Voiced by Amazon Polly |
Overview
In the rapidly evolving landscape of artificial intelligence, the ability to extract structured data from images has become increasingly vital. Ollama’s integration of Llama 3.2 Vision offers a robust solution, enabling developers to harness advanced multimodal processing capabilities for various applications.
Pioneers in Cloud Consulting & Migration Services
- Reduced infrastructural costs
- Accelerated application deployment
Introduction
Available in 11B and 90B parameter sizes, it caters to diverse computational needs, balancing performance and resource requirements.
Key Features
Some of the standout features of Llama 3.2 Vision include:
- Multimodal Processing: Handles text and images, enabling tasks such as object recognition, image captioning, and data extraction.
- Instruction Tuning: Optimized for visual recognition, image reasoning, and captioning, enhancing its ability to understand and generate contextually relevant outputs.
- Model Sizes: The 11B model requires at least 8GB of VRAM, while the 90B model requires at least 64GB of VRAM, allowing flexibility based on available resources.
Data Extraction Capabilities
Llama 3.2 Vision excels in extracting structured data from images. It is particularly useful for:
- Text Recognition: Identifies and transcribes text within images, which is useful for processing documents, signs, or handwritten notes.
- Object Identification: Detects and labels objects, aiding inventory management and scene analysis.
- Information Retrieval: Extracts specific details, such as dates, names, or numerical data, from images.
Implementing Data Extraction with Ollama and Llama 3.2 Vision
Follow these steps to get started with Llama 3.2 Vision:
- Install Ollama: Ensure you have Ollama version 0.4 or higher.
- Download the Model: Use the command
ollama pull llama3.2-vision
to download the 11B model. - Run the Model: Execute
ollama run llama3.2-vision
to start the model. - Process Images: Input images into the model to extract desired data.
Example Usage
Here’s an example Python script using the Ollama library:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import base64 import ollama import json import sys def extract_data_from_image(image_path, extraction_instructions): # Initialize the Ollama client client = ollama.Client() # Read the image and encode it in base64 with open(image_path, 'rb') as image_file: image_data = image_file.read() encoded_image = base64.b64encode(image_data).decode('utf-8') # Prepare the message with the user-specified extraction instructions message = { 'role': 'user', 'content': f'Extract the following data from the image: {extraction_instructions}. Return the result as valid JSON. Do not include any additional text or explanations.', 'images': [encoded_image] } # Send the request to the model response = client.chat(model='llama3.2-vision', messages=[message]) # Get the model's response model_response = response['message']['content'] # Parse the response as JSON try: data = json.loads(model_response) except json.JSONDecodeError as e: print(f"Error parsing JSON for image {image_path}:", e) data = None return data if __name__ == "__main__": # Check if image paths and extraction instructions are provided as command-line arguments if len(sys.argv) > 2: # The first argument is the script name, the last argument is the extraction instructions image_paths = sys.argv[1:-1] extraction_instructions = sys.argv[-1] else: # Prompt the user to input image paths and extraction instructions image_paths = input('Enter the paths to your images, separated by commas: ').split(',') extraction_instructions = input('Enter the data you want to extract from the images: ') for image_path in image_paths: image_path = image_path.strip() data = extract_data_from_image(image_path, extraction_instructions) if data is not None: print(f"Data extracted from {image_path}:") print(json.dumps(data, indent=4)) else: print(f"No data extracted from {image_path}.") |
Considerations
- Resource Requirements: The 11B model requires at least 8GB of VRAM, while the 90B model requires at least 64GB of VRAM.
- Supported Languages: English is the primary language for image and text applications.
- Accuracy: The model’s performance may vary based on image quality and complexity.
Conclusion
By leveraging Ollama’s Llama 3.2 Vision, developers can integrate sophisticated data extraction functionalities into their applications, enhancing automation and data processing capabilities. This tool provides an invaluable resource for tasks ranging from document processing to object recognition.
Drop a query if you have any questions regarding Ollama’s Llama and we will get back to you quickly.
Empowering organizations to become ‘data driven’ enterprises with our Cloud experts.
- Reduced infrastructure costs
- Timely data-driven decisions
About CloudThat
CloudThat is an award-winning company and the first in India to offer cloud training and consulting services worldwide. As a Microsoft Solutions Partner, AWS Advanced Tier Training Partner, and Google Cloud Platform Partner, CloudThat has empowered over 850,000 professionals through 600+ cloud certifications winning global recognition for its training excellence including 20 MCT Trainers in Microsoft’s Global Top 100 and an impressive 12 awards in the last 8 years. CloudThat specializes in Cloud Migration, Data Platforms, DevOps, IoT, and cutting-edge technologies like Gen AI & AI/ML. It has delivered over 500 consulting projects for 250+ organizations in 30+ countries as it continues to empower professionals and enterprises to thrive in the digital-first world.
FAQs
1. What is Ollama Llama 3.2 Vision, and how does it work?
ANS: – Ollama Llama 3.2 Vision is a multimodal large language model (LLM) capable of processing textual and visual inputs. It leverages advanced machine learning techniques to extract structured data from images, perform text recognition, identify objects, and retrieve specific information based on instructions. Users can upload an image and provide a query, and the model processes the visual data to return structured responses.
2. What types of tasks can Llama 3.2 Vision handle?
ANS: – Llama 3.2 Vision can perform a variety of tasks, including:
- Text recognition from images (e.g., extracting text from scanned documents or photographs).
- Object detection and classification (e.g., identifying items in a scene).
- Structured data extraction includes dates, names, and numerical data.
- Generating image captions or descriptions.
WRITTEN BY Abhishek Mishra
Comments