinventing a Face Detector
• Authors: Delong Qi, Weijun Tan, Qi Yao, Jingfeng Liu
• Abstract summary: We implement a face detector based on YOLOv5 object detector and call it YOLO5Face. We design detectors with different model sizes to achieve the best performance. Experiment results on the WiderFace dataset show that our face detectors can achieve state-of-the-art performance.
• Score: 1.3272510644778104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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