GPT-4o Vision Fine-Tuning: A Guide With Examples

OpenAI's latest fine-tuning API now allows image-based customization of GPT-4o, extending its capabilities beyond text. This tutorial demonstrates fine-tuning GPT-4o to identify Georgian Orthodox churches using images.

Fine-Tuning GPT-4o with Images

With a prepared JSONL file (containing image-text pairs), log into your OpenAI dashboard and select "Create":

In the creation menu:

1. Select the gpt-4o-2024-08-06 model.
2. Upload your training JSONL file.
3. Optionally adjust hyperparameters; otherwise, use the defaults.

The fine-tuning process begins automatically:

My fine-tuning (9 epochs) took about 20 minutes. Completion time varies based on dataset size and model complexity.

Testing the Fine-Tuned Model

Access your fine-tuned model via the API or Playground. This example uses the Playground:

The image shows the fine-tuned model (right) correctly identifying a Georgian Orthodox church—an image not included in the training data—while the original model (left) fails.

Conclusion

This tutorial showcased image-based fine-tuning of GPT-4o. We addressed the model's limitations by training it with image-text pairs and used OpenAI's API. The resulting model demonstrated improved accuracy. This approach is applicable to various image-related tasks. Refer to OpenAI's announcement for further use cases.

Further Learning:

• OpenAI Model Distillation: A Guide With Examples
• Working with the OpenAI API Course
• GPT-4o API Tutorial

The above is the detailed content of GPT-4o Vision Fine-Tuning: A Guide With Examples. For more information, please follow other related articles on the PHP Chinese website!