Vision Fine-Tuning

Fine-tune pre-trained models (e.g., GPT-4o) with minimal data (as few as 100 images) to specialize visual AI for domain-specific tasks: maximum performance, minimum overhead.

Vision Fine-Tuning is the rapid, targeted adaptation of a broad, pre-trained model (like ResNet or GPT-4o) to a highly specific visual task. This process leverages the model’s existing general feature knowledge, then refines it using a small, specialized dataset: a powerful transfer learning approach. The efficiency is a game-changer: you can achieve significant performance gains—for instance, Automat saw a 272% uplift in its RPA agent’s success rate—with datasets as small as 100 images. This method cuts training costs and time by up to 90%, delivering tailored, high-accuracy solutions for critical applications, from medical image diagnosis to autonomous vehicle object detection.

https://openai.com/blog/introducing-vision-to-the-fine-tuning-api