YOLO11 vs YOLOv5: A Detailed Comparison

This page provides a technical comparison between two popular object detection models: Ultralytics YOLO11 and Ultralytics YOLOv5, both developed by Ultralytics. We will analyze their architectures, performance metrics, and use cases to help you choose the right model for your computer vision needs, emphasizing the advancements and benefits offered by the Ultralytics ecosystem.

Ultralytics YOLO11

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

Ultralytics YOLO11 is the latest state-of-the-art object detection model from Ultralytics, building upon previous YOLO versions like YOLOv8 to offer enhanced performance and flexibility. It is designed for speed and accuracy across various vision tasks, including object detection, instance segmentation, image classification, and pose estimation.

Architecture and Key Features

YOLO11 introduces several architectural improvements, focusing on efficiency and higher accuracy. While specific architectural details are continuously evolving, YOLO11 generally emphasizes refined backbone networks and optimized head designs to boost detection capabilities without significantly increasing computational cost. It leverages advancements in network architecture to achieve better feature extraction and more efficient processing. A key advantage is its versatility, supporting multiple tasks within a single framework. For more in-depth architectural insights, refer to the Ultralytics documentation.

Strengths

• High Accuracy: Achieves superior mAP scores compared to YOLOv5, offering state-of-the-art precision.
• Efficient Inference: Provides an excellent balance of speed and accuracy, often faster than YOLOv5 at similar accuracy levels, especially on CPU.
• Versatility: Natively supports multiple computer vision tasks beyond detection, simplifying complex pipelines.
• Ease of Use: Benefits from the streamlined Ultralytics API, extensive documentation, and readily available pre-trained weights for efficient training.
• Well-Maintained Ecosystem: Actively developed and supported by Ultralytics, ensuring regular updates and strong community backing via GitHub and Discord.
• Memory Efficiency: Generally requires less memory for training and inference compared to larger architectures like transformers.

Weaknesses

• Larger YOLO11 models (l, x) require more computational resources than smaller variants or YOLOv5 models.
• Being newer, the community adoption is still growing compared to the established YOLOv5.

Learn more about YOLO11

Ultralytics YOLOv5

Author: Glenn Jocher
Organization: Ultralytics
Date: 2020-06-26
GitHub Link: https://github.com/ultralytics/yolov5
Docs Link: https://docs.ultralytics.com/models/yolov5/

Ultralytics YOLOv5 quickly became an industry favorite after its release due to its exceptional balance of speed, accuracy, and ease of use. Built on PyTorch, it is known for its straightforward training and deployment process.

Architecture and Key Features

YOLOv5 features a CSPDarknet53 backbone, PANet path aggregation network, and a YOLOv5 detection head. It was one of the first models to offer a wide range of sizes (n, s, m, l, x), providing excellent scalability for different hardware and performance needs, from edge devices to cloud servers. Its architecture is optimized for fast inference.

Performance Metrics Analysis

The table below compares YOLO11 and YOLOv5 models based on key performance metrics using the COCO dataset. YOLO11 models generally achieve higher mAP^val scores with comparable or better speeds, especially the smaller variants like YOLO11n, which significantly outperforms YOLOv5n in accuracy while being faster on CPU.

Strengths

• Exceptional Speed: Highly optimized for fast inference, particularly the smaller models (n, s).
• Ease of Use and Deployment: Renowned for its simplicity, making it easy to train and deploy, supported by extensive tutorials.
• Mature Ecosystem: Benefits from a large, active community, abundant resources, and proven stability in production environments.
• Scalability: Offers various model sizes suitable for diverse hardware constraints.

Weaknesses

• Lower Accuracy: Generally achieves lower mAP scores compared to the newer YOLO11 models.
• Limited Native Task Support: Primarily focused on object detection, requiring separate models or modifications for tasks like segmentation or pose estimation, unlike the integrated approach of YOLO11.

Learn more about YOLOv5

Ideal Use Cases

• YOLO11: Best suited for applications demanding the highest accuracy and versatility across multiple vision tasks, such as advanced robotics, high-resolution analysis in healthcare, or complex manufacturing inspection systems. Its efficiency also makes it viable for many real-time scenarios.
• YOLOv5: Remains an excellent choice for applications where inference speed is the absolute priority, especially on resource-constrained edge devices like Raspberry Pi or mobile applications. Its maturity makes it reliable for projects needing stability and extensive community support.

Conclusion

Both YOLO11 and YOLOv5 are powerful models developed by Ultralytics, each offering distinct advantages. YOLO11 represents the cutting edge, providing superior accuracy and versatility within the streamlined Ultralytics ecosystem. YOLOv5 remains a highly relevant and efficient option, particularly valued for its speed and maturity. The choice depends on the specific project requirements regarding accuracy, speed, task versatility, and resource availability.

For users exploring other options, Ultralytics also offers models like YOLOv9 and YOLOv10, along with comparisons to other architectures like RT-DETR and EfficientDet.

📅 Created 1 year ago ✏️ Updated 1 month ago

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