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YOLO11 vs YOLOv5: A Technical Evolution in Object Detection

Choosing the right object detection model is a critical decision that balances the need for accuracy, speed, and ease of deployment. This page offers a comprehensive technical comparison between two landmark models from Ultralytics: the state-of-the-art Ultralytics YOLO11 and the widely adopted Ultralytics YOLOv5. While YOLOv5 set an industry standard for its performance and usability, YOLO11 represents the next evolutionary step, delivering superior accuracy, enhanced versatility, and the latest architectural innovations, all within the robust and user-friendly Ultralytics ecosystem.

Ultralytics YOLO11: The New State-of-the-Art

YOLO11, authored by Glenn Jocher and Jing Qiu, is the latest and most advanced model in the Ultralytics YOLO series. Released in 2024, it builds upon the strong foundation of its predecessors like YOLOv8 to set a new benchmark in performance and efficiency. It is engineered not just for object detection but as a comprehensive framework for a multitude of computer vision tasks.

Architecture and Key Features

YOLO11 introduces significant architectural refinements, including an anchor-free detection head and an optimized network structure. This modern design choice simplifies the training process by eliminating the need to pre-define anchor boxes, leading to better generalization on diverse datasets. The model achieves a higher mean Average Precision (mAP) than YOLOv5 with fewer parameters and computational requirements (FLOPs) in many cases, demonstrating superior efficiency.

A standout feature of YOLO11 is its versatility. It is a unified framework that natively supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding boxes (OBB). This multi-task capability makes it an incredibly powerful and flexible tool for complex AI systems.

Strengths

  • State-of-the-Art Accuracy: Delivers significantly higher mAP scores compared to YOLOv5, establishing a new performance standard.
  • High Efficiency: Achieves better accuracy with a more efficient architecture, often requiring fewer parameters and FLOPs.
  • Anchor-Free Design: Simplifies training and improves performance by removing the dependency on anchor box configuration.
  • Multi-Task Versatility: A single framework for a wide range of vision tasks, streamlining development for multifaceted applications.
  • Well-Maintained Ecosystem: Benefits from continuous development, extensive documentation, strong community support, and seamless integration with tools like Ultralytics HUB for MLOps.
  • Training Efficiency: Offers efficient training processes with readily available pre-trained weights and generally requires lower memory usage than more complex architectures like transformers.

Weaknesses

  • As a cutting-edge model, larger variants of YOLO11 can be computationally intensive, requiring modern GPU hardware for optimal performance.

Ideal Use Cases

YOLO11 is the ideal choice for new projects that demand the highest accuracy and flexibility:

  • Advanced Robotics: For precise object interaction and navigation in dynamic environments.
  • Industrial Automation: High-accuracy quality control and defect detection.
  • Healthcare: Assisting in medical image analysis for tasks like tumor detection.
  • Smart Cities: Powering complex systems for traffic management and public safety.

Learn more about YOLO11

YOLOv5: The Established and Versatile Workhorse

Released in 2020 by Glenn Jocher at Ultralytics, YOLOv5 quickly became one of the most popular object detection models in the world. It is celebrated for its exceptional balance of speed and accuracy, its ease of use, and its robust, well-documented implementation in PyTorch.

Architecture and Key Features

YOLOv5 uses an architecture based on a CSPDarknet53 backbone and a PANet neck for effective feature aggregation. Its detection head is anchor-based, which was a standard and effective approach at the time of its release. One of YOLOv5's greatest strengths is its scalability, offering a range of models from the tiny 'n' (nano) version to the large 'x' (extra-large) version, allowing developers to easily trade between speed and accuracy.

Strengths

  • Exceptional Inference Speed: Highly optimized for real-time performance, making it a go-to choice for applications on edge devices like NVIDIA Jetson.
  • Ease of Use: Renowned for its simple API, extensive tutorials, and streamlined training and deployment workflows.
  • Mature Ecosystem: Backed by a massive community, years of active development, and countless real-world deployments, ensuring stability and reliability.
  • Flexibility: The wide range of model sizes makes it adaptable to nearly any hardware constraint.

Weaknesses

  • Lower Accuracy: While still powerful, its accuracy is surpassed by newer models like YOLO11.
  • Anchor-Based Detection: Relies on predefined anchor boxes, which can sometimes require manual tuning for optimal performance on custom datasets compared to modern anchor-free detectors.

Ideal Use Cases

YOLOv5 remains an excellent choice for specific scenarios:

  • Edge Computing: Deploying on resource-constrained devices like a Raspberry Pi where inference speed is the top priority.
  • Rapid Prototyping: Its simplicity and speed make it perfect for quickly building and testing proof-of-concept applications.
  • Legacy Systems: Maintaining or updating existing projects built on the YOLOv5 framework.
  • Real-Time Surveillance: Powering security systems where high FPS is crucial.

Learn more about YOLOv5

Performance and Benchmarks: YOLO11 vs. YOLOv5

The performance metrics clearly illustrate the evolution from YOLOv5 to YOLO11. On the COCO dataset, YOLO11 models consistently achieve higher mAP scores than their YOLOv5 counterparts of similar size. For example, YOLO11m reaches 51.5 mAP, significantly outperforming YOLOv5m's 45.4 mAP. Furthermore, YOLO11 often does this with greater computational efficiency. Notably, the smallest model, YOLO11n, is faster on CPU than YOLOv5n while delivering a massive 11.5-point increase in mAP.

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

Conclusion: Which Model Should You Choose?

The choice between YOLO11 and YOLOv5 depends on your project's specific needs.

YOLOv5 is a proven, reliable, and incredibly fast model. It remains a fantastic option for applications where speed is the absolute priority, especially on older or resource-limited hardware. Its maturity and vast community support provide a stable foundation for many projects.

However, for nearly all new projects, YOLO11 is the clear and recommended choice. It represents a significant leap forward, offering state-of-the-art accuracy, superior efficiency, and unparalleled versatility. Its anchor-free architecture and native support for multiple vision tasks make it a more powerful, flexible, and future-proof solution. By choosing YOLO11, developers are leveraging the latest advancements in AI to build more capable and accurate computer vision applications, all while benefiting from the streamlined and well-maintained Ultralytics ecosystem.

Explore Other Model Comparisons

If you're interested in how these models stack up against other leading architectures, check out our other comparison pages:



📅 Created 1 year ago ✏️ Updated 1 month ago
glenn-jocher

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