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Caption Generator

This project is registered as a open source project in the Android Club Winter of Code (AcWoc) 24' event conducted by the Android Club of VIT Bhopal University.

Overview

The Caption Generator project is an open-source tool that uses image recognition and caption generation to provide meaningful descriptions for images. It features a user-friendly interface built with Gradio, allowing users to upload an image and receive an auto-generated caption.

This project is written in Python and is open for contributions.

Features

  • Image Content Recognition: Detects objects and content within an image.
  • Caption Generation: Provides descriptive captions for uploaded images.
  • Gradio Interface: An intuitive web-based UI for easy interaction.
  • Customizable: Modular architecture to extend and adapt functionality.

Technical Details

Technologies Used

  • Python
  • Gradio for the user interface
  • TensorFlow/PyTorch for the backend models
  • OpenCV for image processing (if applicable)

System Architecture

  • The image is uploaded via the Gradio interface.
  • The backend processes the image and generates a caption using a pretrained model.
  • The caption is displayed in the Gradio interface.

Model Details

  • Uses pretrained models for image captioning (e.g., ResNet + Transformer architecture).
  • Open for customization with other models.

Installation

  1. Clone the repository: 
    git clone https://github.com/Auth0r-C0dez/Caption_generator.git
cd Caption_generator
  2. Install the dependencies through requirements.txt: 
    pip install -r requirements.txt

Usage

  1. Start the application: 
    python app.py
  2. Open the URL provided by Gradio in your web browser.
  3. Upload an image and view the generated caption.

Examples

Input Image

Example Image

Generated Caption

"A group of people sitting around a table with laptops."

What i look for improvement ??

The project allows customization to improve its functionality:

  • Dataset and Keywords: The dataset and the manually added list of keywords for image orientation and recognition can be modified or expanded to enhance the system's performance and accuracy.
  • Caption Orientation: The logic for caption generation can be optimized further to provide more coherent and contextually appropriate captions.
  • Model Improvement : Make the model better thus increasing the contents of the caption or qualitative arrangement of words in the caption
  • Personal improvement : In case you have an idea of improving the model that doesnot fall in the above requirements do create a PR it would be lovely to see that.

Contributors are encouraged to experiment with these aspects and propose improvements.

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix: 
    git checkout -b feature-name
  3. Make your changes and commit them: 
    git commit -m "Add a detailed description of the change"
  4. Push your changes: 
    git push origin feature-name
  5. Create a pull request on the main repository.

Contribution Guidelines

  • Follow the PEP8 coding standards.
  • Document your code where necessary.
  • Include tests for new features or bug fixes.

Support

FAQ

Q: What types of images are supported? A: Standard image formats like PNG, JPEG, and BMP are supported.

Q: Can I use my own model? A: Yes, the project is modular and allows integration of custom models.

Attribution

Created by Auth0r-C0dez.

License

This project is licensed under the MIT License.

HaPPy Coding