pip install -r requirements.txt

Download the files and put them in a folder named models: Leo Google Drive

For training, we used the following dataset from Kaggle Mask Datasets V1. Put the files into a folder named dataset with subfolders train and test. Each folder train and test obtain subfolders mask and no_mask.

python png_to_hdf5.py

If you have not download the dataset for Multi-Person Object Detection and created the appropriate folder structure yet. Run:

python png_to_hdf5.py --mode train
python png_to_hdf5.py --mode test

python SVM.py

MobileNetV2 (Sandler et al., 2018)

python train.py

python train.py --train_mode finetune

For training, we used the following dataset from Kaggle Medical Mask Dataset. Put the files into a folder named dataset/detection with subfolders images and labels. Go To subfolder dataset/detection/images and delete the file 83855_1580055989W0WA.jpg.

If you have not execute png_to_hdf5.py yet, run:

python png_to_hdf5.py

Otherwise run:

python png_to_hdf5.py --mode detection

Faster-RCNN (Ren et al., 2015)

python train.py --detection --train_mode faster_rcnn

MTCNN (Zhang et al., 2016)

python train.py --detection --train_mode mtcnn

For the moment, your image has to be in the same folder as run.py:

python run.py --image_path

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksarXiv e-prints, arXiv:1506.01497.

Sandler, M., Howard, A., Zhu, A., & Chen, L.C. (2018). MobileNetV2: Inverted Residuals and Linear BottlenecksarXiv e-prints, arXiv:1801.04381.

Szegedy, C., Liu, W., Jia, P., Reed, S., Anguelov, D., Vanhoucke, V., & Rabinovich, A. (2014). Going Deeper with ConvolutionsarXiv e-prints, arXiv:1409.4842.

Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint Face Detection and Alignment Using Multitask Cascaded Convolutional NetworksIEEE Signal Processing Letters, 23(10), 1499-1503.

[email protected]

MIT License