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--- |
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language: en |
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license: mit |
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tags: |
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- image-search |
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- machine-learning |
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title: Image Similarity Search Engine |
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sdk: streamlit |
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emoji: 💻 |
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colorFrom: blue |
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colorTo: pink |
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--- |
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## Image Similarity Search Engine |
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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. |
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Features |
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- Deep Feature Extraction: Uses EfficientNetB0 (pre-trained on ImageNet) to extract meaningful features from images |
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- Fast Similarity Search: Implements FAISS for efficient nearest-neighbor search |
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- Interactive Web Interface: User-friendly interface built with Streamlit |
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- Real-time Processing: Shows progress and time estimates during feature extraction |
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- Scalable Architecture: Designed to handle large image datasets efficiently |
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## Installation |
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## Prerequisites |
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Python 3.8 or higher |
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pip package manager |
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## Setup |
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1. Clone the repository: |
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``` |
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git clone https://github.com/yourusername/image-similarity-search.git |
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cd image-similarity-search |
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``` |
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2. Create and activate a virtual environment: |
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``` |
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python -m venv venv |
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source venv/bin/activate # On Windows use: venv\Scripts\activate |
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``` |
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3. Install required packages: |
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``` |
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pip install -r requirements.txt |
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``` |
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## Project Structure |
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``` |
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image-similarity-search/ |
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├── data/ |
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│ ├── images/ # Directory for train dataset images |
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│ ├── sample-test-images/ # Directory for test dataset images |
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│ └── embeddings.pkl # Pre-computed image embeddings |
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├── src/ |
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│ ├── feature_extractor.py # EfficientNetB0 feature extraction |
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│ ├── preprocessing.py # Image preprocessing and embedding computation |
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│ └── similarity_search.py # FAISS-based similarity search |
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├── app.py # Streamlit web interface |
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├── requirements.txt |
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├── README.md |
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└── .gitignore |
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``` |
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## Usage |
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1. **Prepare Your Dataset:** |
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Get training image dataset from drive: |
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``` |
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https://drive.google.com/file/d/1U2PljA7NE57jcSSzPs21ZurdIPXdYZtN/view?usp=drive_link |
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``` |
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Place your image dataset in the data/images directory |
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Supported formats: JPG, JPEG, PNG |
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2. **Generate Embeddings:** |
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``` |
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python -m src.preprocessing |
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``` |
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**This will**: |
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- Process all images in the dataset |
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- Show progress and time estimates |
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- Save embeddings to data/embeddings.pkl |
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3. **Run the Web Interface:** |
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``` |
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streamlit run src/main.py |
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``` |
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4. Using the Interface: |
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- Upload a query image using the file uploader |
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- Click "Search Similar Images" |
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- View the most similar images from your dataset |
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## Technical Details |
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**Feature Extraction** |
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- Uses EfficientNetB0 without top layers |
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- Input image size: 224x224 pixels |
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- Output feature dimension: 1280 |
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**Similarity Search** |
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- Uses FAISS IndexFlatL2 for L2 distance-based search |
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- Returns top-k most similar images (default k=5) |
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**Web Interface** |
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- Responsive design with Streamlit |
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- Displays original and similar images with similarity scores |
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- Progress tracking during processing |
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**Dependencies** |
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- TensorFlow 2.x |
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- FAISS-cpu (or FAISS-gpu for GPU support) |
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- Streamlit |
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- Pillow |
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- NumPy |
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- tqdm |
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**Performance** |
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- Feature extraction: ~1 second per image on CPU |
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- Similarity search: Near real-time for datasets up to 100k images |
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- Memory usage depends on dataset size (approximately 5KB per image embedding) |
