| ## 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) | |