Update README.md

README.md

@@ -1,117 +1,122 @@
----
-language: en
-license: mit
-tags:
-  - image-search
-  - machine-learning
----
-
-## 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)
+---
+language: en
+license: mit
+tags:
+- image-search
+- machine-learning
+title: Image Similarity Search Engine
+sdk: streamlit
+emoji: 💻
+colorFrom: blue
+colorTo: pink
+---
+
+## 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)