How to Fine-Tune GPT-4o for Object Detection

On October 1st, 2024, OpenAI announced support for fine-tuning GPT-4o with vision capabilities. This allows you to customize a version of GPT-4o specifically for tasks where the base model might struggle, such as accurately identifying objects in complex scenes or recognizing subtle visual patterns. While GPT-4o demonstrates impressive general vision capabilities, fine-tuning allows you to enhance its performance for niche or specialized applications.

The Roboflow team has experimented extensively with fine-tuning models, and we are excited to share how we trained GPT-4o, and how you can train your own GPT-4o vision model for object detection. Object detection is a task that the base GPT-4o model finds challenging without fine-tuning.

In this guide, we will demonstrate how to fine-tune GPT-4o using a playing card dataset. While seemingly straightforward, this dataset presents several important challenges for vision language models: dozens of object classes, multiple objects in each image, class names consisting of multiple words, and a high degree of visual similarity between objects.

GPT-4o expects data in a specific format, as shown below. <IMAGE_URL> should be replaced with an HTTP link to your image, while <USER_PROMPT> and <MODEL_ANSWER> represent the user's query about the image and the expected response, respectively.  Depending on the vision-language task, these could be, for example, a question about the image and its corresponding answer in the case of Visual Question Answering (VQA).

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