PP-YOLOE: An evolved version of YOLO

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 27 functions out of this paper's own repositories and ran 17 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

Repositories linked to this paper

• PaddlePaddle/PaddleDetection — marked official in Papers with Code; named in the paper
• Nioolek/PPYOLOE_pytorch
• 2023-MindSpore-4/Code10
• CycloneBoy/PPDetectionPytorch
• Gaurav14cs17/YOLOE
• Mind23-2/MindCode-130
• PaddlePaddle/PaddleYOLO
• open-mmlab/mmyolo

Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.

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 https://github.com/PaddlePaddle/PaddleDetection.

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get_code_for_paper("2203.16250")
have("2203.16250")

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