YOLOv9 vs YOLOv5: A Detailed Comparison
This page provides a technical comparison between YOLOv9 and YOLOv5, two popular models in the YOLO series, focusing on their object detection capabilities. We delve into their architectural differences, performance metrics, training methodologies, and suitable use cases to help you choose the right model for your computer vision tasks.
YOLOv9: Programmable Gradient Information
YOLOv9, introduced in February 2024 by Chien-Yao Wang and Hong-Yuan Mark Liao from the Institute of Information Science, Academia Sinica, Taiwan, represents a significant advancement in real-time object detection. The model is detailed in their paper "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information" and the code is available on GitHub.
Architecture and Innovations: YOLOv9 introduces two key innovations: Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). PGI addresses information loss in deep networks, allowing the model to learn more effectively, while GELAN optimizes network architecture for improved parameter utilization and computational efficiency. This combination leads to enhanced accuracy without a proportional increase in computational cost.
Performance: YOLOv9 achieves state-of-the-art performance on the MS COCO dataset, demonstrating superior accuracy and efficiency compared to previous YOLO versions and other real-time object detectors. For instance, YOLOv9c achieves 53.0 mAPval50-95 with 25.3M parameters.
Use Cases: YOLOv9 is ideally suited for applications demanding high accuracy and efficiency, such as:
- High-precision object detection: Scenarios where accuracy is paramount, like autonomous driving, advanced surveillance, and robotic vision.
- Resource-constrained environments: While training requires more resources than YOLOv5, the efficient architecture allows for deployment on edge devices with optimized inference speed.
Strengths:
- High Accuracy: Achieves superior mAP scores, particularly in models like YOLOv9e.
- Efficient Design: GELAN and PGI contribute to better parameter and computational efficiency compared to previous models with similar accuracy.
Weaknesses:
- Higher Training Resource Demand: Training YOLOv9 models requires more computational resources and time compared to YOLOv5.
- Relatively Newer Model: Being a newer model, the community and documentation are still developing compared to the more established YOLOv5.
YOLOv5: Versatility and Speed
Ultralytics YOLOv5, authored by Glenn Jocher and released in June 2020, is renowned for its speed, ease of use, and versatility. While there isn't a specific arXiv paper, detailed information is available in the Ultralytics YOLOv5 documentation and the GitHub repository.
Architecture and Features: YOLOv5 is built with a focus on speed and accessibility, utilizing architectures like CSP Bottleneck and PANet. It offers a range of model sizes (YOLOv5n, s, m, l, x) to cater to different computational budgets and performance needs. YOLOv5 is implemented in PyTorch, making it user-friendly and highly adaptable.
Performance: YOLOv5 provides a balance between speed and accuracy, making it suitable for a wide range of real-world applications. YOLOv5s, a small variant, achieves 37.4 mAPval50-95 with fast inference speeds.
Use Cases: YOLOv5 is exceptionally versatile and fits well in scenarios where speed and ease of deployment are critical:
- Real-time applications: Ideal for applications requiring fast inference, such as live video processing, robotics, and drone vision.
- Edge deployment: The smaller models (YOLOv5n, YOLOv5s) are well-suited for deployment on edge devices and mobile platforms due to their lower computational demands.
- Rapid prototyping and development: Its ease of use and extensive documentation make YOLOv5 excellent for quick development cycles and educational purposes.
Strengths:
- High Speed: Offers fast inference speeds, especially with smaller model variants.
- Ease of Use: Well-documented with a large and active community, making it easy to use and implement.
- Versatility: Available in multiple sizes and adaptable to various tasks including detection, segmentation, and classification.
Weaknesses:
- Lower Accuracy Compared to YOLOv9: Generally, YOLOv5 models do not achieve the same level of accuracy as the latest YOLOv9, particularly in demanding scenarios.
- Architecture Less Innovative Than YOLOv9: While effective, its architecture does not incorporate the novel PGI and GELAN innovations found in YOLOv9.
| Model | size(pixels) | mAPval50-95 | SpeedCPU ONNX(ms) | SpeedT4 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 |
| YOLOv5n | 640 | 28.0 | 73.6 | 1.12 | 2.6 | 7.7 |
| YOLOv5s | 640 | 37.4 | 120.7 | 1.92 | 9.1 | 24.0 |
| YOLOv5m | 640 | 45.4 | 233.9 | 4.03 | 25.1 | 64.2 |
| YOLOv5l | 640 | 49.0 | 408.4 | 6.61 | 53.2 | 135.0 |
| YOLOv5x | 640 | 50.7 | 763.2 | 11.89 | 97.2 | 246.4 |
Conclusion
Choosing between YOLOv9 and YOLOv5 depends on your project priorities. If accuracy is paramount and resources for training are available, YOLOv9 is the superior choice. For applications prioritizing speed, ease of use, and deployment flexibility, especially on edge devices, YOLOv5 remains an excellent and widely adopted option.
For users interested in exploring other models, Ultralytics also offers YOLOv8, YOLOv7, YOLOv6, and the newly released YOLO11, each with its own strengths and optimizations. Explore the Ultralytics Models documentation to discover the full range of options.
