Technical Comparison: YOLOv5 vs YOLO11 for Object Detection
Ultralytics YOLO models are renowned for their speed and accuracy in object detection. This page offers a detailed technical comparison between two popular models: YOLOv5 and the latest YOLO11, focusing on their object detection capabilities. We will analyze their architectures, performance metrics, training methodologies, and ideal applications to help you choose the right model for your computer vision needs.
YOLOv5: Versatile and Efficient Object Detection
Ultralytics YOLOv5 is a highly versatile and efficient object detection model, widely adopted for its balance of speed and accuracy. It offers a range of model sizes (n, s, m, l, x) to cater to different computational resources and application needs.
Architecture and Strengths: YOLOv5 utilizes a single-stage object detection approach, making it incredibly fast. Its architecture is based on a CSPDarknet53 backbone, known for efficient feature extraction. YOLOv5 is praised for its ease of use, comprehensive documentation, and strong community support. It's a robust choice for various applications, from research to deployment on edge devices.
Performance Metrics: YOLOv5 achieves impressive performance, with mAP ranging from 28.0 to 50.7 on the COCO dataset depending on the model variant. Inference speeds are also remarkable, making it suitable for real-time applications. Model sizes vary, allowing users to select a model that fits their memory constraints.
Use Cases: YOLOv5 is ideal for applications requiring a balance of speed and accuracy, such as:
- Wildlife conservation, as demonstrated by the Kashmir World Foundation's use of YOLOv5 to combat poaching.
- Recycling efficiency in automated sorting systems.
- Object detection in sports analytics.
YOLO11: Redefining Accuracy and Efficiency
Ultralytics YOLO11 is the latest iteration in the YOLO series, building upon the successes of its predecessors to deliver enhanced accuracy and efficiency. YOLO11 maintains the real-time performance of YOLO models while pushing the boundaries of object detection precision.
Architecture and Improvements: YOLO11 introduces architectural refinements that lead to higher accuracy with fewer parameters. It supports the same tasks as YOLOv8, ensuring a smooth transition for existing users. A key improvement is enhanced feature extraction, allowing for more precise detail capture. YOLO11 models are optimized for both edge devices and cloud deployment, offering flexibility across different hardware setups.
Performance Metrics: YOLO11 outperforms YOLOv5 in terms of accuracy, achieving a higher mAP of 39.5 to 54.7 on the COCO dataset. Notably, YOLO11m achieves a higher mAP with 22% fewer parameters than YOLOv8m. Inference speeds remain competitive, and the reduced parameter count contributes to faster processing and lower computational costs.
Use Cases: YOLO11 is particularly well-suited for applications where high accuracy is paramount without sacrificing speed, including:
- Medical image analysis for enhanced diagnostics, such as tumor detection.
- Smart retail inventory management for improved efficiency and customer satisfaction.
- Advanced security systems, requiring precise and reliable object detection.
- Traffic management systems for optimized urban mobility.
Comparison Table
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 |
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 |
Conclusion
Both YOLOv5 and YOLO11 are excellent choices for object detection, each with its strengths. YOLOv5 remains a highly efficient and versatile model, suitable for a wide range of applications where speed is critical. YOLO11 elevates the performance bar, offering superior accuracy and efficiency, making it ideal for applications demanding the highest levels of precision.
For users seeking cutting-edge performance, YOLO11 is the recommended choice. However, YOLOv5 continues to be a robust and widely supported option, particularly for resource-constrained environments or applications where development speed and ease of use are paramount.
Consider exploring other models in the Ultralytics Model Docs, such as YOLOv8, YOLOv7, and YOLOv6 to find the model that best fits your specific needs. You can also find more information and contribute to the project on the Ultralytics GitHub repository.