This guide demonstrates how to fine-tune GPT-4o on Azure OpenAI for image classification using the Stanford Dogs dataset. It walks through preparing data, running batch inference with the Batch API, fine-tuning the model with the Vision Fine-Tuning API, and evaluating results. The fine-tuned model achieved 82.67% accuracy compared to 73.67% for the base model and 61.67% for a CNN baseline, with 9.6% faster latency. The tutorial includes practical considerations for cost, latency trade-offs, and provides a GitHub repository with complete implementation code and scripts.
Table of contents
What Is Image Classification and Why Is It Useful? Copy linkGetting Started: Choosing and Deploying Your Vision-Language Model on Azure Copy linkStep 1: Run Cost-Effective Batch Inference with Azure OpenAI Copy linkStep 2: Fine-Tune GPT-4o for Your Images Using the Vision API Copy linkStep 3: Compare Against a Classic CNN Baseline Copy linkResults at a Glance: Accuracy, Latency, and Cost Copy linkKey takeaways Copy linkNext Steps: How to Apply This in Your Own Projects Copy linkSort: