Image Captioning with TensorFlow

This project demonstrates how to build an image captioning model using TensorFlow. The model combines a pre-trained Convolutional Neural Network (CNN) for image feature extraction and a Long Short-Term Memory (LSTM) network for generating captions.

Table of Contents

• Introduction
• Requirements
• Installation
• Usage
• Model Architecture
• Training
• Prediction
• Acknowledgements

Introduction

Image captioning is the task of generating a descriptive sentence for a given image. This project uses a CNN to extract features from an image and an LSTM to generate a corresponding caption.

Requirements

• Python 3.6 or higher
• TensorFlow 2.x
• NumPy
• Pillow (PIL)

Installation

1. Clone this repository:

   https://github.com/IcodeG00D/Image-Captioning-Using-CNN-and-LSTM.git

2. Install the required packages:

   pip install tensorflow numpy pillow

Usage

1. Place your image in the images folder.
2. Update the image_path and caption in the script as needed.
3. Run the script to train the model and generate captions.