FitPic is a full-stack, AI-driven web application that delivers personalized clothing recommendations using computer vision and machine learning.
During authentication, users upload or capture an image. The system analyzes visual attributes such as:
- Skin tone
- Face shape
- Hair color
- Eye type
Using these extracted features, FitPic intelligently generates outfit combinations from multiple fashion brands and assigns a compatibility score (e.g., 80% match). The platform then recommends the most suitable outfits tailored to each user.
FitPic aims to simplify online fashion decisions, improve styling confidence, and reduce product return rates.
Online shoppers often struggle with:
- Choosing colors that suit their skin tone
- Identifying styles that match their facial structure
- Pairing clothing items effectively
Most e-commerce platforms provide generic recommendations without personalization.
👉 FitPic solves this by delivering data-driven, personalized outfit recommendations based on individual visual attributes.
- User registers or logs in
- Image is uploaded during authentication
Computer vision models analyze:
- Skin tone
- Face shape
- Hair color
- Eye type
- Clothing data is collected from multiple brand sources
- Structured into datasets for processing
-
Clothing items (shirts, pants, etc.) are automatically paired
-
Compatibility score is calculated using:
- User attributes
- Style rules
- Color theory
- Top outfit combinations are displayed
- Each includes a match percentage score
- Product preview in UI
- Secure payment gateway integration
- 🔐 User Authentication
- 📷 AI-Based Image Analysis
- 🎯 Attribute-Based Outfit Matching
- 📊 Compatibility Score System
- 🛍️ Multi-Brand Outfit Pairing
- 📱 Fully Responsive UI (Mobile / Tablet / Desktop)
- 💳 Secure Payment Integration
- ⚡ Fast API-based Backend
- React.js
- HTML5 / CSS3
- Responsive UI Design
- FastAPI (Python)
- REST API Architecture
- TensorFlow (Fit + Try-On Models)
- OpenCV (Image Processing)
- MongoDB
- Docker & Docker Compose
- GitHub (Version Control)
fitpic/
│
├── backend/
│ ├── app/
│ │ ├── main.py
│ │ ├── api/routes/
│ │ ├── services/
│ │ ├── models/
│ │ ├── schemas/
│ │ ├── utils/
│ │ ├── db/
│ │
│ ├── requirements.txt
│ ├── Dockerfile
│
├── frontend/
│ ├── src/
│ │ ├── components/
│ │ ├── api/
│ │ ├── App.js
│
├── ml/
│ ├── train_fit.py
│ ├── train_tryon.py
│
├── docker-compose.yml
└── README.md
- Docker & Docker Compose
- Node.js & npm
docker-compose up --build-
Input: Body + attribute features
-
Output:
- Fit score
- Recommended size
-
Input:
- User image
- Clothing image
-
Output:
- Generated try-on image
⚠️ Note: Current try-on model is a baseline implementation. Production-grade systems should use advanced models like diffusion-based try-on.
- 👕 Virtual Try-On (Advanced / Diffusion Models)
- 📏 Body Measurement Estimation
- 🧠 AI Chat Styling Assistant
- 📈 Style History Tracking
- 🌦️ Seasonal Trend Adaptation
- 🧍 3D Avatar Integration (Three.js)
FitPic aims to become a smart fashion assistant that combines AI with styling intelligence to deliver:
- Personalized outfit recommendations
- Improved shopping confidence
- Reduced product return rates
- Try-on model is not production-level
- Requires real-world datasets for accuracy
- Styling rules are basic (can be enhanced with ML + fashion datasets)