rudra0410hf/image-search-engine-fashion

Image Similarity Search Engine

A deep learning-based image similarity search engine that uses EfficientNetB0 for feature extraction and FAISS for fast similarity search. The application provides a web interface built with Streamlit for easy interaction.

Features

• Deep Feature Extraction: Uses EfficientNetB0 (pre-trained on ImageNet) to extract meaningful features from images
• Fast Similarity Search: Implements FAISS for efficient nearest-neighbor search
• Interactive Web Interface: User-friendly interface built with Streamlit
• Real-time Processing: Shows progress and time estimates during feature extraction
• Scalable Architecture: Designed to handle large image datasets efficiently

Installation

Prerequisites

Python 3.8 or higher pip package manager

Setup

1. Clone the repository:

git clone https://github.com/yourusername/image-similarity-search.git
cd image-similarity-search

2. Create and activate a virtual environment:

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

3. Install required packages:

pip install -r requirements.txt

Project Structure

image-similarity-search/
├── data/
│   ├── images/                     # Directory for train dataset images
│   ├── sample-test-images/         # Directory for test dataset images
│   └── embeddings.pkl              # Pre-computed image embeddings
├── src/
│   ├── feature_extractor.py    # EfficientNetB0 feature extraction
│   ├── preprocessing.py        # Image preprocessing and embedding computation
│   ├── similarity_search.py    # FAISS-based similarity search
│   └── main.py                 # Streamlit web interface
├── requirements.txt
├── README.md
└── .gitignore

Usage

1. Prepare Your Dataset: Get training image dataset from drive:

https://drive.google.com/file/d/1U2PljA7NE57jcSSzPs21ZurdIPXdYZtN/view?usp=drive_link

Place your image dataset in the data/images directory Supported formats: JPG, JPEG, PNG

2. Generate Embeddings:

python -m src.preprocessing

This will:

• Process all images in the dataset
• Show progress and time estimates
• Save embeddings to data/embeddings.pkl

3. Run the Web Interface:

streamlit run src/main.py

4. Using the Interface:

• Upload a query image using the file uploader
• Click "Search Similar Images"
• View the most similar images from your dataset

Technical Details

Feature Extraction

• Uses EfficientNetB0 without top layers
• Input image size: 224x224 pixels
• Output feature dimension: 1280

Similarity Search

• Uses FAISS IndexFlatL2 for L2 distance-based search
• Returns top-k most similar images (default k=5)

Web Interface

• Responsive design with Streamlit
• Displays original and similar images with similarity scores
• Progress tracking during processing

Dependencies

• TensorFlow 2.x
• FAISS-cpu (or FAISS-gpu for GPU support)
• Streamlit
• Pillow
• NumPy
• tqdm

Performance

• Feature extraction: ~1 second per image on CPU
• Similarity search: Near real-time for datasets up to 100k images
• Memory usage depends on dataset size (approximately 5KB per image embedding)