Objectives for multi-task learning approach :
Primary task - to detect faces that have their masks worn correctly or incorrectly
Secondary task - to detect faces that have their mask only covering the nose and mouth; masks only covering mouth and chin and mask under the mouth (i.e three cases of mask incorrectly worn)
Models for Face mask detection :
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MobileNet
Facemask-detection-task1.ipynb - to train MobileNet for primary task Facemask-detection-task2.ipynb - to train MobileNet for secondary task
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BKNet
Evalsingletask.ipynb - to train BKNet for primary task - Training BKNet for both primary and secondary tasks: Training - BKNetMultitask/BKNet_multitask_train.ipynb Evaluation - BKNetMultitask/BKNet_multitask_evaluate.ipynb Model implementation - BKNetMultitask/BKNetStyle.py
The following papers and code were used for this project
Sang, Dinh & Bao, Cuong. (2018). Effective Deep Multi-source Multi-task Learning Frameworks for Smile Detection, Emotion Recognition and Gender Classification. Informatica. 42. 10.31449/inf.v42i3.2301. https://github.com/truongnmt/multi-task-learning
Cabani et al., "MaskedFace-Net - A dataset of correctly/incorrectly masked face images in the context of COVID-19", Smart Health, ISSN 2352-6483, Elsevier, 2020,