mcaption 0.0.5

A modern implementation of simple image captioning

Modern-Caption

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Description:

This Python script provides functionality for generating image captions using the "modern-caption" library. It utilizes CLIP (Contrastive Language-Image Pretraining) and GPT-2 models for image encoding and caption generation, respectively. Users can generate captions for images using different pre-trained models and decoding methods.

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Installation:

1. Dependencies:

   • torch
   • clip
   • transformers
   • numpy
   • PIL
   • scikit-image

2. Setup:

   • Ensure you have Python installed on your system.
   • Dependencies will be installed during setup.
   • Setup will not overwrite existing installs of PyTorch or Torchvision.
   • Install using pip:

     pip install modern-caption
     

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Usage:

1. Importing the Module:

   from mcaption import Caption
   

2. Initializing the Caption Generator:

   cap = Caption(model='conceptual', device='cpu', prefix_length=10)
   

3. Generating Captions:

   import skimage.io as io
   
   image = io.imread("images/cover.jpg")  # Load the image
   caption_conceptual = cap.predict(image, beam=True)  # Generate caption with beam search
   print("Conceptual Model Caption:", caption_conceptual)
   

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Explanation:

• The Caption class initializes the image-captioning functionality with options to specify the model, device, and other parameters.
• Users can create multiple instances of the Caption class to compare different models or share common resources.
• The predict method generates captions for images using the specified model and decoding method.

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Examples:

from mcaption import Caption
import skimage.io as io

# Initialize Caption generator with the 'conceptual' model
cap = Caption(model='conceptual', device='cpu', prefix_length=10)

# Load an image and generate a caption using beam search
image = io.imread("images/cover.jpg")
caption_conceptual = cap.predict(image, beam=True)
print("Conceptual Model Caption:", caption_conceptual)

# Initialize another Caption generator with the 'coco' model, inheriting from the previous one
cap2 = Caption(model='coco', inherit=cap)

# Generate a caption using greedy decoding
caption_coco = cap2.predict(image, beam=False)
print("COCO Model Caption:", caption_coco)

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Notes:

• The script demonstrates how to initialize and use the image-captioning functionality provided by the "modern-caption" library.
• Users can experiment with different models and decoding methods to obtain varied captions.
• Ensure that the image paths are correct and accessible.
• This library used the following repository for conceptual reference.

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License

This project is licensed under the MIT License - see the LICENSE file for details.