Train and Visualize a Face Mask Detection Model - With MobileNet and Augmentations
End-to-End Model Training with Real-World Data
This tutorial uses a face mask detection dataset to walk through a complete training pipeline on Datature Nexus. The task is practical: given an image of a person, determine whether they are wearing a mask, not wearing one, or wearing it incorrectly. It exercises every step from data upload through model evaluation.
What This Tutorial Covers
Datature demonstrates the full cycle:
- Uploading a labeled face mask dataset to a new project
- Reviewing and refining existing annotations
- Selecting MobileNet as the backbone architecture for lightweight inference
- Configuring image augmentations to improve model generalization
- Training the model and visualizing loss curves, precision, and recall
The walkthrough runs about thirteen minutes and shows both the training setup and the evaluation results in detail.
Why MobileNet and Augmentations
MobileNet is designed for efficiency. It runs well on mobile devices and edge hardware where full-size models are too slow. Augmentations like random flipping, rotation, brightness shifts, and cropping create training variety from a limited dataset. Datature lets you preview augmentation effects before committing to a training run.
For more on augmentation strategies, read Performing Image Augmentation for Machine Learning. For the written version of this tutorial, see Train and Visualize a Face Mask Detection Model.
