👔 WardrobeWizard

AI-Powered Digital Wardrobe Management

Transform your physical wardrobe into an intelligent digital closet with automatic clothing classification and personalized outfit recommendations.

Features • Demo • Installation • Usage • API • Contributing

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📋 Table of Contents

• About
• Features
• Demo
• Technology Stack
• Installation
• Usage
• Project Structure
• API Endpoints
• Machine Learning Model
• Contributing
• License
• Acknowledgements

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🎯 About

WardrobeWizard is a Django-based web application that empowers users to digitize their physical wardrobes through photo uploads. It leverages machine learning to automatically categorize clothing items and generates personalized outfit recommendations based on your existing wardrobe.

🎯 Problem Statement

Managing a wardrobe can be overwhelming—people often forget what they own, struggle with outfit coordination, and underutilize items they've purchased.

💡 Solution

WardrobeWizard provides:

• Automated Organization: Upload photos and let AI categorize your clothes
• Smart Recommendations: Get outfit suggestions based on style compatibility
• Personal Style Archive: Visual history of all clothing items with wear tracking
• Time Savings: Quick outfit planning without physically searching through your closet

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✨ Features

📸 Smart Upload & Classification Drag-and-drop image upload Multi-file batch upload support AI-powered automatic categorization (18 categories) Confidence score display for classifications	👗 Wardrobe Management Visual gallery of all clothing items Filter by category Edit item details and categories Soft delete with archive capability
🎨 Outfit Recommendations Intelligent outfit pairing algorithm Color and style compatibility matching Save favorite outfit combinations Occasion and season filtering	📊 Analytics & Insights Wardrobe statistics dashboard Wear tracking and logging Category breakdown charts Most/least worn items identification
👤 User Management Secure registration and authentication Personal profile with wardrobe overview Session management Password protection	📱 Modern UI/UX Mobile-responsive design Bootstrap 5 styling Intuitive navigation Real-time feedback with messages

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🖼️ Demo

ML Model Performance

The custom-trained MobileNetV2 model achieves strong classification accuracy:

Supported Clothing Categories

The ML model classifies items into 18 categories:

Tops	Bottoms	Outerwear	Other
T-Shirt	Pants	Outwear	Dress
Shirt	Shorts	Blazer	Shoes
Polo	Skirt	Hoodie	Hat
Top			Body
Blouse
Longsleeve
Undershirt

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🛠️ Technology Stack

Backend

Technology	Purpose
Django 5.1	Web framework
SQLite	Database (PostgreSQL ready)
TensorFlow 2.19	Machine learning framework
Keras	Deep learning API
MobileNetV2	Pre-trained CNN for transfer learning

Frontend

Technology	Purpose
Django Templates	Server-side rendering
Bootstrap 5	CSS framework
Crispy Forms	Form styling

ML/Data Science

Technology	Purpose
NumPy	Numerical operations
Pandas	Data manipulation
Pillow	Image processing
OpenCV	Computer vision utilities
scikit-learn	Model evaluation

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🚀 Installation

Prerequisites

• Python 3.10 or higher
• pip (Python package manager)
• Git
• Virtual environment (recommended)

Step-by-Step Setup

1. Clone the repository

   git clone https://github.com/NowarkCodes/WardrobeWizard.git
   cd WardrobeWizard

2. Create and activate a virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Apply database migrations

   python manage.py migrate

5. Create a superuser (optional, for admin access)

   python manage.py createsuperuser

6. Run the development server

   python manage.py runserver

7. Access the application

   Open your browser and navigate to: http://127.0.0.1:8000

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📖 Usage

Getting Started

1. Register an Account

   • Navigate to the registration page
   • Create an account with username and password

2. Upload Your First Item

   • Click "Upload" from the menu
   • Select one or more clothing photos
   • The AI will automatically classify each item

3. View Your Wardrobe

   • Browse your digital wardrobe in the gallery view
   • Filter by category to find specific items
   • Click on items to see details and recommendations

4. Get Outfit Recommendations

   • View AI-generated outfit pairings
   • Save your favorite combinations
   • Track what you wear

