  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.
