Implement Mixture of Experts in YOLOv8

[LaThanhTrong]

Hello, I have been given a task to modify and integrate MoE to YOLOv8 models. Currently, I don't understand the approach and how to implement them correctly.

[UltralyticsAssistant]

👋 Hello @LaThanhTrong, thank you for your interest in Ultralytics and for bringing your query here 🚀! We highly recommend exploring the Ultralytics Docs for valuable insights. Specifically, you might find the following sections helpful as you tackle your integration task:

• Check out our Model Training Tips to ensure you're employing the most effective training practices.
• Our Python Usage and CLI Usage guides could also provide relevant examples and instructions.

If you're encountering this as a 🐛 Bug or technical issue during the MoE integration, please provide a minimum reproducible example to help us understand and address it effectively.

For questions related to custom training or modifying models, it would be great to have more context, including any relevant code snippets or logs. This information helps us provide better guidance tailored to your specific needs.

While this is an automated response, rest assured that an Ultralytics engineer will be looking into your inquiry soon.

In the meantime, consider engaging with our community through various platforms. Join our Discord server for interactive discussions or visit our Discourse for more extensive exchanges. You can also dive into threads on our Subreddit to connect with fellow members.

Upgrade

Ensure you're using the latest version of the ultralytics package with all the necessary dependencies in a Python>=3.8 environment, particularly with PyTorch>=1.8:

pip install -U ultralytics

Environments

Consider running YOLO in these efficient environments, where dependencies like CUDA, CUDNN, Python, and PyTorch are pre-installed:

• Notebooks:
• Google Cloud: Learn more in the GCP Quickstart Guide
• Amazon: Follow the steps in the AWS Quickstart Guide
• Docker: Setup instructions are available in the Docker Quickstart Guide

Status

Check here to ensure that all Ultralytics CI tests are passing, as they confirm the correct operation of all YOLO Modes and Tasks across various platforms.

[glenn-jocher]

@LaThanhTrong implementing Mixture of Experts (MoE) in YOLOv8 requires modifying the model architecture to include expert layers. You might start by exploring PyTorch's MoE implementations and integrating them into the YOLOv8 model structure. For detailed guidance, reviewing the YOLOv8 architecture in the Ultralytics repository could be beneficial.