A state-of-the-art deep learning pipeline that automatically detects clothing items, extracts dominant colors, and provides intelligent color harmony recommendations based on established fashion theory principles.
- Project Overview
- Key Features
- Performance Metrics
- Installation
- Step-by-Step Usage Guide
- Model Architecture
- Color Theory Implementation
- Technical Implementation
- Dataset Information
- Results and Validation
- Future Improvements
- Contributing
- Citation
- License
This project presents a comprehensive deep learning pipeline for automated fashion color harmony analysis, combining computer vision and color theory to democratize fashion expertise. Our system integrates YOLOv8 segmentation for clothing detection, K-means clustering for color extraction, and rule-based harmony analysis to provide real-time fashion guidance.
Research Paper: AI Color Stylist: Deep Learning-Based Fashion Color Harmony Analysis (Introduction to Deep Learning, May 2025)
The system addresses three core challenges:
- Accurate Detection: Precise clothing item detection and segmentation across diverse imagery
- Color Extraction: Robust extraction of meaningful color palettes from segmented clothing items
- Harmony Assessment: Principled color harmony evaluation based on established fashion theory
- 🎯 Advanced Object Detection: YOLOv8-medium model achieving 79.5% mAP50 for detection and 64.7% mAP50 for segmentation
- 🎨 Intelligent Color Analysis: K-means clustering in LAB color space with 88% accuracy in dominant color identification
- 📊 Realistic Harmony Scoring: Produces realistic assessments (20-70% range) avoiding grade inflation common in other systems
- ⚡ Real-time Performance: 250ms inference time per image on consumer hardware
- 🔧 Modular Architecture: Easily extensible for texture, style, or occasion-based recommendations
- 📱 Actionable Recommendations: Specific suggestions for color improvements with fashion-theory backing
| Metric | Detection (Box) | Segmentation (Mask) |
|---|---|---|
| Precision | 78.4% | 71.9% |
| Recall | 73.9% | 66.1% |
| mAP50 | 79.5% | 66.8% |
| mAP50-95 | 64.7% | 45.5% |
Processing Speed: 250ms per image (detection + segmentation + color analysis)
Color Extraction Accuracy: 88% in LAB color space
Expert Agreement: 73% agreement on harmony classifications, 81% on recommendations
- Python 3.8+
- CUDA-compatible GPU (recommended: RTX 3080 or better)
- 8GB+ VRAM for optimal performance
- Clone the repository:
git clone https://github.com/voloshynm/Deep_Fashion_Color.git
cd Deep_Fashion_Color
- Create and activate virtual environment:
python -m venv venv
source venv/bin/activate # Linux/macOS
# or
venv\Scripts\activate # Windows
- Install dependencies:
pip install -r requirements.txt
- Download pre-trained weights:
# YOLOv8 weights will be downloaded automatically on first run
# Or manually download from: https://github.com/ultralytics/ultralytics
- Verify installation:
python -c "import torch; print(f'PyTorch: {torch.__version__}')"
python -c "import ultralytics; print('YOLOv8 ready')"
-
Prepare your image:
- Ensure the image contains clearly visible clothing items
- Supported formats:
.jpg,.jpeg,.png - Recommended resolution: 256x256 to 2048x2048 pixels
-
Run the analysis:
python run_analysis.py --image path/to/your/outfit.jpg --output results/
- View results:
- Detection visualization:
results/detected_items.jpg - Color analysis:
results/color_palette.png - Harmony report:
results/harmony_analysis.json - Recommendations:
results/recommendations.txt
- Detection visualization:
- Create analysis script:
from src.color_stylist import ColorStylist
from src.utils import load_image, save_results
# Initialize the stylist
stylist = ColorStylist(model_path='models/yolov8m-seg.pt')
# Load and analyze image
image = load_image('path/to/outfit.jpg')
results = stylist.analyze_outfit(image)
# Extract results
detected_items = results['detected_items']
color_harmony = results['harmony_score']
recommendations = results['recommendations']
print(f"Harmony Score: {color_harmony:.1f}%")
print(f"Recommendations: {recommendations}")
- Run your script:
python your_analysis_script.py
- Launch notebook:
jupyter notebook examples/interactive_analysis.ipynb
- Follow the step-by-step cells:
- Upload image
- Run detection
- Analyze colors
- View recommendations
Detection Results:
- Bounding boxes around detected clothing items
- Segmentation masks for precise color extraction
- Confidence scores for each detection
- Item categories (shirt, trousers, dress, etc.)
