Dataset link : https://www.kaggle.com/datasets/omkargurav/face-mask-dataset

This project aims to detect whether a person is wearing a face mask or not using deep learning techniques with TensorFlow and Keras. It utilizes a custom Convolutional Neural Network (CNN) architecture trained on a dataset consisting of 7553 RGB images labeled as 'with_mask' and 'without_mask'.

The dataset contains 7553 RGB images categorized into two folders:

• with_mask: Contains 3725 images of faces wearing masks.
• without_mask: Includes 3828 images of faces without masks.

The model architecture comprises:

• Input layer: (128, 128, 3)
• Convolutional layers with ReLU activation
• MaxPooling layers for down-sampling
• Dropout layers to prevent overfitting
• Dense layers with ReLU activation
• Output layer with 'sigmoid' activation for binary classification
• The model achieved a training accuracy of 94% and a validation accuracy of 96%.

• Provide the path of the image to be predicted.
• The model will analyze the image and predict whether the person is wearing a mask or not.

The model's performance:

• Training accuracy: 94%
• Validation accuracy: 96%