Face Mask Detection Model using TensorFlow

Utilizing TensorFlow, this project creates a deep learning model that identifies face mask usage in images with approximately 98% training and 95% validation accuracy.

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Sub-category:

Deep Learning

TensorFlow

Overview:

This project aims to develop a face mask detection model using deep learning techniques with the TensorFlow framework. The model is trained from scratch on a dataset of face images, where the images are labeled as "with mask" or "without mask." The model architecture consists of a combination of convolutional and fully connected layers, achieving a training accuracy of around 98% and a validation accuracy of approximately 95% on a 20% validation dataset.

Description:

The Face Mask Detection Model project utilizes deep learning to create a model capable of accurately detecting whether individuals are wearing face masks based on facial images. The training dataset consists of face images, carefully labeled as either "with mask" or "without mask." These images are collected from various sources and preprocessed to ensure consistency and quality.

The model architecture consists of multiple convolutional layers followed by fully connected layers. This combination allows the model to effectively learn and extract features from the input images, enabling accurate mask detection. The training process involves optimizing the model's weights using the Adam optimizer and the binary cross-entropy loss function.

To evaluate the model's performance, a separate validation dataset is created, comprising 20% of the original dataset. The model's accuracy is measured on this validation dataset, providing insights into its ability to generalize to unseen data. The achieved results demonstrate a training accuracy of approximately 98% and a validation accuracy of around 95%, indicating the model's effectiveness in detecting face masks from facial images.

By analyzing live video streams or static images, the model can identify individuals who are not wearing masks, helping to enforce safety protocols and prevent the spread of diseases. The TensorFlow framework provides a robust and efficient platform for developing and deploying deep learning models, making it an ideal choice for face mask detection tasks.