YOLO model

[mickycooky]

what the differents yolo11n, yolo11m etc.
i want to know why yolo11m better performance than yolo11n

[UltralyticsAssistant]

👋 Hello @mickycooky, thank you for your interest in Ultralytics and the YOLO models 🚀! To understand the differences between the various YOLO models like yolo11n and yolo11m, I recommend checking out our Docs. They provide detailed explanations about the model architectures and their performance characteristics.

If your inquiry involves the performance characteristics of different models, such as why yolo11m may show better performance than yolo11n, providing context and data, like specific use cases or results you've observed, can help us give more precise guidance.

If this is related to a 🐛 Bug Report, please provide a minimum reproducible example to help us diagnose any potential issues.

For additional resources or to engage with the community, you can join our Discord 🎧 for chat, browse Discourse for discussions, or jump into our Subreddit to share experiences.

Upgrade

Ensure you're using the latest ultralytics package along with all requirements in a Python>=3.8 environment with PyTorch>=1.8. You can upgrade using:

pip install -U ultralytics

Environments

YOLO is compatible with various environments that come preinstalled with essential dependencies:

• Run in Notebooks for free GPU access:
• Use Google Cloud Deep Learning VM via the GCP Quickstart Guide
• Opt for Amazon Deep Learning AMI using the AWS Quickstart Guide
• Try out our Docker Image as explained in the Docker Quickstart Guide

Status

Our CI tests ensure the stable operation of all YOLO Modes and Tasks across different operating systems.

Please note that this is an automated response, and an Ultralytics engineer will be with you shortly to provide further assistance. 😊

[glenn-jocher]

@mickycooky yOLO11m offers better performance than YOLO11n due to its larger model size, which allows for more complex feature extraction and higher accuracy.