Update README.md

Author: ibadrather

README.md

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 # RevSearch
-Reverse Image search based on AI
+
+RevSearch is a Minimum Viable Product (MVP) showcasing a car reverse image search application. It leverages cutting-edge machine learning, computer vision, and cloud-based technologies to provide an efficient and accurate image search experience. Built as a self-initiative, this project demonstrates end-to-end machine learning workflow expertise.
+
+**Note:** The deployment has been taken down due to running costs.
+
+---
+
+## Workflow
+
+1. **Image Upload**: Users upload an image in various formats (JPG, PNG, BMP, etc.).
+2. **Feature Encoding**: The uploaded image is encoded into a feature vector using a trained neural network encoder (EfficientNet).
+3. **Similarity Search**: The encoded feature vector is compared to precomputed feature vectors in the database.
+4. **Top Matches**: The system retrieves the top 10 image URLs based on similarity scores.
+5. **Image Retrieval**: Top 10 images are fetched from AWS S3 storage via AWS API Gateway and AWS Lambda.
+
+> **Note**: RevSearch is currently not deployed due to associated operational costs.
+
+---
+
+## Project Details
+
+- **Company**: Self-initiative project for applying end-to-end machine learning workflows.
+- **Timeline**: April 2022 - May 2022
+- **Codebase**:
+  - Backend, Frontend, and Core Technology
+    - [DeepImageSearchAPI](https://github.com/ibadrather/DeepImageSearchAPI)
+    - [RevSearch](https://github.com/ibadrather/RevSearch)
+
+---
+
+## Key Features
+
+- Upload images in various formats (JPG, PNG, BMP, etc.) for reverse search.
+- Interactive slider to select up to 6 similar images.
+- Powered by the EfficientNet neural network architecture for accurate feature extraction.
+- Responsive and seamless user experience powered by FastAPI.
+- Fully interactive **Reverse Image Search WebUI**.
+
+---
+
+## Technologies Used
+
+| **Category**         | **Technologies**                                                      |
+|-----------------------|----------------------------------------------------------------------|
+| Core Technologies     | Python, PyTorch, ONNX, ONNX Runtime, Pandas                         |
+| Data Preprocessing    | Albumentations                                                      |
+| Model Optimization    | MLflow, Optuna                                                      |
+| Web App & Deployment  | Streamlit, FastAPI, Docker, Heroku                                  |
+| Cloud Services        | AWS S3, AWS API Gateway, AWS Lambda                                 |
+| CI/CD & Code Quality  | GitHub Actions, Black, Pytest                                       |
+| Image Processing      | PIL                                                                 |
+
+---
+
+## About the Dataset
+
+- **Source**: Stanford University AI Lab’s Cars dataset.
+- **Composition**:  
+  - 16,185 images across 196 car classes.  
+  - Serves as the foundation for the feature extractor (encoder).
+
+---
+
+## DeepSearchLite Integration
+
+RevSearch integrates **DeepSearchLite**, a custom lightweight library, for fast and efficient similarity searches with minimal dependencies.  
+Find it on PyPi: [DeepSearchLite](https://pypi.org/project/DeepSearchLite/)
+
+---
+
+## Challenges and Future Improvements
+
+1. **Dataset Limitations**:
+   - Cars dataset (16,185 images) is outdated, impacting accuracy for newer models.
+2. **MVP Status**:
+   - Currently demonstrates potential but requires further enhancements.
+3. **Next Steps**:
+   - Expand the dataset to include more images and newer models.
+   - Refine search algorithms for improved accuracy and speed.
+   - Incorporate user feedback for additional features and functionality.
+
+---
+
+This project highlights expertise in advanced technologies and practical solutions for the automotive domain.