YOLO11 vs YOLOv5: A Detailed Comparison
Ultralytics YOLO models are renowned for their speed and accuracy in object detection tasks, serving as a cornerstone for many computer vision applications. This page provides a technical comparison between two significant models in the YOLO family: Ultralytics YOLO11 and YOLOv5. We will delve into their architectural nuances, performance benchmarks, and suitable use cases to help you make an informed decision for your projects.
YOLO11: Redefining Efficiency and Accuracy
Ultralytics YOLO11 represents the latest evolution in the YOLO series, building upon the foundation laid by its predecessors. It is designed to offer enhanced accuracy and efficiency, making it suitable for a wide array of real-time object detection tasks.
Architecture and Key Features
YOLO11 introduces several architectural refinements focused on improving feature extraction and processing speed. Key improvements include a streamlined network structure and optimized layers that contribute to a reduction in parameter count while maintaining or improving accuracy. This architectural efficiency translates to faster inference times and reduced computational costs, making YOLO11 particularly effective for deployment on resource-constrained devices and edge computing environments.
Performance Metrics
YOLO11 demonstrates superior performance over YOLOv5 in key metrics, especially in balancing accuracy and speed. The table below provides a detailed comparison, but generally, YOLO11 achieves higher mAP with fewer parameters in comparable model sizes. Its inference speed is also optimized, offering faster processing on both CPU and GPU, particularly when using TensorRT.
Strengths and Weaknesses
Strengths:
- Enhanced Accuracy: YOLO11 achieves higher mAP with fewer parameters compared to YOLOv5, indicating a more efficient use of model complexity.
- Faster Inference: Optimized architecture leads to quicker processing times, crucial for real-time applications.
- Smaller Model Size: Reduced parameter count makes YOLO11 models more compact and easier to deploy on edge devices.
- Versatility: Supports the same range of tasks as YOLOv8, including object detection, instance segmentation, image classification, and pose estimation.
Weaknesses:
- Relatively New: Being a newer model, the community support and breadth of resources might still be growing compared to the more established YOLOv5.
- Computational Cost: While efficient, the 'x' variants for highest accuracy still require significant computational resources, though less than comparable YOLOv5 models.
Use Cases
YOLO11 is ideally suited for applications demanding high accuracy and real-time performance, such as:
- Autonomous Systems: Self-driving cars and robotics benefit from YOLO11's speed and precision in object detection for navigation and safety (AI in Self-Driving).
- Surveillance and Security: Real-time security systems requiring accurate and fast detection of objects or anomalies (Computer Vision for Theft Prevention).
- Industrial Automation: Quality control in manufacturing and automated sorting in recycling plants (AI in Manufacturing, Recycling Efficiency with Vision AI).
- Healthcare: Medical image analysis for faster and more accurate diagnostics (AI in Healthcare, YOLO11 in Hospitals).
YOLOv5: The Established and Versatile Choice
Ultralytics YOLOv5 is a highly popular and widely adopted object detection model, known for its balance of speed and accuracy, and its ease of use. It has become a staple in the computer vision community due to its robust performance and extensive documentation.
Architecture and Key Features
YOLOv5 offers a flexible architecture that allows for easy scaling and customization, making it adaptable to various hardware and application needs. It utilizes a CSP (Cross Stage Partial) network and focuses on efficient feature reuse, which contributes to its speed and efficiency. YOLOv5 is implemented in PyTorch, which provides a user-friendly environment and facilitates deployment across different platforms.
Performance Metrics
YOLOv5 provides a range of models from Nano to Extra Large, catering to different performance requirements. While it generally has lower mAP compared to YOLO11 for similarly sized models, YOLOv5 excels in inference speed and is still highly competitive in terms of accuracy for many applications. Its performance is well-documented and widely benchmarked, making it a reliable choice.
Strengths and Weaknesses
Strengths:
- High Inference Speed: YOLOv5 is particularly fast, making it excellent for real-time applications where latency is critical.
- Ease of Use: Well-documented and easy to implement, with strong community support and resources.
- Scalability: Offers a range of model sizes to suit different computational budgets and accuracy needs.
- Mature and Stable: Being an established model, YOLOv5 benefits from extensive testing and community feedback, making it a stable and reliable option.
Weaknesses:
- Lower Accuracy Compared to YOLO11: For tasks requiring the highest possible accuracy, YOLOv5 might be less optimal than YOLO11.
- Larger Parameter Count for Similar Accuracy: To achieve comparable accuracy to YOLO11, YOLOv5 models often require more parameters, leading to larger model sizes.
Use Cases
YOLOv5 is widely used in applications where speed and reliability are paramount:
- Edge Devices: Deployment on Raspberry Pi and NVIDIA Jetson for edge AI applications due to its efficiency (Raspberry Pi Quickstart, NVIDIA Jetson Quickstart).
- Mobile Applications: Real-time object detection in mobile apps where model size and inference speed are critical (Ultralytics iOS App).
- Wildlife Conservation: Monitoring and tracking animals in real-time for conservation efforts (Kashmir World Foundation with YOLOv5).
- Retail Analytics: Inventory management and customer behavior analysis in retail environments (AI for Smarter Retail, Build Intelligent Stores with YOLOv8 and Seeed Studio).
Model Comparison Table
Model | size (pixels) |
mAPval 50-95 |
Speed CPU ONNX (ms) |
Speed T4 TensorRT10 (ms) |
params (M) |
FLOPs (B) |
---|---|---|---|---|---|---|
YOLO11n | 640 | 39.5 | 56.1 | 1.5 | 2.6 | 6.5 |
YOLO11s | 640 | 47.0 | 90.0 | 2.5 | 9.4 | 21.5 |
YOLO11m | 640 | 51.5 | 183.2 | 4.7 | 20.1 | 68.0 |
YOLO11l | 640 | 53.4 | 238.6 | 6.2 | 25.3 | 86.9 |
YOLO11x | 640 | 54.7 | 462.8 | 11.3 | 56.9 | 194.9 |
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 |
Key Differences Summarized
Feature | YOLO11 | YOLOv5 |
---|---|---|
Accuracy (mAP) | Generally higher for comparable model sizes | Slightly lower, but still competitive |
Inference Speed | Faster, especially with TensorRT | Very fast, excellent for latency-critical apps |
Model Size | Smaller, fewer parameters | Can be larger for similar accuracy |
Architecture | Streamlined, optimized for efficiency | Flexible CSP architecture, scalable |
Maturity & Community | Newer, growing community | Established, large community and resources |
Ideal Use Cases | High accuracy, real-time, edge deployment | Speed-critical, mobile, edge, versatile |
Conclusion
Choosing between YOLO11 and YOLOv5 depends on the specific requirements of your project. If accuracy is paramount and computational resources are reasonably available, YOLO11 offers superior performance. For applications where speed and ease of deployment are critical, and a slightly lower mAP is acceptable, YOLOv5 remains an excellent and reliable choice.
Users interested in exploring other models may also consider YOLOv8, YOLOv9, YOLO-NAS, and RT-DETR from Ultralytics, each offering unique strengths and optimizations for various computer vision tasks.
For further details and to explore the capabilities of each model, refer to the official Ultralytics documentation and GitHub repository.