Image Feature Detection & Captioning
An AI-powered system that automatically generates descriptive captions for images using advanced deep learning models. This project demonstrates the integration of computer vision and natural language processing to create meaningful image descriptions.
Project Overview
This project implements a sophisticated image captioning system that combines computer vision and natural language processing. The system uses CNN and VGG-16 models for feature extraction from images, then employs both LSTM and Transformer architectures for generating human-like captions. The project achieved impressive results with BLEU scores of 0.65 for LSTM and 0.80 for Transformer models, demonstrating the effectiveness of attention mechanisms in caption generation. The user interface was built using Streamlit, providing an intuitive way for users to upload images and receive instant captions. Key technical challenges included optimizing model performance, handling diverse image types, and ensuring real-time inference capabilities. The project showcases full-stack development skills in AI application development, from model training to deployment.
Key Features
Advanced AI Models
Implemented CNN and VGG-16 for feature extraction, LSTM and Transformer for caption generation
High Performance
Achieved BLEU scores of 0.65 (LSTM) and 0.80 (Transformer) for caption quality
User-Friendly Interface
Built with Streamlit for easy image upload and instant caption generation
Real-time Processing
Optimized for fast inference and real-time caption generation
Technical Implementation
- CNN and VGG-16 models for image feature extraction
- LSTM architecture with attention mechanisms
- Transformer model for improved caption quality
- BLEU score evaluation metrics
- Streamlit web interface for user interaction
- Image preprocessing and augmentation techniques
- Model optimization for deployment
Challenges Faced
- Balancing model complexity with inference speed
- Handling diverse image types and content
- Optimizing BLEU scores for better caption quality
- Creating an intuitive user interface
- Managing model memory requirements
Key Learnings
- Deep learning model architecture design
- Computer vision and NLP integration
- Performance optimization techniques
- User interface design for AI applications
- Model evaluation and metrics analysis