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Face mask detection

This model is a lightweight face mask detection model. Based on ssd,the backbone is Mobilenet and RFB.

Key Features

  • Tensorflow 2.1
  • Trainging and Inference
  • Precision with mAP
  • Eager mode training with tf.GradientTape
  • Network function with tf.keras
  • Dataset prepocessing with 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

Usage

Installation

Create a new python virtual environment by Anaconda ,pip install -r requirements.txt

Data Preparing

  1. 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:

  2. 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.

  3. Check tfrecord dataloader by run python dataset/check_dataset.py .

Training

  1. Modify your configuration in components/config.py.

    You can get the anchors by run python components/kmeans.py

  2. Train the model by run python train.py .

Inference

  • 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

mAP

  • 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

For K210 project:

something refer to k210-camera-project.

Reference