YOLOv10 vs YOLO11: A Detailed Comparison
Ultralytics YOLOv10 and YOLO11 represent the cutting edge in real-time object detection technology, each model tailored to specific needs within the computer vision landscape. This page provides a comprehensive technical comparison to help users understand the key differences and choose the model best suited for their applications.
Both models are successors in the YOLO (You Only Look Once) series, renowned for their speed and accuracy. However, they diverge in architectural focuses and performance optimizations, making them suitable for distinct use cases. YOLO11, announced at YOLO Vision 2024, builds upon the YOLOv8 architecture, emphasizing enhanced accuracy and efficiency with a reduced parameter count. YOLOv10, on the other hand, prioritizes achieving even greater speed and efficiency, pushing the boundaries of real-time performance, and is the latest model in the YOLO family.
Architecture
YOLO11: This model refines the architecture of its predecessor, YOLOv8, focusing on improving feature extraction while reducing the number of parameters. This is achieved through architectural optimizations that enhance precision without significantly increasing computational cost. YOLO11 supports all the computer vision tasks of YOLOv8, including object detection, instance segmentation, and image classification.
YOLOv10: YOLOv10 introduces a novel architecture specifically engineered for minimal latency and maximal efficiency. It is designed to be an anchor-free detector, streamlining the detection process and reducing computational overhead. The architectural focus is on achieving state-of-the-art real-time object detection capabilities, making it exceptionally fast and efficient for edge deployments.
Performance Metrics
When comparing performance, key metrics include mAP (mean Average Precision), inference speed, and model size.
mAP (Mean Average Precision): YOLO11 generally achieves a higher mAP compared to previous YOLO versions and maintains competitive accuracy while reducing model size. YOLOv10, while also highly accurate, prioritizes speed, which may result in a slightly lower mAP in favor of significantly faster inference times. Refer to the YOLO Performance Metrics guide for a detailed understanding of mAP and other metrics.
Inference Speed: YOLOv10 is engineered for superior inference speed, making it one of the fastest models in the YOLO family. It is optimized for real-time applications where low latency is critical. YOLO11, while also fast, is designed to balance speed with accuracy, offering a robust performance profile suitable for a wide range of applications.
Model Size: Both YOLOv10 and YOLO11 are designed to be efficient in terms of model size. YOLO11 reduces parameters compared to YOLOv8, and YOLOv10 further optimizes for a smaller footprint, making both models suitable for deployment on resource-constrained devices. Model pruning and quantization techniques can further reduce model size and improve inference speed for both models.
Use Cases and Applications
YOLO11: Ideal for applications requiring a balance of high accuracy and reasonable speed. Use cases include:
- Quality Control in Manufacturing: Detecting defects with high precision in real-time. Explore AI in manufacturing.
- Medical Imaging: Assisting in medical image analysis for diagnostics. Learn about AI in healthcare and medical image analysis.
- Advanced Security Systems: Enhancing security with accurate object detection and recognition. See computer vision for theft prevention.
YOLOv10: Best suited for applications where real-time processing and speed are paramount. Applications include:
- Autonomous Vehicles: Enabling fast and reliable object detection for safe navigation. Discover AI in self-driving cars.
- Robotics: Providing real-time vision for robotic systems in dynamic environments. Learn about robotics and edge AI.
- Real-time Analytics in Smart Cities: Analyzing traffic and urban environments for immediate insights. Explore AI in smart cities and AI in transportation.
Strengths and Weaknesses
YOLO11 Strengths:
- High Accuracy: Delivers excellent object detection accuracy, suitable for complex tasks.
- Efficient Parameter Usage: Achieves strong performance with a relatively small model size.
- Versatility: Supports a wide range of computer vision tasks.
YOLO11 Weaknesses:
- Inference Speed: While fast, it may not be as latency-optimized as YOLOv10 for extremely real-time critical applications.
YOLOv10 Strengths:
- Exceptional Speed: Offers the fastest inference speeds within the YOLO family.
- Highly Efficient: Optimized for minimal computational resources, ideal for edge devices.
- Anchor-Free Architecture: Simplifies the model and enhances speed.
YOLOv10 Weaknesses:
- Accuracy Trade-off: To achieve maximum speed, there might be a slight compromise in absolute accuracy compared to YOLO11 in certain scenarios.
Model Comparison Table
Model | size (pixels) |
mAPval 50-95 |
Speed CPU ONNX (ms) |
Speed T4 TensorRT10 (ms) |
params (M) |
FLOPs (B) |
---|---|---|---|---|---|---|
YOLOv10n | 640 | 39.5 | - | 1.56 | 2.3 | 6.7 |
YOLOv10s | 640 | 46.7 | - | 2.66 | 7.2 | 21.6 |
YOLOv10m | 640 | 51.3 | - | 5.48 | 15.4 | 59.1 |
YOLOv10b | 640 | 52.7 | - | 6.54 | 24.4 | 92.0 |
YOLOv10l | 640 | 53.3 | - | 8.33 | 29.5 | 120.3 |
YOLOv10x | 640 | 54.4 | - | 12.2 | 56.9 | 160.4 |
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 |
For users interested in exploring other models, Ultralytics also offers YOLOv8 and YOLOv9, each with its own strengths and optimizations. Check out the Ultralytics Models documentation for more details. You can also visit the Ultralytics GitHub repository for the latest updates and contributions.