Fashion Recognition & Recommendation System 👗🤖

An AI-powered clothing recognition and recommendation system that identifies fashion items in images and suggests similar products using deep learning and computer vision techniques.

🌟 Features

• Image Recognition: Identify clothing items from uploaded imagess
• Smart Recommendations: Get 5 similar fashion items based on visual similarity
• Deep Learning Backend: Powered by ResNet50 for accurate feature extraction
• User-Friendly Interface: Clean Streamlit web application
• Real-time Processing: Fast image analysis and recommendation generation
• Scalable Architecture: Easy to extend with more fashion categories

🚀 Demo

Upload an image of any clothing item and instantly receive personalized recommendations of similar fashion products from the database.

🛠️ Technology Stack

• Deep Learning: TensorFlow, Keras
• Computer Vision: OpenCV, PIL
• Machine Learning: scikit-learn (Nearest Neighbors)
• Web Framework: Streamlit
• Data Processing: NumPy, Pandas
• Model: ResNet50 (pre-trained on ImageNet)

📋 Prerequisites

• Python 3.7+
• TensorFlow 2.x
• CUDA-compatible GPU (recommended for faster processing)

🔧 Installation

1. Clone the repository

   git clone https://github.com/yourusername/fashion-recognition-recommendation-system.git
   cd fashion-recognition-recommendation-system

2. Create virtual environment

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

3. Install dependencies

   pip install tensorflow streamlit opencv-python scikit-learn numpy pillow tqdm

4. Create required directories

   mkdir images uploads sample

📁 Project Structure

fashion-recognition-recommendation-system/
│
├── app.py                 # Feature extraction and model training
├── main.py               # Streamlit web application
├── test.py               # Testing and validation script
├── README.md             # Project documentation
├── requirements.txt      # Python dependencies
│
├── images/               # Dataset directory (add your fashion images here)
├── sample/               # Sample test images
├── uploads/              # Temporary upload directory
│
├── embeddings.pkl        # Extracted feature vectors (generated)
├── filenames.pkl         # Image file paths (generated)
└── models/               # Saved model files (optional)

🚀 Usage

Step 1: Prepare Your Dataset

1. Add fashion images to the images/ directory
2. Organize images by categories (optional but recommended)
3. Supported formats: JPG, PNG, JPEG

Step 2: Extract Features

Run the feature extraction script to process your dataset:

python app.py

This will:

• Load the ResNet50 model
• Extract features from all images in the images/ directory
• Save feature vectors to embeddings.pkl
• Save file paths to filenames.pkl

Step 3: Launch the Web Application

Start the Streamlit web interface:

streamlit run main.py

Step 4: Test Individual Images (Optional)

Test with specific images using the test script:

python test.py

🎯 How It Works

1. Feature Extraction Pipeline

• Input: Fashion images in various formats
• Processing: ResNet50 CNN extracts 2048-dimensional feature vectors
• Normalization: Features are normalized using L2 norm
• Storage: Features saved as pickle files for quick access

2. Similarity Matching

• Algorithm: K-Nearest Neighbors with Euclidean distance
• Process: Compare uploaded image features with database
• Output: Top 5 most similar fashion items

3. Web Interface

• Upload: Simple drag-and-drop interface
• Processing: Real-time feature extraction
• Display: Visual grid of recommendations

📊 Model Performance

• Feature Extraction: ResNet50 (ImageNet pre-trained)
• Feature Dimension: 2048
• Similarity Metric: Euclidean Distance
• Recommendation Speed: ~1-2 seconds per query
• Accuracy: Depends on dataset quality and diversity

🔧 Configuration

Model Parameters

# Adjust in main.py and app.py
INPUT_SHAPE = (224, 224, 3)
N_NEIGHBORS = 6  # Returns top 5 (excluding input image)
SIMILARITY_METRIC = 'euclidean'

Streamlit Configuration

# Customize UI elements in main.py
st.set_page_config(
    page_title="Fashion Recommender",
    page_icon="👗",
    layout="wide"
)

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

🐛 Known Issues & Solutions

Common Problems:

1. Memory Error during feature extraction

   • Solution: Process images in smaller batches
   • Reduce image resolution if needed

2. Slow recommendations

   • Use GPU acceleration
   • Consider using approximate nearest neighbors (Annoy, Faiss)

3. Poor recommendations

   • Ensure diverse, high-quality dataset
   • Consider fine-tuning ResNet50 on fashion data

📈 Future Enhancements

• Add clothing category classification
• Implement color and pattern filtering
• Add user feedback loop for improved recommendations
• Support for multiple clothing items in single image
• Mobile app version
• Integration with e-commerce APIs
• Advanced filtering (price, brand, size)

🙏 Acknowledgments

• ResNet50 architecture by Microsoft Research
• TensorFlow team for the excellent framework
• Streamlit team for the intuitive web framework
• Fashion dataset contributors

⭐ Show Your Support

Give a ⭐️ if this project helped you!

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Built with ❤️ using Python and Deep Learning