Face Mask Detection using Deep Learning | OpenCV and Keras Tutorial

Mask-detection-with-realtime

Prerequisites:

Python
OpenCV
Keras
TensorFlow
Dataset:
The model is trained on a dataset containing images of people with and without face masks. The dataset is available on [provide_dataset_source].

Key Steps:

Data Preprocessing: Resizing and normalizing images.
Model Architecture: A CNN with multiple convolutional and dense layers.
Training: Using Adam optimizer and binary crossentropy loss.
Evaluation: Metrics include accuracy and F1 score.
Real-time Detection: Utilizing Haar Cascade for face detection.
Testing on Video: Demonstrating the model on a video file.
Results:
The model achieves [mention_results], demonstrating its effectiveness in detecting face masks.

Note:
Feel free to customize the code for your specific use case. The trained model can be saved and used for real-time detection in various applications.

Resources:

OpenCV Documentation
Keras Documentation
TensorFlow Documentation

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