PP-YOLOE: An evolved version of YOLO
Shangliang Xu, Xinxin Wang, Wenyu Lv, Qinyao Chang, Cheng Cui, Kaipeng, Deng, Guanzhong Wang, Qingqing Dang, Shengyu Wei, Yuning Du, Baohua Lai

TL;DR
PP-YOLOE is an advanced object detection model that improves accuracy and speed over previous YOLO versions, optimized for industrial deployment with various model sizes and high-performance inference capabilities.
Contribution
The paper introduces PP-YOLOE, a new state-of-the-art object detector with optimized architecture and training strategies, achieving higher accuracy and faster inference for industrial applications.
Findings
PP-YOLOE-l achieves 51.4 mAP on COCO test-dev.
PP-YOLOE-l runs at 78.1 FPS on Tesla V100.
Inference speed reaches 149.2 FPS with TensorRT and FP16.
Abstract
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · COVID-19 diagnosis using AI
MethodsBitcoin Customer Service Number +1-833-534-1729 · *Communicated@Fast*How Do I Communicate to Expedia? · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Feature Pyramid Network · Average Pooling · Residual Connection · Softmax · BNB Customer Service Number +1-833-534-1729 · Global Average Pooling · Max Pooling
