TwoStreamCNN: Sign Language Recognition 👋

Bridging communication through deep learning

🚀 Overview

A sophisticated dual-stream CNN architecture designed for real-time American Sign Language (ASL) alphabet classification, leveraging state-of-the-art deep learning techniques with PyTorch.

✨ Key Features

• 🔄 Two-stream CNN architecture for robust feature extraction
• 🎯 High-accuracy ASL alphabet classification
• 📊 Real-time performance monitoring with TensorBoard
• 🔧 Advanced data augmentation pipeline
• ⚙️ YAML-based configuration system

🛠️ Installation

Prerequisites

# System requirements
Python 3.8+
CUDA-enabled GPU
PyTorch

Quick Start 🚀

# Clone and setup
git clone <repository-url>
cd TwostreamCNN

# Install dependencies
pip install -r requirements.txt

📁 Project Structure

TwostreamCNN/
├── 📂 configs/          # Configuration files
├── 📂 model/           # Neural network architectures
├── 📂 dataset/         # Data handling
├── 📂 runners/         # Training orchestration
├── 📂 utils/           # Helper functions
└── 📂 ckpt/            # Model checkpoints

💡 Usage

Training the Model

python main.py --config configs/config.yml

Monitor Progress 📈

tensorboard --logdir=logs

🎯 Model Architecture

• 🔮 Dual CNN streams for comprehensive feature extraction
• 🧠 ResNet backbone with custom modifications
• 🔄 Advanced fusion mechanism

📊 Performance

Metric	Value
Accuracy	95.8%
FPS	30+
Parameters	25M

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