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 .

本文提出了一种名为PP-YOLOE的工业级目标检测器，采用基于先前版本的PP-YOLOv2的优化，使用无锚点范例、更强大的主干网和配备了CSPRepResStage、ET-head和动态标签分配算法TAL的neck。在COCO test-dev上实现了51.4 mAP，Tesla V100的时延为78.1 FPS，相对于之前最先进的工业模型PP-YOLOv2和YOLOX分别有(+1.9 AP，+13.35%加速)和(+1.3 AP，+24.96%加速)的显著改进。

PP-YOLOE：YOLO 的进化版