📢 Image Captioning with Flickr8k Dataset

This project implements an Image Captioning system using the Flickr8k dataset and deep learning techniques. The system generates descriptive captions for images using a combination of Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) with LSTM units for sequence generation.

🚀 Project Structure

The project is structured as follows:

• Import Modules: Python modules for data preprocessing, model building, and evaluation.
• Extract Image Features: Uses a pre-trained VGG16 model to extract image features and stores them as pickle files.
• Load the Captions Data: Loads and preprocesses captions associated with the images in the dataset.
• Preprocess Text Data: Cleans and preprocesses text data by tokenizing and padding sequences.
• Train Test Split: Splits the data into training and testing sets for model training.
• Model Creation: Defines and trains the deep learning model for image captioning.
• Generate Captions for the Image: Generates captions for new images and evaluates using BLEU scores.
• Visualize the Results: Visualizes actual vs. predicted captions for sample images.

⬇️ How to Use

1. Setup Environment:

   • Ensure Python environment is set up with necessary dependencies (requirements.txt).
   • Install required libraries: TensorFlow, tqdm, NLTK, PIL, matplotlib, etc.

2. Dataset Preparation:

   • Download the Flickr8k dataset and arrange images with associated captions.
   • Ensure captions.txt file is formatted correctly with image IDs and captions.

3. Model Training:

   • Run the script to extract image features and preprocess captions.
   • Train the model using the prepared data, adjusting hyperparameters as necessary.
   • Monitor training progress and save the best performing model (best_model.h5).

4. Generate Captions:

   • Use the trained model to generate captions for new images.
   • Visualize the results and evaluate caption quality using BLEU scores.

🎯 Evaluation

• BLEU Score: Evaluates the quality of generated captions against reference captions using the BLEU metric (BiLingual Evaluation Understudy).

🛠️ Dependencies

Ensure you have the following Python libraries installed:

numpy
tensorflow
tqdm
pillow
matplotlib
nltk

🤝 Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

👉 Contact

For any inquiries or feedback, please reach out to:

• Name: Mohd Ramzan Shareef
• Email: [email protected]
• GitHub: ramzanshareef