Step-by-Step Tutorial: How to Fine-Tune Foundation Models for Training Data Labeling

Introduction:

Welcome to this step-by-step tutorial on fine-tuning Foundation models to auto-label training data based on the webinar transcript provided by Encord. In this tutorial, we will cover the process of fine-tuning Foundation models for auto-labeling trading data.

Step 1: Understanding Foundation Models

• Foundation models have become essential in AI, especially after the release of large language models like chat gbt.
• Visual Foundation models (VFMs) have gained popularity in computer vision, offering predictive capabilities based on prompts.

Step 2: Types of VFMs:

• Predictive VFMs: Predict outcomes based on prompts.
• Generative CVFMs: Generate images from text.
• Multi-modal VFMs: Convert text to images or vice versa.

Step 3: Innovations and Models:

• Meta introduced Dyno V2 and Segment Anything model in the predictive model category.
• Other AI powerhouses like OpenAI, Microsoft, and Google are working on generative and multimodal models.

Step 4: Building VFMs:

• Create, curate, and annotate a diverse dataset.
• Develop a scalable model architecture supporting various applications.
• Pre-train the model with generalization in mind.

Step 5: Fine-Tuning Decision Factors:

• Consider robustness to errors and complexity/diversity of the use case.
• Decide whether to use VFMs out of the box or fine-tune them for specific applications.

Step 6: Fine-Tuning Experiment:

• Meta created 11 million images and fine-tuned the Segment Anything model.
• Annotated images manually and semi-automated the labeling process.
• Developed a promptable model architecture for the Segment Anything model.

Step 7: Model Fine-Tuning Process:

• Understand the model architecture and pre-train the model.
• Select a fine-tuning strategy based on cost, infrastructure, and deployment requirements.
• Evaluate the model performance after fine-tuning.

Step 8: Data Exploration and Selection:

• Use tools like Active Learning to prioritize data for annotation and correction.
• Curate data subsets for training and fine-tuning based on performance metrics.

Step 9: Collab Network and Fine-Tuning:

• Use Collab notebooks to pre-process data and start the fine-tuning process.
• Fine-tune specific parts of the model architecture based on the use case requirements.

Step 10: Performance Evaluation:

• Evaluate the model performance using metrics like Mean Average Precision (MAP) and Intersection over Union (IOU).
• Compare the results of the fine-tuned model with the baseline model to assess performance improvements.

Step 11: Deployment and Monitoring:

• Deploy the fine-tuned model for combined models cautiously.
• Monitor the model's performance post-deployment to ensure it meets the desired objectives.

Conclusion:

By following these steps, you can effectively fine-tune Foundation models for auto-labeling training data, ensuring improved model performance and accuracy in various use cases.

This tutorial provides a comprehensive guide on how to fine-tune Foundation models for auto-labeling training data based on the insights from the Encord webinar.