Face Recognition System ‐ Research‐Oriented Pipelines & Model Fine‐Tuning

Welcome to the Face Recognition System project! This project is focused on building cutting-edge face recognition pipelines, with a strong emphasis on model fine-tuning and research. It integrates multiple backbone architectures, vector databases, and tools to enhance the accuracy and efficiency of face recognition models.

Whether you're an AI researcher, a machine learning engineer, or a contributor looking to explore face recognition, this repository provides flexible tools for training, evaluating, and experimenting with models. The project is designed to be modular and easy to extend, offering support for:

• Multiple Vector Databases: Includes Milvus, Weaviate, Pinecone, and MongoDB Atlas for facial embedding searches.
• Model Fine-Tuning Pipelines: Automated pipelines for fine-tuning various backbone models like ResNet, EfficientNet, etc.
• Cloud-Friendly Environments: Pre-configured for easy use in Kaggle and Colab environments, making it accessible for large-scale experiments. Dive into the documentation, get started with our PyPI package, and join us in pushing the boundaries of face recognition research!

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Here’s a list of project issues and tasks aligned with our goals for converting the face recognition system into a more modular, research-oriented PyPI package.

Updated Project Issues

1. Support for Multiple Vector Databases:

   • Task: Refactor the current system to support multiple vector databases (Milvus, Weaviate, Pinecone) alongside MongoDB Atlas.
   • Approach: Design a modular interface for integrating different vector DBs, allowing easy switching based on configuration.
   • Outcome: Increase the flexibility of the face recognition system to work with various vector DBs.

2. Refactor Code for Tight Coupling:

   • Task: Refactor the code to introduce tight coupling and organized module structures without using classes.
   • Approach: Group related functionalities into compact modules while maintaining readability and ease of debugging. Use functional programming paradigms where applicable.
   • Outcome: Cleaner, more maintainable code that remains easy to work with and debug.

3. Pipeline Refactoring for Linting & Environment Compatibility:

   • Task: Separate the pipelines into distinct files to improve code organization, linting, and ease of use in Kaggle and Colab environments.
   • Approach: Create individual files for model training, evaluation, and fine-tuning pipelines with clear documentation for easy execution.
   • Outcome: Simplified workflows for training in cloud environments and improved code cleanliness.

4. Create a PyPI Package for Model Training and Evaluation:

   • Task: Package the face recognition system as a PyPI library, making it accessible to the broader community.
   • Approach:
     1. Refactor and organize the code for easy packaging.
     2. Add setup.py or use poetry for dependency management.
     3. Ensure the package contains essential features like training, evaluation, vector DB integration, and utility functions.
   • Outcome: A modular, research-oriented PyPI package for training and evaluating face recognition models with multiple backbones.

5. Integrate Different CNN Backbones for Research:

   • Task: Add support for multiple CNN backbones like ResNet, EfficientNet, and others.
   • Approach: Create reusable modules that allow users to switch between different backbone architectures and fine-tune models.
   • Outcome: Researchers can experiment with various models, improving face recognition performance.

6. Vector Embedding Search Optimization:

   • Task: Implement optimizations for embedding search using different indexing strategies or caching mechanisms to improve query performance in vector DBs.
   • Approach: Use database-specific features like approximate nearest neighbor (ANN) search for faster lookups.
   • Outcome: Faster and more efficient face recognition searches across different vector DBs.

7. Automated Model Training and Fine-Tuning Pipeline:

   • Task: Build an automated pipeline that trains and fine-tunes models, handling hyperparameter tuning and evaluation automatically.
   • Approach: Develop scripts that can run in Kaggle/Colab environments with minimal setup, allowing researchers to focus on experimentation.
   • Outcome: A streamlined process for model experimentation and fine-tuning in cloud environments.

8. Model Evaluation and Explainability Tools:

   • Task: Add tools to evaluate models and visualize facial features contributing to recognition.
   • Approach: Implement explainability techniques like Grad-CAM to understand model behavior.
   • Outcome: Improved transparency and understanding of how models make decisions.