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Computer Vision




Python, TensorFlow, PyTorch

Skills to be Learned:

Object detection methodologies

Face Mask Detection

This comprehensive course is dedicated to teaching computer vision and deep learning techniques for detecting face masks in images and videos. With the growing need for such technology in public health and safety, especially in the wake of a pandemic, this course is timely and relevant. Students will acquire skills to develop sophisticated face mask detection systems applicable in various environments like public spaces, workplaces, and healthcare facilities.

This course is a specialized project-based course focused on computer vision and deep learning techniques for the detection of face masks in images and videos. Face mask detection has gained significant importance in recent times due to its application in public health, safety, and compliance with health guidelines. 

This course aims to provide students with the knowledge and skills required to develop face mask detection systems, which can be used in various scenarios, including public spaces, workplaces, and healthcare facilities. Participants will learn to build and deploy deep learning models that can identify whether individuals in an image or video are wearing face masks or not, contributing to safety measures in a pandemic and beyond.

Learning Outcomes:

Upon successful completion of this course, students will:

- Gain expertise in computer vision and object detection techniques.

- Understand the significance and applications of face mask detection.

- Proficiently program in Python and work with deep learning frameworks.

- Learn data preprocessing and augmentation for training detection models.

- Develop and fine-tune deep neural networks for accurate face mask detection.

- Evaluate model performance using appropriate metrics.

- Implement real-world face mask detection solutions.


- Proficiency in Python programming.

- Basic knowledge of machine learning and deep learning concepts.

- Familiarity with deep learning frameworks like TensorFlow or PyTorch is beneficial but not mandatory.

- Prior experience with computer vision concepts is helpful but not required.

Libraries and Programming Language Used:

- Programming Language: Python

- Deep Learning Framework: TensorFlow or PyTorch

- Computer Vision: OpenCV

- Numerical Computing: NumPy

- Data Visualization: Matplotlib

Course Syllabus:

Introduction to Face Mask Detection

   - Understanding the relevance and applications of face mask detection.

   - Overview of object detection techniques.

Setting Up the Development Environment

   - Installing Python and essential libraries.

   - Configuring the environment for computer vision and deep learning projects.

Exploring Face Mask Detection Datasets

   - Introduction to datasets containing images with and without masks.

   - Data loading, preprocessing, and annotation.

Data Preprocessing for Face Mask Detection

   - Techniques for preparing image data for model training.

   - Augmentation strategies to improve model robustness.

Building Face Mask Detection Models

   - Creating and training object detection models using CNN architectures.

   - Customizing models for specific face mask detection tasks.

Evaluating Face Mask Detection Model

   - Understanding evaluation metrics for object detection accuracy.

   - Assessing model effectiveness and limitations.

Real-world Applications and Deployment

   - Deploying trained models for real-time face mask detection.

   - Integration with cameras and video streams.

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