YOLO11 vs YOLOv10: Detailed Technical Comparison

This page offers a detailed technical comparison between two cutting-edge object detection models: Ultralytics YOLO11 and YOLOv10. Both models represent significant advancements in the YOLO series, renowned for real-time object detection. We will delve into their architectural nuances, performance benchmarks, and suitability for various applications, aiding you in selecting the optimal model for your computer vision needs.

Ultralytics YOLO11

Ultralytics YOLO11, the latest iteration from Ultralytics, is designed to excel in speed and accuracy for object detection tasks. Building upon previous YOLO models like YOLOv8, YOLO11 incorporates architectural enhancements to optimize performance across different hardware platforms, from edge devices to cloud servers. It stands out for its versatility, supporting a range of computer vision tasks including object detection, instance segmentation, image classification, and pose estimation.

Technical Details:

• Authors: Glenn Jocher, Jing Qiu
• Organization: Ultralytics
• Date: 2024-09-27
• GitHub Link: https://github.com/ultralytics/ultralytics
• Docs Link: https://docs.ultralytics.com/models/yolo11/

Architecture and Key Features

YOLO11 focuses on refining the balance between model size, accuracy, and speed. Key architectural improvements include enhanced feature extraction layers and a streamlined network structure to minimize computational overhead. This design facilitates efficient deployment on resource-constrained devices like Raspberry Pi and NVIDIA Jetson.

A major advantage of YOLO11 is its integration into the well-maintained Ultralytics ecosystem. This provides users with a streamlined user experience, a simple Python API, extensive documentation, and strong community support. The model benefits from efficient training processes, readily available pre-trained weights, and typically lower memory requirements during training and inference compared to alternatives like transformer models. Its versatility across multiple tasks (detection, segmentation, classification, pose, OBB) makes it a comprehensive solution.

Performance Metrics

YOLO11 offers a range of models (YOLO11n, YOLO11s, YOLO11m, YOLO11l, YOLO11x) to suit diverse performance needs. YOLO11n, the nano version, achieves a mAP^val 50-95 of 39.5 with only 2.6M parameters and a CPU ONNX speed of 56.1ms. The larger YOLO11x reaches a mAP^val 50-95 of 54.7, prioritizing accuracy. YOLO11 utilizes techniques like mixed precision training to further enhance inference speed.

Strengths

• High Speed and Efficiency: Excellent inference speed, suitable for real-time applications.
• Strong Accuracy: High mAP, especially with larger model variants.
• Versatile Task Support: Supports multiple computer vision tasks beyond object detection.
• User-Friendly Ecosystem: Seamless integration with the Ultralytics Python package and Ultralytics HUB. Ease of use is a key benefit.
• Flexible Deployment: Optimized for various hardware platforms.
• Performance Balance: Achieves a favorable trade-off between speed and accuracy.
• Training Efficiency: Efficient training and readily available pre-trained weights.

Weaknesses

• Speed-Accuracy Trade-off: Smaller models prioritize speed over accuracy.
• One-Stage Detector Limitations: May have challenges with very small objects compared to two-stage detectors.

Ideal Use Cases

YOLO11 is ideal for real-time object detection applications such as:

• Autonomous Systems: Self-driving cars, robotics.
• Security and Surveillance: Security alarm systems, theft prevention.
• Industrial Automation: Quality control in manufacturing, recycling efficiency.
• Retail Analytics: Inventory management, customer behavior analysis.

Learn more about YOLO11

YOLOv10

YOLOv10, developed by researchers at Tsinghua University, emphasizes end-to-end real-time object detection by addressing post-processing bottlenecks like Non-Maximum Suppression (NMS) and refining model architecture for enhanced efficiency and accuracy. A key innovation is the introduction of consistent dual assignments for NMS-free training, aiming to reduce inference latency.

Technical Details:

• Authors: Ao Wang, Hui Chen, Lihao Liu, et al.
• Organization: Tsinghua University
• Date: 2024-05-23
• Arxiv Link: https://arxiv.org/abs/2405.14458
• GitHub Link: https://github.com/THU-MIG/yolov10
• Docs Link: https://docs.ultralytics.com/models/yolov10/

Architecture and Key Features

YOLOv10's architecture is designed for holistic efficiency. It optimizes various components to reduce computational redundancy and enhance detection capabilities. The model is trained without NMS, simplifying deployment and potentially reducing latency. This NMS-free approach, combined with architectural optimizations, allows YOLOv10 to achieve competitive performance.

Performance Metrics

YOLOv10 also provides models of varying scales (YOLOv10n, YOLOv10s, YOLOv10m, YOLOv10b, YOLOv10l, YOLOv10x). For example, YOLOv10-S achieves a mAP^val of 46.7% with 7.2M parameters and a T4 TensorRT10 latency of 2.66ms. YOLOv10-X reaches 54.4% mAP^val with 56.9M parameters and 12.2ms latency.

Strengths

• NMS-Free Training: Simplifies deployment and reduces inference latency associated with NMS post-processing.
• High Efficiency: Optimized architecture leads to reduced computational overhead in some aspects.
• Competitive Performance: Achieves strong accuracy across model scales.
• Real-Time Focus: Designed specifically for real-time end-to-end object detection.

Weaknesses

• Relatively New: Being newer, community support and readily available resources within established ecosystems like Ultralytics might be less mature.
• Integration Effort: May require more effort to integrate into existing Ultralytics workflows compared to native models like YOLO11.
• Task Specificity: Primarily focused on object detection, lacking the built-in multi-task versatility of YOLO11 (segmentation, classification, pose).

Ideal Use Cases

YOLOv10 is particularly suited for applications requiring ultra-fast, end-to-end object detection, such as:

• High-Speed Object Tracking: Applications needing minimal latency.
• Edge Computing with Latency Constraints: Deployments where NMS overhead is critical.
• Real-Time Video Analytics: Scenarios requiring immediate analysis.
• Advanced Robotics: Systems demanding rapid environmental perception.

Learn more about YOLOv10

Performance Comparison

The table below provides a detailed comparison of performance metrics for various YOLO11 and YOLOv10 model variants on the COCO dataset.

Note: CPU ONNX speeds for YOLOv10 are not directly provided in the source material but YOLO11 demonstrates strong CPU performance.

Conclusion

Both Ultralytics YOLO11 and YOLOv10 are excellent choices for object detection, each with unique strengths. Ultralytics YOLO11 is highly recommended for users seeking a versatile, high-performance model integrated into a mature, easy-to-use ecosystem with extensive support and documentation. Its balance of speed, accuracy, multi-task capabilities, and efficient training makes it ideal for a wide range of real-world applications. For those prioritizing minimal latency via NMS-free deployment above all else, YOLOv10 presents a compelling alternative, though potentially requiring more integration effort and lacking the broad task support of YOLO11.

Users might also be interested in exploring other YOLO models such as YOLOv9 and comparing them against models like RT-DETR and DAMO-YOLO.

📅 Created 1 year ago ✏️ Updated 1 month ago

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