Vision fine-tuning

Fine-tune models for better image understanding.

Vision fine-tuning uses image inputs for supervised fine-tuning to improve the model's understanding of image inputs. This guide will take you through this subset of SFT, and outline some of the important considerations for fine-tuning with image inputs.

Data format

Just as you can send one or many image inputs and create model responses based on them, you can include those same message types within your JSONL training data files. Images can be provided either as HTTP URLs or data URLs containing Base64-encoded images.

Here's an example of an image message on a line of your JSONL file. Below, the JSON object is expanded for readability, but typically this JSON would appear on a single line in your data file:

{
  "messages": [
    {
      "role": "system",
      "content": "You are an assistant that identifies uncommon cheeses."
    },
    {
      "role": "user",
      "content": "What is this cheese?"
    },
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "https://upload.wikimedia.org/wikipedia/commons/3/36/Danbo_Cheese.jpg"
          }
        }
      ]
    },
    {
      "role": "assistant",
      "content": "Danbo"
    }
  ]
}

Uploading training data for vision fine-tuning follows the same process described here.

Image data requirements

Size

• Your training file can contain a maximum of 50,000 examples that contain images (not including text examples).
• Each example can have at most 10 images.
• Each image can be at most 10 MB.

Format

• Images must be JPEG, PNG, or WEBP format.
• Your images must be in the RGB or RGBA image mode.
• You cannot include images as output from messages with the assistant role.

Content moderation policy

We scan your images before training to ensure that they comply with our usage policy. This may introduce latency in file validation before fine-tuning begins.

Images containing the following will be excluded from your dataset and not used for training:

• People
• Faces
• Children
• CAPTCHAs

What to do if your images get skipped

Your images can get skipped during training for the following reasons:

• contains CAPTCHAs, contains people, contains faces, contains children
  • Remove the image. For now, we cannot fine-tune models with images containing these entities.
• inaccessible URL
  • Ensure that the image URL is publicly accessible.
• image too large
  • Please ensure that your images fall within our dataset size limits.
• invalid image format
  • Please ensure that your images fall within our dataset format.

Best practices

Reducing training cost

If you set the detail parameter for an image to low, the image is resized to 512 by 512 pixels and is only represented by 85 tokens regardless of its size. This will reduce the cost of training. See here for more information.

{
  "type": "image_url",
  "image_url": {
    "url": "https://upload.wikimedia.org/wikipedia/commons/3/36/Danbo_Cheese.jpg",
    "detail": "low"
  }
}

Control image quality

To control the fidelity of image understanding, set the detail parameter of image_url to low, high, or auto for each image. This will also affect the number of tokens per image that the model sees during training time, and will affect the cost of training. See here for more information.

Next steps

Now that you know the basics of vision fine-tuning, explore these other methods as well.

[

Supervised fine-tuning

Fine-tune a model by providing correct outputs for sample inputs.

](/docs/openai/guides/supervised-fine-tuning)[

Direct preference optimization

Fine-tune a model using direct preference optimization (DPO).

](/docs/openai/guides/direct-preference-optimization)[

Reinforcement fine-tuning

Fine-tune a reasoning model by grading its outputs.

](/docs/openai/guides/reinforcement-fine-tuning)