vineet019/face-mask-detection-tf2

A face mask detection using ssd with simplified Mobilenet and RFB in Tensorflow 2.1. Training on your own dataset. Can be converted to kmodel and run on the edge device of k210

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_120.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. https://pan.baidu.com/s/1330OWyOL4huB-GWXRkKK6Q code：x5e0

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/*.h5 --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/*.h5 --dataset_path Maskdata/ --split val 
  
   python mAP/compute_mAP.py

Reference

• mAP code: https://github.com/Cartucho/mAP
• SSD-Tensorflow: https://github.com/balancap/SSD-Tensorflow
• ssd-tf2: https://github.com/ChunML/ssd-tf2