A face mask detection app using a custom trained YOLOv4 model from Darknet converted to TensorFlow.

The dataset used for training was taken from Kaggle. It consists of 853 images belonging to the 3 classes, as well as their bounding boxes in the PASCAL VOC format which was later converted to text format (.txt) to be used in the Darknet framework.

It consists of 3 classes:

1. With mask
2. Without mask
3. Mask Weared Incorrect

The detection model used was YOLOv4. Yolo (You Only Look Once) is one of the fastest objection detection algorithms. It can provide object detections in real-time.

These were the config and metrics of the custom YOLOv4 model trained in Darknet.

• Used the baseline weights used in the COCO dataset.
• Number of Epochs: 3000
• Learning Rate: 0.001
• Width and Height: 416x416
• Number of classes: 3
• Mean Average Precision (mAP): 87.02% on the dataset.

# Tensorflow CPU
conda env create -f conda-cpu.yml
conda activate yolov4-cpu

# Tensorflow GPU
conda env create -f conda-gpu.yml
conda activate yolov4-gpu

#If you have CUDA 10.1 Toolkit you can use the GPU requirements.txt
pip install -r requirements-gpu.txt

#For CPU only machines used this
pip install -r requirements-cpu.txt

# Run detections on a webcam
python detect_video.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --video 0 --output ./detections/results.avi

#Run detections on an image
#Put image on /data/images and replace kite.jpg
python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --images ./data/images/kite.jpg

My project is based on these previous fantastic YOLOv4 implementations:

• hunglc007 / tensorflow-yolov4-tflite
• theAIGuysCode / tensorflow-yolov4-tflite