Image Caption Generator using Deep Learning

Welcome to the Image Caption Generator project! This deep learning model takes an image as input and generates descriptive captions, making it an invaluable tool for accessibility, digital content organization, and automatic captioning for social media. Developed with convolutional and recurrent neural networks, this project showcases the power of AI in understanding and describing visual content.

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📋 Table of Contents

• Project Overview
• Key Features
• Model Architecture
• Dataset
• Setup and Installation
• Usage
• Results
• License

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📖 Project Overview

The Image Caption Generator employs a CNN-RNN architecture to interpret images and produce human-like captions. It first extracts features from the image using a Convolutional Neural Network (CNN) and then generates descriptive text using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells.

This project is designed for tasks that require image understanding and language generation, and it can be extended to applications in accessibility tech, content automation, and more.

🌟 Key Features

• Visual Feature Extraction: Uses a CNN (e.g., InceptionV3) to extract essential image features.
• Natural Language Generation: LSTM layers generate coherent and contextually relevant captions.
• Customizable with Datasets: Compatible with the MS COCO dataset and other custom datasets.
• End-to-End Training and Testing Pipeline: Simple setup to train, test, and evaluate the model.

🏗️ Model Architecture

1. CNN Encoder: A pretrained CNN model (e.g., InceptionV3) is used to encode images into feature vectors, capturing essential visual information.
2. Embedding Layer: Maps words to vector representations, helping the RNN understand relationships between words.
3. LSTM Decoder: Takes the encoded image features and generates captions, word-by-word, for natural language output.

📊 Dataset

The project supports the MS COCO dataset and other datasets formatted with image-caption pairs. The MS COCO dataset offers a rich variety of captions and images, ideal for training models that generalize well.

To use a different dataset, ensure it’s structured similarly with images and corresponding captions.

🚀 Setup and Installation

Prerequisites

• Python 3.x
• Jupyter Notebook
• Required libraries (install via):

  pip install -r requirements.txt