PP-YOLO: An Effective and Efficient Implementation of Object Detector

By Xiang Long, Kaipeng Deng, Guanzhong Wang, Yan Zhang, Qingqing Dang, Yuan Gao, Hui Shen, Jianguo Ren, Shumin Han, Errui Ding and 1 more. arXiv.org, 2020

345 citations recorded by the source corpus.

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 YOLOv4.Source code is at this https URL.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d14bc27d39614b1f16c84a55eeb1eae157ec9517

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Citations in the corpus

DETRs Beat YOLOs on Real-time Object Detection (2023, methodology): “YOLO detectors can be classified into two categories: anchor-based [1, 10, 14, 24, 27, 28, 34, 35] and anchor-free [9, 11, 15, 37], which achieve a reasonable trade-off between speed and accuracy and are widely used in various practical scenarios.”
Scaled-YOLOv4: Scaling Cross Stage Partial Network (2020, background): “At present, the more accurate real-time one-stage object detectors are anchor-based EfﬁcientDet [31], YOLOv4 [1], and PP-YOLO [19].”
PP-YOLOE: An evolved version of YOLO (2022, background): “8% PP-YOLO [18] ResNet50-vd-dcn 416 109.”
A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond (2023, methodology): “PP-YOLO [76] 1.”
YOLO-World: Real-Time Open-Vocabulary Object Detection (2024, background): “Several works [11, 23, 33, 52, 55] propose various architectures or designs for YOLO, including path aggregation networks [29], cross-stage partial networks [51], and re-parameterization [7], which further improve both speed and accuracy.”