Latest update of this project introduce a sophisticated image analysis enhancement powered by Facebook AI Similarity Search (FAISS) and deep learning feature extraction. This update significantly improves our ability to identify duplicate or similar images, streamlining asset management and enhancing data quality.
Key Features:
- FAISS Vector Database Generation: Leveraging the power of FAISS, we now generate and store high-dimensional feature vectors extracted from images. This allows for efficient similarity searches and duplicate detection.
- Advanced Feature Extraction: Utilizing a pretrained ResNet18 model, we extract meaningful features from images, ensuring high accuracy in identifying similarities.
- Euclidean Distance for Similarity Measurement: By employing Euclidean distance measures, our system accurately finds and groups similar images, aiding in the decluttering and organization of image assets.
- Streamlit Integration: For an improved user experience, we've integrated Streamlit, providing an intuitive interface for progress tracking and interactive data exploration.
Action Required:
- **Update requirements.txt: Given the introduction of new dependencies for FAISS, ResNet18, and Streamlit, please ensure your requirements.txt file is updated accordingly to include these new libraries and any of their dependencies. This is crucial for maintaining project compatibility and operational integrity.
- **Recreate settings.db: To accommodate new configurations and settings introduced with this update, we recommend recreating the settings.db file. This step ensures that all new features are fully supported and operational within your environment.
This update is part of our ongoing effort to provide cutting-edge tools for image management and analysis. We appreciate your feedback and contributions to further enhance this project.