Vision Models with Ollama

What This Article Covers

• Which vision models Ollama offers.
• How to analyze images via the API or CLI.
• Prompting tips.
• Common use cases.
• Pitfalls and limitations.

Introduction: Vision Models with Ollama

Ollama supports not just text models, but also vision models that can process images. With them, you can describe images, answer questions about their content, extract text from images, or recognize objects. Everything runs locally, without sending images to cloud services. This is particularly privacy-friendly.

This article shows which vision models Ollama offers, how to install them, and how to analyze images.

Key Terms

• Vision model: A language model that additionally processes images.
• Multimodal: A combination of text and image as input.
• CLIP: A method that maps images and text into a shared space.
• LLaVA: A well-known open-source vision model.
• OCR: Optical character recognition, extracting text from images.
• Image token: The internal representation of an image for the model.

Available Vision Models

Ollama offers several multimodal models. Common examples include:

• llava: A popular vision model with various variants available.
• llava-phi3: A faster variant.
• moondream: A very small, fast model for simple image analysis.
• bakllava: Another variant with good performance.

Download:

ollama pull llava
ollama pull moondream

Analyzing Images via the CLI

Ollama lets you pass images directly as an argument:

ollama run llava "Describe this image." ./bild.png

Or interactively:

ollama run llava

Then specify the image path in your prompt.

Analyzing Images via the REST API

curl http://localhost:11434/api/chat -d '{
  "model": "llava",
  "messages": [
    {
      "role": "user",
      "content": "Describe the content of this image.",
      "images": ["BASE64_ENCODED_IMAGE"]
    }
  ],
  "stream": false
}'

The image must be passed as Base64-encoded data.

Python Example

import requests
import base64

with open("bild.png", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode()

url = "http://localhost:11434/api/chat"
payload = {
    "model": "llava",
    "messages": [
        {
            "role": "user",
            "content": "Describe the image in three sentences.",
            "images": [image_b64]
        }
    ],
    "stream": False
}

resp = requests.post(url, json=payload)
print(resp.json()["message"]["content"])

Prompting for Images

• Be specific: “What objects are in the foreground?”
• Extract text: “Read the text in the image.”
• Summarize: “Summarize the content.”
• Compare: If you have multiple images, describe the differences.

Use Cases

• OCR: Extract text from screenshots, documents, or forms.
• Image description: For accessibility or cataloging.
• Object recognition: Summarize the content of images.
• Code from screenshots: Recognize and describe UI elements.
• Data analysis: Interpret diagrams and graphs.

Performance and Resources

Vision models require significantly more memory than pure text models. Image processing generates additional tokens and demands GPU or CPU resources. Starting a vision model can take longer than a pure text model.

Tips

• Use smaller vision models like moondream for quick tasks.
• Don’t send overly large images to save tokens.
• Ask specific questions instead of expecting vague descriptions.
• Prefer GPU, since CPU inference is slower for vision tasks.
• Verify results, as vision models aren’t always accurate.

Common Pitfalls

• Wrong model: Not every Ollama model supports images.
• Image too large: The model has context or image size limits.
• Base64 errors: Image not correctly encoded.
• Slow responses: Vision requires more VRAM and compute time.
• Incorrect API structure: Use the images field in the messages object.

Further Reading

• BotServ.de Ollama Commands
• BotServ.de Ollama REST API
• BotServ.de Ollama Performance
• BotServ.de Multimodal AI

FAQ: Vision Models with Ollama

Which vision model do you recommend? llava for good quality, moondream for fast analysis.

Can Ollama process multiple images at once? Yes, by passing multiple Base64 strings in the images field.

Do I need a GPU? Recommended, but not required. CPU inference is slower.

What image formats are supported? Typically PNG, JPEG, and GIF, depending on the model.

Are vision models compliant with data protection regulations? Yes, since everything is processed locally.

Sources and Further Reading

• Ollama Vision Docs: https://github.com/ollama/ollama/blob/main/docs/
• LLaVA: https://llava-vl.github.io/
• Moondream: https://moondream.ai/

Summary: Vision Models with Ollama

Ollama enables local image analysis with vision models like llava or moondream. Through the CLI or REST API, you can describe images, extract text, or recognize objects. Key requirements include proper Base64 encoding, sufficient VRAM, and concrete prompts. Vision models demand more resources than pure text models, but they offer many applications from OCR to image description. If you want to process image data locally, you’ll benefit especially from privacy protection and independence from cloud services.