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Facial Recognition Using YOLOv7 Deep Learning Project

Learn Facial Recognition Using YOLOv7: Deep Learning Project using Roboflow and Google Colab
Instructor:
ARUNNACHALAM SHANMUGARAAJAN
883 students enrolled
English [Auto]
Understand how to seamlessly integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimizat
Explore the process of collecting and preprocessing datasets of faces, ensuring the data is optimized for training a YOLOv7 model.
Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed dataset, adjusting parameters and monitoring model performance.
Understand how to deploy the trained YOLOv7 model for real-world facial recognition tasks, making it ready for integration into applications or security systems

Course Title: Facial Recognition Using YOLOv7: Deep Learning Project using Roboflow and Google Colab

Course Description:

Welcome to the “Facial Recognition Using YOLOv7: Deep Learning Project using Roboflow and Google Colab.” This comprehensive course is designed to take you on a hands-on journey through the process of building a facial recognition system using the state-of-the-art YOLOv7 algorithm. Leveraging the capabilities of Roboflow for efficient dataset management and Google Colab for cloud-based model training, you will acquire the skills needed to implement facial recognition in real-world scenarios.

What You Will Learn:

  1. Introduction to Facial Recognition and YOLOv7:

    • Gain insights into the significance of facial recognition in computer vision and understand the fundamentals of the YOLOv7 algorithm.

  2. Setting Up the Project Environment:

    • Learn how to set up the project environment, including the installation of necessary tools and libraries for implementing YOLOv7 for facial recognition.

  3. Data Collection and Preprocessing:

    • Explore the process of collecting and preprocessing datasets of faces, ensuring the data is optimized for training a YOLOv7 model.

  4. Annotation of Facial Images:

    • Dive into the annotation process, marking facial features on images to train the YOLOv7 model for accurate and robust facial recognition.

  5. Integration with Roboflow:

    • Understand how to seamlessly integrate Roboflow into the project workflow, leveraging its features for efficient dataset management, augmentation, and optimization.

  6. Training YOLOv7 Model:

    • Explore the end-to-end training workflow of YOLOv7 using the annotated and preprocessed dataset, adjusting parameters and monitoring model performance.

  7. Model Evaluation and Fine-Tuning:

    • Learn techniques for evaluating the trained model, fine-tuning parameters for optimal facial recognition, and ensuring robust performance.

  8. Deployment of the Model:

    • Understand how to deploy the trained YOLOv7 model for real-world facial recognition tasks, making it ready for integration into applications or security systems.

  9. Ethical Considerations in Facial Recognition:

    • Engage in discussions about ethical considerations in facial recognition, focusing on privacy, consent, and responsible use of biometric data.

You can view and review the lecture materials indefinitely, like an on-demand channel.
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Includes

1 hours on-demand video
Certificate of Completion

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