/finetuning

Vision Fine-Tuning

Fine-tune models with image data to customize visual understanding. Uses the same chat-completions JSONL format as text SFT, but with image content blocks in user messages.

Supported Models

Image Requirements

Important: Images can only appear in user messages, never in assistant responses.

Data Format

Each training example follows the standard SFT messages format. Images are included as image_url content blocks within user messages.

{"messages": [{"role": "system", "content": "You are a helpful AI assistant that describes images."}, {"role": "user", "content": [{"type": "text", "text": "Describe this image."}, {"type": "image_url", "image_url": {"url": "https://example.com/photo.png", "detail": "high"}}]}, {"role": "assistant", "content": "The image shows a cityscape with tall buildings against a blue sky."}]}

Image Sources

Images can be provided in two ways:

1. Public URL:

{"type": "image_url", "image_url": {"url": "https://example.com/image.png"}}

2. Base64 data URI:

{"type": "image_url", "image_url": {"url": "data:image/png;base64,iVBORw0KGgo..."}}

Detail Control

The detail parameter controls image processing fidelity and cost:

{"type": "image_url", "image_url": {"url": "https://example.com/image.png", "detail": "low"}}

Use low for tasks where fine visual detail doesn't matter (classification, general description). Use high for tasks needing precise detail (OCR, diagram reading, defect detection).

Content Moderation

Images are screened before training. The following are automatically excluded:

• Images containing people or faces (face detection only — no identification)
• CAPTCHAs
• Content violating Azure usage policies

This screening may add latency to file upload validation.

Best Practices

• Diverse examples: Vary image content, angles, lighting, and resolution
• Consistent annotations: Keep assistant response style and detail level uniform
• Start with detail: low: Cheaper and faster — upgrade to high only if results need it
• Check for excluded images: After upload, verify the training count matches expectations — some images may be silently skipped due to content moderation
• Mixed text+image: You can include both text-only and image examples in the same training file

Training Workflow

Vision fine-tuning follows the exact same workflow as text SFT:

1. Prepare JSONL with image content blocks
2. Upload training file (validation may take longer due to image screening)
3. Create fine-tuning job with a supported vision model
4. Monitor and evaluate as usual

# Upload (image validation may take longer)
train_file = client.files.create(purpose="fine-tune", file=open("vision_train.jsonl", "rb"))
client.files.wait_for_processing(train_file.id)

# Submit — same as text SFT
job = client.fine_tuning.jobs.create(
    model="gpt-4.1-2025-04-14",
    training_file=train_file.id,
    validation_file=val_file.id,
    method={"type": "supervised"}
)

Troubleshooting

Reference

• Official docs