Image Understanding with Ollama LLaVA Using Python

🖼 Image Understanding Using Ollama (LLaVA) with Python

In this post, we build a simple yet powerful Python script that sends an image to an Ollama vision model and receives a detailed, factual description of what is visible in the image.

This approach is useful for image analysis, OCR preprocessing, accessibility tools, AI automation, and vision-based applications.

---

🧠 What This Project Does

• ✔ Reads an image file from your system
• ✔ Converts the image to Base64 format
• ✔ Sends the image to an Ollama vision model
• ✔ Returns a detailed description of visible content
• ✔ Avoids assumptions or inferred information

---

🖥 System Requirements (Mandatory)

Before running this project, ensure your system meets the following requirements:

• Operating System: Windows 10+, Linux, or macOS
• Python: Python 3.9 or higher
• RAM: Minimum 8 GB (16 GB recommended)
• Storage: At least 10 GB free space for Ollama models
• CPU: Modern multi-core processor
• GPU: Optional (CPU-only works, GPU improves speed)

⚠ Ollama must be installed and running in the background.

---

🤖 Ollama Model Used & Why

This project uses the following Ollama model:

llava:7b

Why LLaVA?

• ✔ Multimodal (supports both image and text input)
• ✔ Designed for visual understanding
• ✔ Can describe objects, text, colors, and scenes
• ✔ Runs locally using Ollama

This makes it ideal for image-to-text and vision-based AI tasks.

---

⬇ Install Ollama

Download and install Ollama from the official website. After installation, verify it works:

ollama --version

---

📥 Download the Required Model

Pull the LLaVA vision model using:

ollama pull llava:7b

This will download the model locally.

---

📦 Install Required Python Dependencies

Install the Ollama Python client:

pip install ollama

The base64 module is part of Python’s standard library and does not require installation.

---

🧠 How the Image Is Processed

• The image is opened in binary mode
• Converted to Base64 encoding
• Sent to the Ollama API along with a prompt
• The model returns a textual description

---

🧪 Python Code: Image Description with Ollama

from ollama import chat
import base64

image_path = r"image.png"

# Convert image to base64
with open(image_path, "rb") as img:
    image_base64 = base64.b64encode(img.read()).decode("utf-8")

# Send image to Ollama vision model
response = chat(
    model="llava:7b",
    messages=[
        {
            "role": "user",
            "content": (
                "Describe the image in detail. Explain what is visible, "
                "including objects, text, people, actions, colors, and any "
                "noticeable details. Do not infer anything beyond what is clearly shown."
            ),
            "images": [image_base64]
        }
    ]
)

print(response["message"]["content"]) 

---

📌 Output Example

The model returns a detailed explanation of what is visible in the image, such as objects, colors, layout, and readable text (if present).

---

🚀 Use Cases

• ✔ Image-to-text conversion
• ✔ Accessibility tools
• ✔ AI document processing
• ✔ OCR + reasoning pipelines
• ✔ Vision-based automation

---

⚠ Limitations

• ❌ No external knowledge inference
• ❌ Image quality affects accuracy
• ❌ Large models require more RAM

---

🔚 Conclusion

This project demonstrates how easily you can perform image understanding locally using Ollama and LLaVA. It is private, offline, and highly customizable for real-world AI applications.

You can extend this further by combining it with OCR, RAG, or chatbot pipelines.

Happy building with vision AI 🚀