Harsha Vardan Maradam

Graduate with good research, technical, and problem-solving skills. Detail-oriented and able to learn new concepts quickly.

• Face Mask Detection Using MobileNet and Global Pooling
  
  Developed a deep learning model for automated face mask detection using MobileNet and a global pooling block.
  
  Implemented transfer learning to enhance accuracy while reducing computational costs.
  
  Evaluated the model on two public datasets, achieving up to 100% accuracy in detecting masked and unmasked faces.
  
  Optimized model performance with global pooling, reducing overfitting and improving training efficiency.
  
  Conducted experiments using Python and TensorFlow on high-performance hardware, demonstrating superior results over existing models.