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# YOLOv5 vs YOLOv9: A Detailed Comparison

This page provides a technical comparison between two significant object detection models: Ultralytics YOLOv5 and YOLOv9. Both models are part of the influential YOLO (You Only Look Once) series, known for balancing speed and accuracy in real-time object detection. This comparison explores their architectural differences, performance metrics, and ideal use cases to help you select the most suitable model for your computer vision projects.

## Ultralytics YOLOv5: The Established Industry Standard

Author: Glenn Jocher
Organization: Ultralytics
Date: 2020-06-26
GitHub: https://github.com/ultralytics/yolov5
Documentation: https://docs.ultralytics.com/models/yolov5/

Ultralytics YOLOv5 quickly gained popularity after its release due to its remarkable balance of speed, accuracy, and ease of use. Developed entirely in PyTorch, YOLOv5 features an architecture utilizing CSPDarknet53 as the backbone and PANet for feature aggregation, along with an efficient anchor-based detection head. It offers various model sizes (n, s, m, l, x), allowing users to choose based on their computational resources and performance needs.

### Strengths

- Exceptional Speed and Efficiency: YOLOv5 is highly optimized for fast inference, making it ideal for real-time applications on various hardware, including edge devices.
- Ease of Use: Ultralytics YOLOv5 is renowned for its streamlined user experience, simple Python and CLI interfaces, and extensive documentation.
- Well-Maintained Ecosystem: Benefits from the integrated Ultralytics ecosystem, featuring active development, a large and supportive community, frequent updates, and comprehensive resources like Ultralytics HUB for no-code training.
- Performance Balance: Achieves a strong trade-off between inference speed and detection accuracy, suitable for diverse real-world deployment scenarios.
- Versatility: Supports multiple tasks including object detection, instance segmentation, and image classification.
- Training Efficiency: Offers efficient training processes, readily available pre-trained weights, and generally lower memory requirements compared to many other architectures, especially transformer-based models.

### Weaknesses

- Accuracy: While highly accurate for its time, newer models like YOLOv9 can achieve higher mAP scores on benchmarks like COCO.
- Anchor-Based: Relies on predefined anchor boxes, which might require more tuning for specific datasets compared to anchor-free approaches.

### Use Cases

- Real-time video surveillance and security systems.
- Deployment on resource-constrained edge devices like Raspberry Pi and NVIDIA Jetson.
- Industrial automation and quality control, such as improving manufacturing with computer vision.
- Rapid prototyping and development due to its ease of use and robust ecosystem.

Learn more about YOLOv5

## YOLOv9: Advancing Accuracy with Novel Techniques

Authors: Chien-Yao Wang, 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 introduces significant architectural innovations, namely Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). PGI aims to mitigate information loss as data flows through deep networks by providing complete input information for loss function calculation. GELAN is a novel architecture designed for superior parameter utilization and computational efficiency. These advancements allow YOLOv9 to achieve higher accuracy while maintaining efficiency.

### Strengths

- Enhanced Accuracy: Sets new state-of-the-art results on the COCO dataset for real-time object detectors, surpassing YOLOv5 and other models in mAP.
- Improved Efficiency: GELAN and PGI contribute to models that require fewer parameters and computational resources (FLOPs) for comparable or better performance than previous models.
- Information Preservation: PGI effectively addresses the information bottleneck problem, which is crucial for training deeper and more complex networks accurately.

### Weaknesses

- Training Resources: Training YOLOv9 models can be more resource-intensive and time-consuming compared to Ultralytics YOLOv5, as noted in the YOLOv9 documentation.
- Newer Architecture: As a more recent model from a different research group, its ecosystem, community support, and third-party integrations are less mature than the well-established Ultralytics YOLOv5.
- Task Versatility: Primarily focused on object detection, lacking the built-in support for segmentation, classification, and pose estimation found in Ultralytics models like YOLOv5 and YOLOv8.

