Issue : Model Development

Tasks: 2.1. Choose an appropriate deep learning framework (e.g., TensorFlow, PyTorch) and set up the development environment. 2.2. Select a suitable architecture for your model, such as a Convolutional Neural Network (CNN), and define its structure. 2.3. Preprocess the dataset, including data augmentation, resizing, and normalization. 2.4. Split the dataset into training, validation, and test sets. 2.5. Train the model on the training data, fine-tuning hyperparameters as needed. 2.6. Implement data augmentation techniques to improve the model's robustness. 2.7. Monitor training progress and track metrics like accuracy and loss. 2.8. Save the trained model and its weights for future use.

Deliverable: A trained machine learning model that can detect face masks in images, and the code for model development and training.

Flyheap / FaceX

Issue : Model Development #1