A face mask detection using ssd with simplified Mobilenet and RFB or Pelee in Tensorflow 2.1. Training on your own dataset. Can be converted to kmodel and run on the edge device of k210
This model is a lightweight face mask detection model. Based on ssd,the backbone is Mobilenet and RFB.
tf.GradientTape
tf.keras
tf.data.TFRecordDataset
├── assets
│ ├── 1_Handshaking_Handshaking_1_71.jpg
│ ├── out_1_Handshaking_Handshaking_1_71.jpg
│ ├── out_test_00002330.jpg
│ └── test_00002330.jpg
├── checkpoints
│ └── weights_epoch_100.h5
├── components
│ ├── config.py
│ ├── __init__.py
│ ├── kmeans.py
│ ├── prior_box.py
│ └── utils.py
├── dataset
│ ├── check_dataset.py
│ ├── tf_dataset_preprocess.py
│ ├── train_mask.tfrecord
│ ├── trainval_mask.tfrecord
│ ├── val_mask.tfrecord
│ ├── voc_to_tfrecord.py
├── inference.py
├── logs
│ └── train
├── mAP
│ ├── compute_mAP.py
│ ├── detection-results
│ ├── detect.py
│ ├── ground-truth
│ ├── __init__.py
│ ├── map-results
│ └── README.md
├── Maskdata
│ ├── Annotations
│ ├── ImageSets
│ └── Main
│ │ ├── train.txt
│ │ ├── trainval.txt
│ │ └── val.txt
│ └── JPEGImages
├── network
│ ├── __init__.py
│ ├── losses.py
│ ├── model.py
│ ├── net.py
│ ├── network.py
├── README.md
└── train.py
└── requirements.txt
Create a new python virtual environment by Anaconda ,pip install -r requirements.txt
Face Mask Data
Source data from AIZOOTech , which is a great job.
I checked and corrected some error to apply my own training network according to the voc dataset format. You can download it here:
Data Processing
Download the mask data images
Convert the training images and annotations to tfrecord file with the the script bellow.
python dataset/voc_to_tfrecord.py --dataset_path Maskdata/ --output_file dataset/train_mask.tfrecord --split train
you can change the --split parameters to 'val' to get the validation tfrecord, Please modify the inside setting voc_to_tfrecord.py
for different situations.
Check tfrecord dataloader by run python dataset/check_dataset.py
.
Modify your configuration in components/config.py
.
You can get the anchors by run python components/kmeans.py
Train the model by run python train.py
.
Run on video
python inference.py --model_path checkpoints/ --camera True
or
python inference.py --model_path checkpoints/*.h5 --camera True
Detect on Image
python inference.py --model_path checkpoints/ --img_path assert/1_Handshaking_Handshaking_1_71.jpg
Convert xml to txt file on mAP/ground truth
, predicting the bbox and class on mAP/detection-results
.
python mAP/detect.py --model_path checkpoints/ --dataset_path Maskdata/ --split val
python mAP/compute_mAP.py
something refer to k210-camera-project.