Computer Science > Computer Vision and Pattern Recognition

Computer Science > Computer Vision and Pattern Recognition
arXiv:2007.12099 (cs)
[Submitted on 23 Jul 2020 (v1), last revised 3 Aug 2020 (this version, v3)]
Title:PP-YOLO: An Effective and Efficient Implementation of Object Detector
Authors:Xiang Long, Kaipeng Deng, Guanzhong Wang, Yang Zhang, Qingqing Dang, Yuan Gao, Hui Shen, Jianguo Ren, Shumin Han, Errui Ding, Shilei Wen
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Abstract:Object detection is one of the most important areas in computer vision, which plays a key role in various practical scenarios. Due to limitation of hardware, it is often necessary to sacrifice accuracy to ensure the infer speed of the detector in practice. Therefore, the balance between effectiveness and efficiency of object detector must be considered. The goal of this paper is to implement an object detector with relatively balanced effectiveness and efficiency that can be directly applied in actual application scenarios, rather than propose a novel detection model. Considering that YOLOv3 has been widely used in practice, we develop a new object detector based on YOLOv3. We mainly try to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged. Since all experiments in this paper are conducted based on PaddlePaddle, we call it PP-YOLO. By combining multiple tricks, PP-YOLO can achieve a better balance between effectiveness (45.2% mAP) and efficiency (72.9 FPS), surpassing the existing state-of-the-art detectors such as EfficientDet and this http URL code is at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2007.12099 [cs.CV]
(or
arXiv:2007.12099v3 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2007.12099
arXiv-issued DOI via DataCite
Submission history
From: Xiang Long [view email]
[v1]
Thu, 23 Jul 2020 16:06:16 UTC (244 KB)
[v2]
Mon, 27 Jul 2020 06:10:35 UTC (245 KB)
[v3]
Mon, 3 Aug 2020 03:53:24 UTC (245 KB)
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