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PP-YOLOE: An evolved version of YOLO

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arxiv 2203.16250 v3 pith:OMFBHIZU submitted 2022-03-30 cs.CV

Shangliang Xu , Xinxin Wang , Wenyu Lv , Qinyao Chang , Cheng Cui , Kaipeng Deng , Guanzhong Wang , Qingqing Dang
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Shengyu Wei Yuning Du Baohua Lai
This is my paper
classification cs.CV
keywords modelspp-yoloespeedachievesindustrialpp-yolov2previousstate-of-the-art
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 225 citations worldwide. Full citation record

  1. RiO-DETR: DETR for Real-time Oriented Object Detection

    cs.CV 2026-03 conditional novelty 6.0 of 10

    RiO-DETR gives the first real-time oriented DETR, matching or beating CNN real-time detectors on DOTA-1.0, DIOR-R, and FAIR-1M-2.0 with a new speed-accuracy trade-off.

  2. Measuring the Impact of Rotation Equivariance on Aerial Object Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    MessDet shows that strict rotation equivariance in backbone and neck improves aerial detection accuracy over approximate equivariance, achieving SOTA on DOTA-v1.0/v1.5 and DIOR-R with 18.1M parameters.

  3. YOLOv14:Unified Cross-Domain Real-Time Object Detectionwith Adaptive Multi-View Representation

    cs.CV 2026-08 reject novelty 4.0 of 10

    YOLOv14 reports a real-time detector with 49.1 COCO mAP and cross-domain gains, but its headline game-character gain is measured on a benchmark synthesized from its own training augmentation.

  4. CollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detection

    cs.CV 2026-03 conditional novelty 4.0 of 10

    CollabOD improves UAV small-object detection via dual-path detail preservation, dense aggregation, bilateral reweighting, and a reparameterized detail-aware head, reporting 52.4 AP50 on VisDrone at 65.5 GFLOPs.

  5. E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections

    cs.CV 2025-08 conditional novelty 3.0 of 10

    E-ConvNeXt combines CSPNet, batch-normalized ConvNeXt blocks, a stepped stem, and ESE attention to reach 78.3-81.9% ImageNet top-1 at 0.9-3.1 GFLOPs.

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