Methods › Computer Vision › One-Stage Object Detection Models › PP-YOLO

PP-YOLO

4 papers tagged archive 2025-07-28

Introduced by Xiang Long et al. in PP-YOLO: An Effective and Efficient Implementation of Object Detector

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

PP-YOLO is an object detector based on YOLOv3. It mainly tries to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed is almost unchanged. Some of these changes include:

PaperSource

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object2
Object Detection2
Real-Time Object Detection2
2D Object Detection1
Dense Object Detection1
Multi-Object Tracking1
Multiple Object Tracking1
Online Multi-Object Tracking1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with PP-YOLO: 2020 to 2022, peak 2 2 0 2020: 1 paper 2020 2021: 1 paper 2021 2022: 2 papers 2022
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

One-Stage Object Detection ModelsObject Detection Models

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