| # Face Mask Detection | |
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| Detecting face mask with OpenCV and TensorFlow. Using simple CNN or model provided by TensorFlow as MobileNetV2, VGG16, Xception. | |
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| ## Data | |
| Raw data collected from kaggle and script `crawl_image.py`, split to 'Mask' and 'Non Mask' class. | |
| Using `build_data.py` to extract faces from raw dataset and resize to 64x64. | |
| ## Installation | |
| Clone the repo | |
| ``` | |
| git clone [email protected]:ksvbka/face-mask-detector.git | |
| ``` | |
| cd to project folder and create virtual env | |
| ``` | |
| virtualenv .env | |
| source .env/bin/activate | |
| pip install -r requirements.txt | |
| ``` | |
| Download raw dataset and execute script build_dataset.py to preprare dataset for training | |
| ``` | |
| cd data | |
| bash download_data.sh | |
| cd - | |
| python3 build_dataset.py --data-dir data/dataset_raw/ --output-dir data/64x64_dataset | |
| ``` | |
| ## Training | |
| Execute `train.py` script and pass network architecture type dataset dir and epochs to it. | |
| Default network type is MobileNetV2. | |
| ``` | |
| python3 train.py --net-type MobileNetV2 --data-dir data/64x64_dataset --epochs 20 | |
| ``` | |
| View tensorboard | |
| ``` | |
| tensorboard --logdir logs --bind_all | |
| ``` | |
| ## Testing | |
| ``` | |
| python3 mask_detect_image.py -m results/MobileNetV2-size-64-bs-32-lr-0.0001.h5 -i demo_image/2.jpg | |
| ``` | |
| ## Result | |
| Hyperparameter: | |
| - batch size: 32 | |
| - Learing rate: 0.0001 | |
| - Input size: 64x64x3 | |
| Model result | |
| | Model | Test Accuracy| Size | Params | Memory consumption| | |
| | ------------- | -------------|-------------|-----------|-------------------| | |
| | CNN | 87.67% | 27.1MB | 2,203,557 | 72.58 MB | |
| | VGG16 | 93.08% | 62.4MB | **288,357** | **18.06 MB** | |
| | MobileNetV2 (fine tune) | 97.33% | **20.8MB** | 1,094,373 | 226.67 MB | |
| | **Xception** | **98.33%** | 96.6MB | 1,074,789 | 368.18 MB | |
| Download pre-trained model: [link](https://drive.google.com/u/0/uc?id=1fvoIX1cz3O8yF3VNfneoM0AK7bR5ok7T&export=download) | |
| ## Demo | |
| Using MobileNetV2 model | |
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