### Use Cases

- Applications demanding the highest possible object detection accuracy.
- Scenarios where computational efficiency is critical alongside high performance.
- Advanced video analytics and high-precision industrial inspection.
- AI in traffic management and smart city applications requiring top-tier detection.

Learn more about YOLOv9

## Performance and Benchmarks: YOLOv5 vs. YOLOv9

When comparing performance, YOLOv9 models generally achieve higher mAP scores than their YOLOv5 counterparts, demonstrating the effectiveness of its architectural innovations. However, Ultralytics YOLOv5 maintains a strong position due to its exceptional inference speed and highly optimized implementation, making it a formidable choice for real-time applications where frames per second (FPS) is a critical metric.

Model | size (pixels) | mAPval 50-95 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B)
--- | --- | --- | --- | --- | --- | ---
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
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

## Architectural Deep Dive

### YOLOv5 Architecture

The architecture of Ultralytics YOLOv5 is a refined implementation of the YOLO family principles. It consists of three main parts:

- Backbone: A CSPDarknet53 network, which is a modified version of Darknet-53 that incorporates Cross Stage Partial (CSP) modules to reduce computation while maintaining accuracy.
- Neck: A Path Aggregation Network (PANet) is used to aggregate features from different backbone levels, improving the detection of objects at various scales.
- Head: The detection head is anchor-based, predicting bounding boxes from predefined anchor box shapes, which contributes to its high speed.

### YOLOv9 Architecture

YOLOv9 introduces novel concepts to push the boundaries of accuracy and efficiency:

- Programmable Gradient Information (PGI): This mechanism is designed to combat the information bottleneck problem in deep networks. It ensures that complete input information is available for calculating the loss function, leading to more reliable gradient updates and better model convergence.
- Generalized Efficient Layer Aggregation Network (GELAN): This is a new network architecture that builds upon the principles of CSPNet and ELAN. GELAN is designed to optimize parameter utilization and computational efficiency, allowing the model to achieve higher accuracy with fewer resources.

## Training and Ecosystem

The training experience and ecosystem support are where Ultralytics YOLOv5 truly shines.

- Ease of Use: YOLOv5 offers an incredibly user-friendly experience with simple command-line and Python APIs, extensive tutorials, and comprehensive documentation.
- Well-Maintained Ecosystem: As an official Ultralytics model, YOLOv5 is part of a robust ecosystem that includes active development, a large community on GitHub and Discord, frequent updates, and seamless integration with MLOps tools like Ultralytics HUB.
- Training Efficiency: YOLOv5 is highly efficient to train, with readily available pre-trained weights and lower memory requirements compared to more complex architectures. This makes it accessible to users with a wider range of hardware.

While YOLOv9 is a powerful model, its training process can be more demanding, and its ecosystem is not as mature or integrated as that of Ultralytics models. For developers looking for a smooth, well-supported path from training to deployment, YOLOv5 offers a clear advantage.

## Conclusion: Which Model Should You Choose?

Both YOLOv5 and YOLOv9 are excellent models, but they cater to different priorities.

- Ultralytics YOLOv5 is the ideal choice for developers who prioritize speed, ease of use, and a mature, well-supported ecosystem. Its exceptional performance balance makes it perfect for real-time applications, rapid prototyping, and deployment on resource-constrained edge AI devices. Its versatility across multiple vision tasks adds to its value as a general-purpose vision AI framework.

- YOLOv9 is best suited for applications where achieving the highest possible object detection accuracy is the primary objective, and computational resources for training are less of a concern. Its innovative architecture delivers state-of-the-art results on challenging benchmarks.

For most users, especially those looking for a reliable, fast, and easy-to-use model with strong community and commercial support, Ultralytics YOLOv5 remains a top recommendation. For those interested in the latest advancements from Ultralytics, models like YOLOv8 and the newest YOLO11 offer even greater performance and versatility while retaining the user-friendly experience that defines the Ultralytics ecosystem.

📅 Created 1 year ago ✏️ Updated 1 month ago

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