论文标题
PP-YOLO：对象检测器的有效实施
PP-YOLO: An Effective and Efficient Implementation of Object Detector
论文作者
论文摘要
对象检测是计算机视觉中最重要的领域之一，在各种实际情况下起着关键作用。由于硬件的限制，通常有必要牺牲准确性，以确保在实践中推断探测器的速度。因此，必须考虑对象检测器的有效性与效率之间的平衡。本文的目的是实施具有相对平衡的有效性和效率的对象检测器，该对象检测器可以直接在实际应用程序场景中应用，而不是提出一种新颖的检测模型。考虑到Yolov3已在实践中广泛使用，我们基于Yolov3开发了一个新的对象检测器。我们主要尝试结合几乎不会增加模型参数和拖船数量的各种现有技巧，以实现尽可能提高检测器准确性的目标，同时确保速度几乎没有变化。由于本文中的所有实验都是基于桨板进行的，因此我们称其为pp-yolo。通过结合多种技巧，PP-Yolo可以在有效性（45.2％的地图）和效率（72.9 fps）之间取得更好的平衡，超过了现有的最新检测器，例如EdgitionDet和Yolov4.source4.source 4.source code在https://github.com/github.com/paddlepaddle/paddlepaddle/paddedletection上。
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 https://github.com/PaddlePaddle/PaddleDetection.
