Skip to content

Register now for

# YOLOv9 vs PP-YOLOE+: A Technical Comparison

Choosing the right object detection model involves a critical trade-off between accuracy, speed, and resource requirements. This page provides a detailed technical comparison between Ultralytics YOLOv9, a state-of-the-art model known for its architectural innovations, and Baidu's PP-YOLOE+, a strong contender from the PaddlePaddle ecosystem. We will analyze their architectures, performance metrics, and ideal use cases to help you select the best model for your computer vision projects.

## YOLOv9: Programmable Gradient Information for Enhanced Learning

Ultralytics YOLOv9 represents a significant advancement in real-time object detection, introducing groundbreaking concepts to address long-standing challenges in deep learning.

Authors: Chien-Yao Wang and Hong-Yuan Mark Liao
Organization: Institute of Information Science, Academia Sinica, Taiwan
Date: 2024-02-21
Arxiv: https://arxiv.org/abs/2402.13616
GitHub: https://github.com/WongKinYiu/yolov9
Documentation: https://docs.ultralytics.com/models/yolov9/

YOLOv9's core innovations are Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). PGI is designed to tackle the problem of information loss as data flows through deep networks, ensuring that reliable gradient information is available for accurate model updates. This helps preserve key features and improves learning. GELAN is a novel network architecture optimized for superior parameter utilization and computational efficiency. This powerful combination allows YOLOv9 to achieve exceptional accuracy while maintaining high inference speeds.

A key advantage of YOLOv9 is its integration into the Ultralytics ecosystem. This provides a streamlined user experience with a simple API, comprehensive documentation, and a robust support network. The ecosystem benefits from active development, a strong community on platforms like GitHub and Discord, and frequent updates. This ensures that developers have access to efficient training processes, readily available pre-trained weights, and a platform that supports multiple tasks like object detection and instance segmentation.

### Strengths

- High Accuracy: Achieves state-of-the-art mAP scores on challenging datasets like COCO, with the YOLOv9-E model setting a new benchmark for real-time detectors.
- Efficient Architecture: The GELAN architecture and PGI lead to excellent performance with significantly fewer parameters and FLOPs compared to models with similar accuracy.
- Information Preservation: PGI effectively mitigates the information bottleneck problem in deep networks, leading to better model convergence and accuracy.
- Ultralytics Ecosystem: Benefits from ease of use, extensive documentation, active maintenance, and strong community support. It is built on PyTorch, the most popular AI framework, making it highly accessible.
- Versatility: The architecture is versatile, supporting multiple computer vision tasks beyond just detection.

### Weaknesses

- Newer Model: As a recent release, the breadth of community-contributed examples and third-party integrations is still growing, although its inclusion in the Ultralytics framework accelerates adoption.
- Training Resources: While efficient for its performance level, training the largest YOLOv9 variants can require substantial computational resources.

### Ideal Use Cases

YOLOv9 excels in applications that demand the highest levels of accuracy and efficiency. This makes it ideal for complex tasks such as autonomous driving, advanced security systems, and high-precision robotics. Its efficient design also makes smaller variants suitable for deployment in resource-constrained edge AI environments.

Learn more about YOLOv9

## PP-YOLOE+: High Accuracy within the PaddlePaddle Ecosystem

PP-YOLOE+ is a high-performance object detector developed by Baidu and is a key part of their PaddleDetection suite. It is designed to deliver a strong balance of speed and accuracy, but its implementation is tightly coupled with the PaddlePaddle deep learning framework.

Authors: PaddlePaddle Authors
Organization: Baidu
Date: 2022-04-02
Arxiv: https://arxiv.org/abs/2203.16250
GitHub: https://github.com/PaddlePaddle/PaddleDetection/
Documentation: https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.8.1/configs/ppyoloe/README.md

PP-YOLOE+ is an anchor-free, single-stage detector that builds on previous PP-YOLO versions. It incorporates an efficient backbone, often based on CSPRepResNet, and a detection head that uses Task Alignment Learning (TAL) to improve classification and localization alignment. The model series offers various sizes (s, m, l, x) to cater to different computational budgets.

### Strengths

- Strong Performance: Delivers competitive accuracy and speed, making it a capable model for many object detection tasks.
- Optimized for PaddlePaddle: For teams already invested in the Baidu PaddlePaddle ecosystem, PP-YOLOE+ offers seamless integration and optimized performance.

