👋 Hello @ZHIYUAN816, thank you for your interest in Ultralytics 🚀! We recommend checking out the Docs, where you'll find detailed explanations of various features, including segmentation, along with Python and CLI usage examples. Many of your questions might already be answered there.

If this is a ❓ Question about how segmentation works in YOLOv11, could you please elaborate on the details you are looking for or clarify your use case? Specific structural or implementation-related questions may also be addressed in the source code or through our reference guides.

If this request relates to a specific 🐛 Bug Report or feature request, we kindly ask you to share additional context or details, such as any implementation issues or specific outputs you're aiming to achieve. A minimum reproducible example would also be very helpful for debugging, where applicable.

Join the Ultralytics community where it suits you best to discuss this topic further:

• For real-time interaction, visit our Discord 🎧.
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Upgrade

Consider upgrading to the latest version of the ultralytics package to ensure you're benefiting from all recent updates and fixes. Run the following in a Python>=3.8 environment with PyTorch>=1.8:

pip install -U ultralytics

Environments

YOLOv11 can be run in various environments, including notebooks (with free GPUs), cloud platforms, and Docker. Choose any of the options outlined below to get started:

• Notebooks with GPU support:
• Google Cloud Deep Learning VM: See GCP Quickstart Guide.
• Amazon Deep Learning AMI: See AWS Quickstart Guide.
• Docker Image: Check out the Docker Quickstart Guide .

Status

If this badge is green, all Ultralytics CI tests are currently passing. These CI tests verify the correctness of all Modes and Tasks across macOS, Windows, and Ubuntu on a 24-hour schedule and after every commit.

This is an automated response to help address your query quickly. An Ultralytics engineer will also assist you soon. Let us know if there's any further detail you'd like to share in the meantime 😊!

@ZHIYUAN816 yOLO11 achieves segmentation by leveraging a variant of the U-Net architecture, enabling it to segment images into regions with high accuracy and speed. While we currently don't provide structural diagrams directly, you can explore more about segmentation and its implementation in our documentation here: https://docs.ultralytics.com/tasks/. For detailed inquiries, feel free to engage in the Ultralytics GitHub Discussions.