Image Feature Detection & Captioning
Generates captions for images using CNN feature extraction and Transformer-based text generation. Built with TensorFlow and Streamlit.
Project Overview
An image captioning system that uses CNN/VGG-16 for feature extraction and both LSTM and Transformer architectures for generating captions. The Transformer model hit a BLEU score of 0.80 (LSTM got 0.65), showing how much attention mechanisms help with caption quality. The frontend is a Streamlit app where you can upload an image and get a caption back instantly. The main challenges were optimizing inference speed, handling different image types, and keeping the model small enough for real-time use.
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