Color Analysis:
- Dominant colors for each clothing item (up to 5 colors per item)
- Color names using fashion-specific terminology
- Color temperature classification (warm/cool/neutral)
- Percentage coverage of each color
Harmony Assessment:
- Overall harmony score (0-100%)
- Detailed breakdown by color theory principles
- Identification of harmony issues (monotone, clashing, etc.)
- Specific recommendations for improvement
Sample Output:
=== AI Color Stylist Analysis ===
Detected Items: 3 (shirt, trousers, shoes)
Overall Harmony Score: 42.3%
Issues Identified:
- Monotone palette (too many neutrals)
- Lack of focal point color
- Insufficient contrast
Recommendations:
- Replace gray shirt with coral or teal for better contrast
- Add accent color through accessories
- Consider navy instead of black trousers for softer look
- Backbone: CSPDarknet53 feature extractor
- Architecture: Feature Pyramid Network for multi-scale detection
- Parameters: 71M parameters (YOLOv8-medium)
- Specialized heads: Simultaneous bounding box regression, classification, and mask prediction
- Activation: SiLU activation functions throughout
- Modern techniques: Attention mechanisms and path aggregation networks
- Dataset: DeepFashion2 (491K images, 801K items, 13 categories)
- Hardware: Optimized for RTX 3080 (8GB VRAM)
- Batch size: 64, Image resolution: 256×256
- Optimizer: SGD with momentum 0.937
- Learning rate: 0.01 with step decay
- Data augmentation: Mosaic, mixup, color jittering
- Training time: 38 epochs over 17 hours
- Algorithm: K-means clustering (k=5) in LAB color space
- Preprocessing: Shadow/highlight removal, minimum region constraints
- Color naming: Extended database of 50+ fashion-specific colors
- Filtering: Brightness thresholds (30-225), minimum 100 pixels, >5% coverage
Temperature Mixing Assessment:
- Penalizes excessive mixing of warm and cool colors
- Identifies temperature conflicts that create visual discord
Monotone/Neutral Detection:
- Flags outfits composed entirely of neutral colors
- Encourages visual interest through color variety
Contrast and Saturation Analysis:
- Ensures sufficient contrast between clothing items
- Discourages all-dark or all-washed-out combinations
Focal Point Identification:
- Checks for at least one saturated or bright color
- Ensures visual anchor points in the outfit
The FixedColorHarmonyAnalyzer class implements:
- Color classification: Warm, cool, neutral categorization using HSV thresholds
- Rule-based evaluation: Fashion theory principles application
- Contextual suggestions: Curated color recommendations (accent colors, versatile neutrals)
- Actionable feedback: Specific item identification and color alternatives
# Core classes and modules
src/
├── models/
│ ├── yolo_detector.py # YOLOv8 clothing detection
│ └── color_extractor.py # K-means color analysis
├── analysis/
│ ├── harmony_analyzer.py # Color theory implementation
│ └── recommendation.py # Styling suggestions
├── utils/
│ ├── preprocessing.py # Image preprocessing
│ ├── color_utils.py # Color space conversions
│ └── visualization.py # Results visualization
└── color_stylist.py # Main pipeline orchestrator
LAB vs RGB vs HSV Performance:
- LAB clustering: 88% accuracy (perceptually uniform)
- RGB clustering: 76% accuracy
- HSV clustering: 82% accuracy
LAB color space provides superior perceptual uniformity, enabling more meaningful color distances for fashion applications.
