Mask detection and classification in thermal face images
This repository allows testing mask detection and classification models on thermal images.
The proposed solution based on semi-supervised CNN with Convolutional Autoencoder (CAE) was provided in the classification folder. This model achieved 91% accuracy in face mask classification on thermal images. To test the model you can use thermal images of three types of masks given in the images folder.
Requirements
For installing the required packages, run the following command:
pip install -r requirements.txt
Classify image
To run mask classification you should use:
>> python mask_classification.py --img_path <path to image>For example:
python mask_classification.py --img_path ../images/cloth.jpg
The best model weights are provided in the detection folder - Yolov5 in "nano" version trained on a thermal images dataset with weights obtained on the COCO set. For this model obtained metrics were:
- precision 0.964 ± 0.025,
- recall 0.935 ± 0.006,
- mAP50 0.970 ± 0.013.
To detect mask on face you can also use images given in images folder.
To run detection you should use Yolov5 by Ultralytics.
>> python detect.py --weights <path to weights> --source <path to image>For example:
python detect.py --weights best.pt --source cloth.jpg
Kowalczyk, N., and Rumiński J. "Mask detection and classification in thermal face images." - in review