  In this report, we present PP-YOLOE, an industrial state-of-the-art object
detector with high performance and friendly deployment. We optimize on the
basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful
backbone and neck equipped with CSPRepResStage, ET-head and dynamic label
assignment algorithm TAL. We provide s/m/l/x models for different practice
scenarios. As a result, PP-YOLOE-l achieves 51.4 mAP on COCO test-dev and 78.1
FPS on Tesla V100, yielding a remarkable improvement of (+1.9 AP, +13.35% speed
up) and (+1.3 AP, +24.96% speed up), compared to the previous state-of-the-art
industrial models PP-YOLOv2 and YOLOX respectively. Further, PP-YOLOE inference
speed achieves 149.2 FPS with TensorRT and FP16-precision. We also conduct
extensive experiments to verify the effectiveness of our designs. Source code
and pre-trained models are available at
https://github.com/PaddlePaddle/PaddleDetection.
