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Face Recognition
My Hugging Face Space

My Hugging Face Space

Identify and track faces in a live video stream

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What is My Hugging Face Space ?

My Hugging Face Space is a face recognition application designed to identify and track faces in live video streams. It leverages cutting-edge AI technology to provide accurate and real-time facial detection, making it a valuable tool for surveillance, analytics, and interactive applications.

Features

  • Real-time Face Detection: Detect faces in live video streams with high accuracy.
  • Face Tracking: Continuously track detected faces across frames for seamless monitoring.
  • Integration with Hugging Face Ecosystem: Easily integrate with other AI models and libraries.
  • Customizable Models: Use pre-trained models or fine-tune them for specific use cases.
  • User-Friendly Interface: Intuitive design for easy deployment and interaction.
  • Cross-Platform Compatibility: Works on multiple operating systems and devices.

How to use My Hugging Face Space ?

  1. Install Required Dependencies: Ensure you have the necessary libraries installed (e.g., OpenCV, PyTorch).
  2. Launch the Application: Run the application using the provided script or interface.
  3. Configure Settings: Adjust settings for model accuracy, frame rate, and other parameters.
  4. Start Video Stream: Feed a live video stream to the application (e.g., webcam, RTSP stream).
  5. Monitor Results: Observe detected faces and their tracking in real-time.

Frequently Asked Questions

What models are supported by My Hugging Face Space?
My Hugging Face Space supports a variety of pre-trained face recognition models, including popular ones from the Hugging Face Model Hub. You can also upload and use your custom models.

Can I use My Hugging Face Space for commercial purposes?
Yes, My Hugging Face Space can be used for commercial purposes. However, ensure compliance with licensing terms of any third-party models or libraries used.

How do I improve the accuracy of face detection?
You can improve accuracy by fine-tuning models with your dataset, adjusting model parameters, or using higher-resolution video inputs.

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