计算机科学
推论
探测器
编码(集合论)
软件部署
国家(计算机科学)
对象(语法)
人工智能
算法
程序设计语言
操作系统
电信
集合(抽象数据类型)
作者
Shangliang Xu,Xinxin Wang,Wenyu Lv,Qinyao Chang,Cheng Cui,Kaipeng Deng,Guanzhong Wang,Qingqing Dang,Shengyu Wei,Yuning Du,Baohua Lai
出处
期刊:Cornell University - arXiv
日期:2022-01-01
被引量:11
标识
DOI:10.48550/arxiv.2203.16250
摘要
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.
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