- Total images: 491K diverse fashion images
- Clothing items: 801K annotated items
- Categories: 13 popular clothing categories
- Annotations: Bounding boxes, segmentation masks, landmarks, style attributes
- Split: 80% training (391K images), 20% validation (100K images)
- Resolution diversity: 128×128 to 2048×2048 pixels
- Average items per image: 1.6
| Category | Instances | Performance (mAP50) |
|---|---|---|
| Short-sleeved shirt | 152K | 81% |
| Trousers | 100K+ | 79% |
| Long-sleeved shirt | 80K+ | 80% |
| Sling dress | 8K | 67% |
- Short-sleeved shirts: 81% accuracy
- Trousers: 79% accuracy
- Long-sleeved shirts: 80% accuracy
- Dresses: 95% precision, 96% recall
- Lower performance on rare classes (sling dresses: 67%) due to limited training examples
- Expert agreement: 73% on harmony classifications
- Recommendation agreement: 81% with fashion experts
- Realistic scoring: Mean score 42.3% (σ=18.7%) addresses grade inflation
- Processing speed: Consistent 250ms per image across diverse inputs
- Detection improvement: 79.5% mAP50 vs 36.9% mAP (Mask R-CNN)
- Real-time performance: 250ms vs >1000ms (two-stage detectors)
- Realistic scoring: 20-70% range vs 90-100% (grade inflation in competitors)
- Texture Analysis: Pattern recognition for richer styling advice
- 3D Pose Integration: Body-shape-aware recommendations
- Video Analysis: Real-time styling feedback for virtual try-on
- Online Learning: Temporal trend adaptation
- Personalization: User preference history integration
- Cultural Adaptation: Region-specific color preferences
- Mobile Application: On-the-go styling assistance
- Virtual Try-on: Integration with AR/VR technologies
- Interior Design: Color harmony for home decoration
- Graphic Design: Brand color palette optimization
- Accessibility: Enhanced support for color vision deficiencies
We welcome contributions from the community! Here's how you can help:
- 🐛 Bug Reports: Report issues with detailed reproduction steps
- 💡 Feature Requests: Suggest new functionality or improvements
- 🔧 Code Contributions: Submit pull requests for bug fixes or features
- 📚 Documentation: Improve documentation and examples
- 🧪 Testing: Add test cases and improve coverage
# Clone and setup development environment
git clone https://github.com/voloshynm/Deep_Fashion_Color.git
cd Deep_Fashion_Color
pip install -e .
pip install -r requirements-dev.txt
# Run tests
python -m pytest tests/
# Code formatting
black src/
flake8 src/
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
If you use this work in your research, please cite our paper:
@article{voloshyn2025aicolorstylist,
title={AI Color Stylist: Deep Learning-Based Fashion Color Harmony Analysis},
author={Voloshyn, Maksym},
journal={Introduction to Deep Learning},
year={2025},
month={May},
institution={University of Luxembourg}
}
@inproceedings{ge2019deepfashion2,
title={DeepFashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images},
author={Ge, Yuying and Zhang, Ruimao and Wu, Lingyun and Wang, Xiaogang and Tang, Xiaoou and Luo, Ping},
booktitle={CVPR},
year={2019}
}
@article{ultralytics2023yolov8,
title={YOLOv8: A new state-of-the-art computer vision model},
author={Ultralytics},
year={2023}
}
This project is licensed under the University of Luxembourg License. See the LICENSE file for details.
- ✅ Research and educational purposes
- ✅ Non-commercial applications
- ✅ Citation required for publications
- 📧 Contact for commercial licensing
- 💼 Enterprise solutions available
- 🤝 Collaboration opportunities welcome
- University of Luxembourg - Academic support and resources
- DeepFashion2 Team - Comprehensive fashion dataset
- Ultralytics - YOLOv8 implementation and support
- Fashion Theory Researchers - Color harmony principles and validation
Author: Maksym Voloshyn
Email: maksym.voloshyn.002@student.uni.lu
Institution: University of Luxembourg
Course: Introduction to Deep Learning (May 2025)
Project Links:
- 🔗 GitHub Repository: https://github.com/voloshynm/Deep_Fashion_Color
- 📄 Research Paper: Available in repository
- 🎯 Live Demo: Coming soon
Created with ❤️ for fashion and AI enthusiasts. Democratizing fashion expertise through deep learning.
Keywords: deep-learning computer-vision fashion-ai color-theory yolov8 segmentation style-recommendation pytorch fashion-technology