Blip 2

blip-2

Blip 2 is an AI model for converting image data into detailed and descriptive text.

A100 80GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~2 sec

StatusActive

Version

0.0.1

Updated14 days ago

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "blip-2",
            "version": "0.0.1",
            "input": {
  "image": "your_file.image/jpeg",
  "caption": false,
  "context": "your context here",
  "question": "your question here",
  "temperature": "1",
  "use_nucleus_sampling": false
}
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~2 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

BLIP-2 (Bootstrapped Language-Image Pre-training) is a cutting-edge AI model designed to bridge the gap between visual and textual understanding. Developed to enhance multimodal learning, BLIP-2 excels in generating captions, answering questions about images, and other tasks requiring a combination of language and visual processing.

Technical Specifications

Architecture: Combines a lightweight vision encoder with a pre-trained language model, enabling efficient cross-modal learning. Uses a Querying Transformer (Q-Former) to align visual and textual representations.
Training Dataset: Trained on a diverse dataset of image-text pairs, including web-crawled data and curated datasets, ensuring robust performance across various domains.
Multimodal Capabilities: Supports tasks like image captioning, visual question answering (VQA), and image-to-text generation.

Key Considerations

Image Quality: The model’s performance depends on the quality and clarity of the input image. High-resolution images yield better results.
Prompt Engineering: Crafting effective prompts is crucial for obtaining accurate and relevant outputs. Experiment with different phrasing to optimize results.

Tips & Tricks

Custom Queries: Use tailored prompts to extract specific information from images, such as "What is the brand of the car?" or "Describe the weather in the scene."
Input Requirements: Provide high-quality images for optimal results. Low-resolution or distorted images may affect performance.
Prompting: Use clear and specific textual prompts to guide the model’s responses. For example, "Describe the scene in the image" or "What objects are present?"

Capabilities

Visual Question Answering (VQA): Answers questions about the content of an image, providing insights and interpretations.
Image-to-Text Generation: Converts visual inputs into coherent and contextually relevant text outputs.

What can I use for?

Content Creation: Automate the generation of image descriptions for social media, e-commerce, or digital archives.
Accessibility Tools: Develop applications that assist visually impaired users by describing images or scenes.
Research and Analysis: Analyze visual data in fields like healthcare, autonomous driving, or environmental monitoring.

Things to be aware of

Creative Applications: Use the model to generate imaginative captions or narratives based on images.
Custom Queries: Test the model’s ability to answer specific questions about an image, such as "How many people are in the picture?"

Limitations

Contextual Understanding: While powerful, the model may struggle with highly abstract or context-dependent tasks.
Bias in Data: As with most AI models, BLIP-2’s outputs can reflect biases present in the training data.
Complex Scenes: The model may have difficulty accurately interpreting images with multiple overlapping objects or intricate details.

Output Format: Text

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