Feature Overview
This work is to research novel techniques to bring to InstructLab to add the ability to fine-tune vision models. This feature will enable users to train and customize models for image recognition tasks, expanding the platform's capabilities and user base.

Goals

• Enable users to fine-tune vision models within the InstructLab.
• Expand the InstructLab primary use cases to include image recognition.
• Leverage existing features such as model training and customization tools.

Requirements

• Explore support for popular vision model architectures.
• Ensure seamless integration with existing InstructLab features and tools.
• Research standards for vision model fine-tuning and evaluation.

Background

InstructLab's current capabilities focus on text-based models. Extending the platform to support vision models will allow it to cater to a broader range of users and applications, including computer vision, object detection, and image classification tasks.

Done

• [ ] Support for popular vision model architectures is integrated.
• [ ] InstructLab's existing features and tools are seamlessly integrated with the new vision model fine-tuning capability.
• [ ] The new feature extends model fine-tuning and evaluation to vision models.

Questions to Answer

How can we ensure the new feature is user-friendly and accessible to non-specialized users while maintaining value for image recognition specialists and researchers?

Out of Scope

• Detailed implementation of specific vision model architectures.
• Integration with external libraries or APIs for advanced vision model training.

Customer Considerations

• Ensure the new feature is easy to use and understand for users with varying levels of technical expertise.
• Provide clear documentation and tutorials on how to fine-tune vision models within InstructLab.