YOLOv10 vs YOLO11: Detailed Technical Comparison

This page offers a detailed technical comparison between two cutting-edge object detection models: YOLOv10 and Ultralytics YOLO11. Both models represent significant advancements in the YOLO (You Only Look Once) 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.

YOLOv10

YOLOv10, developed by Tsinghua University, emphasizes end-to-end real-time object detection by addressing post-processing bottlenecks and refining model architecture for enhanced efficiency and accuracy. A key innovation in YOLOv10 is the introduction of consistent dual assignments for NMS-free training, aiming to reduce inference latency while maintaining competitive performance.

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 and accuracy. It comprehensively optimizes various components within YOLO models to reduce computational redundancy and enhance detection capabilities, as detailed in their arXiv paper. The model is trained without Non-Maximum Suppression (NMS), simplifying deployment and reducing latency. This NMS-free approach, combined with architectural optimizations available in their PyTorch implementation, allows YOLOv10 to achieve state-of-the-art performance with improved efficiency.

Performance Metrics

YOLOv10 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, demonstrating a strong balance between speed and accuracy.

Strengths

• NMS-Free Training: Simplifies deployment and reduces inference latency.
• High Efficiency: Optimized architecture leads to reduced computational overhead.
• Competitive Performance: Achieves state-of-the-art accuracy across model scales.
• Real-Time Focus: Designed specifically for real-time end-to-end object detection.

Weaknesses

• Relatively New: Being a newer model, community support and integration within established ecosystems like Ultralytics might be less mature compared to YOLO11.
• Integration Effort: May require more effort to integrate into existing Ultralytics workflows compared to native models like YOLO11.

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 in object detection and tracking.
• Edge Computing with Latency Constraints: Deployments on edge devices where latency is critical.
• Real-Time Video Analytics: Scenarios requiring immediate analysis of video streams, like traffic management.
• Advanced Robotics: Robotics systems demanding rapid environmental perception.

Learn more about YOLOv10

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, YOLO11 incorporates architectural enhancements to optimize performance across different hardware platforms, from edge devices to cloud servers. It supports 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 and accuracy. 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 seamless integration within the well-maintained Ultralytics ecosystem. This includes a simple API, extensive documentation, active development, strong community support via GitHub and Discord, and readily available resources, ensuring a streamlined user experience. Furthermore, YOLO11 integrates smoothly with Ultralytics HUB for simplified training and deployment workflows.

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 for efficient training and faster inference, often requiring lower memory usage compared to transformer-based models.

Strengths

• High Speed and Efficiency: Excellent inference speed, suitable for real-time applications.
• Strong Accuracy: High mAP, especially with larger model variants, achieving a favorable performance balance.
• Versatility: Supports multiple computer vision tasks (detection, segmentation, classification, pose, OBB).
• User-Friendly Ecosystem: Seamless integration with the Ultralytics Python package and Ultralytics HUB, backed by extensive documentation and community support. Ease of use is paramount.
• Flexible Deployment: Optimized for various hardware platforms with efficient training processes and readily available pre-trained weights.
• Training Efficiency: Benefits from efficient training pipelines and lower memory requirements compared to many alternatives.

Weaknesses

• Speed-Accuracy Trade-off: Smaller models prioritize speed, potentially sacrificing some accuracy compared to larger variants.
• One-Stage Detector Limitations: Like other one-stage detectors, may face challenges with extremely small objects compared to two-stage detectors.

Ideal Use Cases

YOLO11 is highly versatile and ideal for a broad spectrum of real-time object detection applications:

• 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

Performance Comparison

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

Conclusion

Both YOLOv10 and Ultralytics YOLO11 are powerful object detection models, each offering distinct advantages. YOLOv10 excels with its NMS-free design, leading to potentially lower latency in end-to-end deployment scenarios.

However, Ultralytics YOLO11 stands out due to its versatility, supporting multiple vision tasks beyond detection, and its seamless integration into the comprehensive and user-friendly Ultralytics ecosystem. This ecosystem provides extensive documentation, active community support, a simple API, efficient training processes with lower memory needs, and tools like Ultralytics HUB, making YOLO11 an exceptionally easy-to-use and well-supported option for developers and researchers. For users seeking a robust, versatile, and easy-to-integrate model with strong performance balance and excellent support, Ultralytics YOLO11 is the recommended choice.

Explore Other Models

Users interested in YOLOv10 and YOLO11 might also find comparisons with other state-of-the-art models valuable:

• Ultralytics YOLOv8: A highly successful predecessor known for its balance of speed and accuracy. See YOLOv8 vs YOLO11 and YOLOv8 vs YOLOv10.
• YOLOv9: Introduced innovations like PGI and GELAN. Compare YOLOv9 vs YOLO11 and YOLOv9 vs YOLOv10.
• RT-DETR: An efficient real-time DETR model. See RT-DETR vs YOLO11 and RT-DETR vs YOLOv10.
• DAMO-YOLO: Another efficient YOLO variant. Compare DAMO-YOLO vs YOLO11 and DAMO-YOLO vs YOLOv10.

Exploring these comparisons can provide further context for selecting the best model for specific project requirements.

📅 Created 1 year ago ✏️ Updated 1 month ago

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