  Being effective and efficient is essential to an object detector for
practical use. To meet these two concerns, we comprehensively evaluate a
collection of existing refinements to improve the performance of PP-YOLO while
almost keep the infer time unchanged. This paper will analyze a collection of
refinements and empirically evaluate their impact on the final model
performance through incremental ablation study. Things we tried that didn't
work will also be discussed. By combining multiple effective refinements, we
boost PP-YOLO's performance from 45.9% mAP to 49.5% mAP on COCO2017 test-dev.
Since a significant margin of performance has been made, we present PP-YOLOv2.
In terms of speed, PP-YOLOv2 runs in 68.9FPS at 640x640 input size. Paddle
inference engine with TensorRT, FP16-precision, and batch size = 1 further
improves PP-YOLOv2's infer speed, which achieves 106.5 FPS. Such a performance
surpasses existing object detectors with roughly the same amount of parameters
(i.e., YOLOv4-CSP, YOLOv5l). Besides, PP-YOLOv2 with ResNet101 achieves 50.3%
mAP on COCO2017 test-dev. Source code is at
https://github.com/PaddlePaddle/PaddleDetection.
