# PP-YOLO: An Effective and Efficient Implementation of Object Detector

arXiv: https://arxiv.org/abs/2007.12099 · HTML: https://mlsanity.com/paper/2007.12099/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 30 of 44)

- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [PP-YOLOE: An evolved version of YOLO](https://mlsanity.com/paper/2203.16250/) (2022)
- [Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information](https://mlsanity.com/paper/2204.09860/) (2022)
- [RT-DETRv3: Real-time End-to-End Object Detection with Hierarchical Dense Positive Supervision](https://mlsanity.com/paper/2409.08475/) (2024)
- [PP-YOLOv2: A Practical Object Detector](https://mlsanity.com/paper/2104.10419/) (2021)
- [Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism](https://mlsanity.com/paper/2301.10051/) (2023)
- [A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects](https://mlsanity.com/paper/2305.07348/) (2023)
- [Real-Time Object Detection Meets DINOv3](https://mlsanity.com/paper/2509.20787/) (2025)
- [PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices](https://mlsanity.com/paper/2111.00902/) (2021)
- [Domain Adaptive YOLO for One-Stage Cross-Domain Detection](https://mlsanity.com/paper/2106.13939/) (2021)
- [Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head](https://mlsanity.com/paper/2403.06892/) (2024)
- [Mamba YOLO: A Simple Baseline for Object Detection with State Space Model](https://mlsanity.com/paper/2406.05835/) (2024)
- [Cascaded Zoom-in Detector for High Resolution Aerial Images](https://mlsanity.com/paper/2303.08747/) (2023)
- [A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance](https://mlsanity.com/paper/2207.12926/) (2022)
- [Continuous Human Action Recognition for Human-Machine Interaction: A Review](https://mlsanity.com/paper/2202.13096/) (2022)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)
- [ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition](https://mlsanity.com/paper/2301.09878/) (2023)
- [PAFNet: An Efficient Anchor-Free Object Detector Guidance](https://mlsanity.com/paper/2104.13534/) (2021)
- [YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification](https://mlsanity.com/paper/2305.04170/) (2023)
- [Deep Learning Based Steel Pipe Weld Defect Detection](https://mlsanity.com/paper/2104.14907/) (2021)
- [A Decade of You Only Look Once (YOLO) for Object Detection: A Review](https://mlsanity.com/paper/2504.18586/) (2025)
- [DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation](https://mlsanity.com/paper/2107.07330/) (2021)
- [In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos](https://mlsanity.com/paper/2201.11450/) (2022)
- [FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications](https://mlsanity.com/paper/2209.03157/) (2022)
- [A Survey of Advanced Computer Vision Techniques for Sports](https://mlsanity.com/paper/2301.07583/) (2023)
- [GeoPos: A Minimal Positional Encoding for Enhanced Fine-Grained Details in Image Synthesis Using Convolutional Neural Networks](https://mlsanity.com/paper/2401.01951/) (2024)
- [A Deep Learning Approach to Detect Complete Safety Equipment For Construction Workers Based On YOLOv7](https://mlsanity.com/paper/2406.07707/) (2024)
- [Unified-IoU: For High-Quality Object Detection](https://mlsanity.com/paper/2408.06636/) (2024)

## Abstract (arXiv)

Object detection is one of the most important areas in computer vision, which plays a key role in various practical scenarios. Due to limitation of hardware, it is often necessary to sacrifice accuracy to ensure the infer speed of the detector in practice. Therefore, the balance between effectiveness and efficiency of object detector must be considered. The goal of this paper is to implement an object detector with relatively balanced effectiveness and efficiency that can be directly applied in actual application scenarios, rather than propose a novel detection model. Considering that YOLOv3 has been widely used in practice, we develop a new object detector based on YOLOv3. We mainly try 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. Since all experiments in this paper are conducted based on PaddlePaddle, we call it PP-YOLO. By combining multiple tricks, PP-YOLO can achieve a better balance between effectiveness (45.2% mAP) and efficiency (72.9 FPS), surpassing the existing state-of-the-art detectors such as EfficientDet and YOLOv4.Source code is at https://github.com/PaddlePaddle/PaddleDetection.