By combining Convolutional Neural Networks (CNNs) for feature extraction with Recurrent Neural Networks (RNNs) for sequence generation, the model learns to associate visual elements with corresponding textual descriptions.

Dataset

The model is trained on the Flickr8k dataset, which contains 8,000 images, each annotated with five different captions. This diverse set of images provides a rich resource for training the captioning model.

Features

• Deep Learning Architecture: Utilizes CNNs and RNNs to process images and generate captions.
• Comprehensive Walkthrough: The Jupyter notebook includes a step-by-step explanation of the model architecture, data preprocessing, training, and evaluation.
• Performance Metrics: Detailed analysis of model performance using various evaluation metrics to ensure effective caption generation.

Technologies Used

• Python
• TensorFlow / Keras
• NumPy
• Matplotlib (for visualizations)

Getting Started

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Shrinjita/Image-Captioning.git
   cd Image-Captioning

2. Download the Flickr8k dataset: You can download the dataset from here. Ensure that the dataset is placed in the correct directory as referenced in the notebook.

3. Install the required packages: If you don’t have the required libraries installed, you can do so with:

   pip install -r requirements.txt

4. Run the Jupyter Notebook: Start Jupyter Notebook:

   jupyter notebook

5. Open the Notebook: Open ImageCaptioning.ipynb to explore the project and generate captions for your images.

Usage

Load your images from the Flickr8k dataset and generate captions by executing the cells in the notebook. The model can be further fine-tuned with additional training data for improved performance.