### Weaknesses

- Framework Dependency: Its reliance on the PaddlePaddle framework can be a significant barrier for the broader community, which predominantly uses PyTorch. Migrating projects or integrating with PyTorch-based tools can be complex.
- Limited Versatility: PP-YOLOE+ is primarily focused on object detection. In contrast, Ultralytics models like YOLOv8 and YOLO11 offer a unified framework for multiple tasks, including segmentation, pose estimation, and classification, out of the box.
- Ecosystem and Support: The ecosystem around PP-YOLOE+ is less extensive than that of Ultralytics YOLO. Users may find fewer community tutorials, less responsive support channels, and slower updates compared to the vibrant and actively maintained Ultralytics ecosystem.

### Ideal Use Cases

PP-YOLOE+ is best suited for developers and organizations that are already standardized on the Baidu PaddlePaddle deep learning framework. It is a solid choice for standard object detection applications where the development team has existing expertise in PaddlePaddle.

Learn more about PP-YOLOE+

## Performance Analysis: YOLOv9 vs. PP-YOLOE+

When comparing performance, it's clear that YOLOv9 sets a higher standard for both accuracy and efficiency.

Model | size (pixels) | mAPval 50-95 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B)
--- | --- | --- | --- | --- | --- | ---
YOLOv9t | 640 | 38.3 | - | 2.3 | 2.0 | 7.7
YOLOv9s | 640 | 46.8 | - | 3.54 | 7.1 | 26.4
YOLOv9m | 640 | 51.4 | - | 6.43 | 20.0 | 76.3
YOLOv9c | 640 | 53.0 | - | 7.16 | 25.3 | 102.1
YOLOv9e | 640 | 55.6 | - | 16.77 | 57.3 | 189.0
PP-YOLOE+t | 640 | 39.9 | - | 2.84 | 4.85 | 19.15
PP-YOLOE+s | 640 | 43.7 | - | 2.62 | 7.93 | 17.36
PP-YOLOE+m | 640 | 49.8 | - | 5.56 | 23.43 | 49.91
PP-YOLOE+l | 640 | 52.9 | - | 8.36 | 52.2 | 110.07
PP-YOLOE+x | 640 | 54.7 | - | 14.3 | 98.42 | 206.59

- Peak Accuracy: YOLOv9-E achieves the highest mAP of 55.6, outperforming the largest PP-YOLOE+x model (54.7 mAP) while using significantly fewer parameters (57.3M vs. 98.42M).
- Efficiency: YOLOv9 demonstrates superior parameter efficiency across the board. For instance, YOLOv9-C reaches 53.0 mAP with just 25.3M parameters, whereas PP-YOLOE+l requires 52.2M parameters to achieve a similar 52.9 mAP. The smallest model, YOLOv9-T, is exceptionally lightweight with only 2.0M parameters.
- Speed vs. Accuracy Trade-off: While PP-YOLOE+s shows the fastest inference time on a T4 GPU, it comes at the cost of accuracy (43.7 mAP). In contrast, YOLOv9-S delivers a much higher 46.8 mAP with only a marginal increase in latency, representing a better trade-off for applications where accuracy is important.

## Conclusion: Which Model Should You Choose?

For the vast majority of developers, researchers, and businesses, YOLOv9 is the superior choice. Its state-of-the-art accuracy, combined with remarkable computational and parameter efficiency, sets a new standard in real-time object detection.

The primary advantage of YOLOv9 lies not just in its performance but in its integration within the Ultralytics ecosystem. Built on the widely-adopted PyTorch framework, it offers unparalleled ease of use, extensive documentation, multi-task versatility, and a vibrant, supportive community. This holistic environment drastically reduces development time and simplifies deployment and maintenance.

PP-YOLOE+ is a capable model, but its value is largely confined to users already operating within the Baidu PaddlePaddle ecosystem. For those outside this specific environment, the costs of adopting a new framework and the limitations in versatility and community support make it a less practical option compared to the powerful and accessible solution offered by Ultralytics YOLOv9.

## Other Models to Consider

If you are exploring different architectures, you might also be interested in other models available in the Ultralytics ecosystem:

- YOLOv8: A highly versatile and balanced model, excellent for a wide range of tasks and known for its speed and ease of use.
- YOLO11: The latest official Ultralytics model, pushing the boundaries of performance and efficiency even further.
- RT-DETR: A real-time transformer-based detector that offers a different architectural approach to object detection.

📅 Created 1 year ago ✏️ Updated 1 month ago

## Comments