🖼️ Text-to-Image Generation Model

ML_Demo.mp4

📝 Contents

• Introduction
• Technology Stack
• Features
• Project Structure
• How to Run
• Using the Model
• Contribution Guidelines
• Get in Touch

🌟 Introduction

This project implements a Text-to-Image Generation Model using TensorFlow and Gradio. The model converts textual descriptions (prompts) into corresponding images by identifying and generating captions for similar images from a dataset.

💻 Technology Stack

• TensorFlow: For deep learning and model training.
• Gradio: For creating the web-based interface.
• Pandas: For data manipulation and analysis.
• Numpy: For numerical operations.
• PIL: For image processing.
• COCO Dataset: Used for training and testing the model.

✨ Features

• Image Captioning: Generate captions for images using a Transformer-based model.
• Text-to-Image Matching: Find and display images that match a given textual prompt.
• Interactive Web Interface: Use Gradio to interact with the model and visualize results.
• Model Training and Saving: Train the model on a subset of the COCO dataset and save the weights.

🗂️ Project Structure

• coco_dataset/: Directory containing the COCO dataset.
  • annotations/captions_train2017.json: Annotations for training data.
  • train2017/: Directory containing the training images.
• captions_sample.csv: Sampled captions and image paths used for training.
• vocab_coco.file: Serialized vocabulary file created from the tokenizer.
• model.weights.h5: File containing the saved weights of the trained model.
• Text-to-Image-Generation.ipynb: Jupyter Notebook containing the code for this project.

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/OKihichak/image_from_text_generation.git
   cd text-to-image-generator

2. Install the required dependencies:

   pip install tensorflow pandas numpy matplotlib pillow tqdm requests scikit-learn gradio

3. Prepare the COCO dataset:

   • Download and place the COCO dataset in the coco_dataset/ directory.

4. Run the Jupyter Notebook:

   • Open Text-to-Image-Generation.ipynb and run the cells sequentially.

🎨 Using the Model

1. Generate Images from Text Prompts:

   • Input a text prompt into the Gradio interface.
   • The model will find the most similar image and display it along with a generated caption.

2. Save or Load Model Weights:

   • Train the model and save the weights to model.weights.h5.
   • Load saved weights for further training or inference.

🤝 Contribution Guidelines

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/YourFeatureName

3. Make your changes and commit them:

   git commit -m 'Add YourFeatureName'

4. Push to the branch:

   git push origin feature/YourFeatureName

5. Open a pull request to discuss and merge your changes.

📧 Get in Touch

• Email: [email protected]
• GitHub: Oleh Kihichak