Quick Commands

# Run development server
python manage.py runserver

# Create database migrations
python manage.py makemigrations

# Apply migrations
python manage.py migrate

# Create superuser for admin panel
python manage.py createsuperuser

# Access admin panel
# Navigate to: http://127.0.0.1:8000/admin

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📁 Project Structure

WardrobeWizard/
├── WardrobeWizard/           # Django project settings
│   ├── __init__.py
│   ├── settings.py           # Project configuration
│   ├── urls.py               # Root URL configuration
│   ├── wsgi.py               # WSGI entry point
│   └── asgi.py               # ASGI entry point
│
├── wardrobe/                 # Main application
│   ├── migrations/           # Database migrations
│   ├── templates/wardrobe/   # HTML templates
│   │   ├── dashboard.html    # User dashboard
│   │   ├── upload.html       # Image upload page
│   │   ├── history.html      # Wardrobe gallery
│   │   ├── profile.html      # User profile
│   │   └── ...               # Other templates
│   ├── admin.py              # Admin configuration
│   ├── apps.py               # App configuration
│   ├── forms.py              # Django forms
│   ├── models.py             # Database models
│   ├── urls.py               # App URL patterns
│   ├── views.py              # View functions
│   ├── tests.py              # Unit tests
│   └── custom_fashion_model.h5  # Trained ML model
│
├── templates/                # Base templates
│   ├── base.html             # Base template
│   └── registration/         # Auth templates
│
├── dataset/                  # Training dataset
│   ├── images/               # Training images
│   └── labels/               # Label files
│
├── media/                    # User uploads (gitignored)
├── train_model.py            # Model training script
├── manage.py                 # Django CLI
├── requirements.txt          # Python dependencies
└── README.md                 # This file

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🔌 API Endpoints

Authentication

Method	Endpoint	Description
GET	/login/	Login page
POST	/login/	Authenticate user
POST	/logout/	Logout user
GET/POST	/register/	User registration

Wardrobe Management

Method	Endpoint	Description
GET	/	Landing page / Dashboard redirect
GET	/dashboard/	User dashboard with statistics
GET	/menu/	Navigation menu
GET/POST	/upload/	Upload new clothing items
GET	/history/	View wardrobe gallery
GET	/result/<id>/	View item details
GET/POST	/edit/<id>/	Edit item details
POST	/delete/<id>/	Remove item from wardrobe
POST	/worn/<id>/	Mark item as worn

Outfits & Analytics

Method	Endpoint	Description
GET	/outfits/	View saved outfits
GET/POST	/outfits/save/	Create new outfit
POST	/outfits/delete/<id>/	Delete outfit
GET	/stats/	Wardrobe statistics
GET	/profile/	User profile overview

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🤖 Machine Learning Model

Architecture

WardrobeWizard uses a MobileNetV2 convolutional neural network with transfer learning:

Model: MobileNetV2 (Pre-trained on ImageNet)
├── Base Model: MobileNetV2 (frozen weights)
├── Global Average Pooling 2D
├── Dense Layer (1024 units, ReLU activation)
└── Output Layer (20 classes, Softmax activation)

Training Configuration

Parameter	Value
Input Size	224 × 224 × 3 (RGB)
Batch Size	32
Epochs	25
Optimizer	Adam
Loss Function	Categorical Crossentropy

Data Augmentation

The training pipeline includes:

• Random rotation (±20°)
• Width/height shifts (±20%)
• Shear transformation
• Zoom (±20%)
• Horizontal flip

Retraining the Model

To retrain the model with your own dataset:

1. Prepare your dataset in dataset/images/ with corresponding labels in dataset/labels/dataset.csv

2. Run the training script:

   python train_model.py

3. The new model will be saved as custom_fashion_model.h5

4. Move the model to the wardrobe app:

   mv custom_fashion_model.h5 wardrobe/

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🤝 Contributing

Contributions are welcome! Here's how you can help:

Getting Started

1. Fork the repository
2. Clone your fork

   git clone https://github.com/YOUR_USERNAME/WardrobeWizard.git

3. Create a feature branch

   git checkout -b feature/amazing-feature

4. Make your changes
5. Commit your changes

   git commit -m "Add amazing feature"

6. Push to your branch

   git push origin feature/amazing-feature

7. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Write meaningful commit messages
• Add tests for new functionality
• Update documentation as needed
• Ensure all tests pass before submitting PR

Areas for Contribution

• 🐛 Bug fixes
• ✨ New features
• 📝 Documentation improvements
• 🎨 UI/UX enhancements
• 🧪 Test coverage
• 🌐 Internationalization

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📄 License

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

MIT License

Copyright (c) 2026 WardrobeWizard

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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🙏 Acknowledgements

• Django - The web framework for perfectionists with deadlines
• TensorFlow - Machine learning platform
• MobileNetV2 - Efficient CNN architecture
• Bootstrap - CSS framework
• Fashion-MNIST - Inspiration for fashion classification
• DeepFashion - Fashion dataset reference

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⭐ Star this repository if you find it helpful!

Made with ❤️ by the WardrobeWizard Team

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