Image Caption Generator

Overview

An AI-powered image captioning system that combines Computer Vision and Natural Language Processing to automatically generate human-like descriptions of images. The project demonstrates deep learning expertise and the practical application of neural networks for real-world problems.

Key Features

• CNN + LSTM Architecture — Utilizes Convolutional Neural Networks (CNN) for extracting visual features from images and Long Short-Term Memory (LSTM) networks for generating sequential text descriptions.
• Large-Scale Training — Model trained on extensive image-caption datasets to learn diverse vocabulary and contextual understanding for accurate caption generation.
• Interactive Application — User-friendly app that allows real-time image upload and instant caption generation, demonstrating the model's practical applicability.
• Transfer Learning — Leverages pre-trained CNN models (like VGG16 or ResNet) for robust feature extraction, improving accuracy and reducing training time.
• Beam Search Decoding — Implements advanced decoding strategies to generate more accurate and natural-sounding captions.

Technical Architecture

• Computer Vision: CNN models (VGG16/ResNet) for image feature extraction
• NLP: LSTM/GRU networks for sequence-to-sequence text generation
• Framework: TensorFlow/Keras or PyTorch for model development and training
• Dataset: Trained on large-scale datasets like COCO or Flickr8k/30k
• Application: Interactive web/desktop app for real-time inference

Development Period

December 2024 - February 2025

Learning Outcomes

This project demonstrates proficiency in deep learning, computer vision, natural language processing, and the ability to integrate complex AI models into user-facing applications.