AI Applications in Textile-Garment Industry

A comprehensive collection of AI/ML solutions for textile manufacturing, including defect detection, demand forecasting, worker assistance systems, and fashion product classification.

This repository contains various AI/ML applications focused on solving challenges in the textile and garment industry. The projects demonstrate the practical implementation of artificial intelligence in different aspects of textile manufacturing and quality control.

Projects Overview

1. Textile Damage Detection

Location: textile-damage-detection/

• YOLOv8-based defect detection system
• Real-time quality control for textile manufacturing
• Detects various types of fabric defects and damages
• Uses computer vision for automated quality inspection

2. Garment Worker AI & ML Demo

Location: Garments Workers AI&ML demo/

• AI-powered worker assistance system
• Helps garment workers with quality control
• Provides real-time feedback and guidance
• Improves worker efficiency and product quality

3. Textile Demand Forecasting

Location: textile-demand-forecasting/

• Machine learning-based demand prediction
• Helps in inventory management
• Optimizes production planning
• Reduces waste and improves resource allocation

4. Demand Prediction

Location: demand prediction/

• Advanced demand forecasting models
• Market trend analysis
• Seasonal pattern recognition
• Helps in strategic decision making

5. Precision Fashion Image Classification

Location: Precision-fashion-image-classification/

• HOG and KNN-based fashion product classification
• 98% classification accuracy
• Fast and efficient inference
• No deep learning required
• Works with limited computational resources

Technology Stack

• Computer Vision: YOLOv8, OpenCV, HOG
• Machine Learning: PyTorch, scikit-learn, KNN
• Deep Learning: Neural Networks, Transfer Learning
• Data Processing: Pandas, NumPy
• Visualization: Matplotlib, Plotly

Getting Started

Each project directory contains its own:

• requirements.txt for dependencies
• README.md with specific instructions
• Training scripts and models
• Dataset information

Prerequisites

• Python 3.8+
• CUDA-capable GPU (for computer vision tasks)
• Sufficient RAM (16GB+ recommended)

Installation

1. Clone the repository:

git clone [repository-url]

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies for specific project:

cd [project-directory]
pip install -r requirements.txt

Project Structure

├── textile-damage-detection/     # Defect detection system
├── Garments Workers AI&ML demo/  # Worker assistance system
├── textile-demand-forecasting/   # Demand prediction system
├── demand prediction/           # Market analysis system
├── Precision-fashion-image-classification/  # Fashion product classification
├── venv/                        # Virtual environment
└── README.md                    # This file

Features

Textile Damage Detection

• Real-time defect detection
• Multiple defect type classification
• High accuracy and speed
• Easy integration with existing systems

Garment Worker AI

• Real-time assistance
• Quality control guidance
• Performance monitoring
• Training support

Demand Forecasting

• Accurate demand prediction
• Market trend analysis
• Inventory optimization
• Resource planning

Precision Fashion Classification

• High accuracy (98%)
• Fast inference
• Simple and interpretable model
• Works with limited resources

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Roboflow for dataset support
• Ultralytics for YOLOv8 implementation
• Open source community for various tools and libraries

Contact

For any queries or suggestions, please open an issue in the repository.