MASK-DECTECTION-YOLO-V$

Mask Detection using YOLO v4.

Steps to execute:

• Create Dataset

  • Collect all images. Classes should be balanced.
  • Augement dataset using tools like RoboFlow, etc.
  • Anotate images to convert into txt file(Yolo input data format) using tools like LabelImg,Bbox etc.
  • Create train.txt which contains path of all input data will be used in training.
  • Create test.txt which contains path of all input images will be used in validation.

• Download Darknet

  • Darknet GIT Repo.

• Compile

  • Use ./make command from command line to compile all C++ files.

• Download Predefined weights

  • yolov4.conv.137

• Prepare Input data format structure

  • Copy Input data from step 1 to data folder under darknet folder
  • Create .names files which contains classes which need to detect.
  • Create .data files contains
    • classes= no of classes
    • train = data/train.txt
    • valid = data/test.txt
    • names = data/mask.names
    • backup = backup/ [where all weight files be stored]

• Train model

  • !./darknet detector train .datafile_path configfile_path weightfile_path -dont_show -i 0 -map

• Test Model

  • Test against images
    !./darknet detector test data/mask.data cfg/yolov4-custom.cfg backup/yolov4-custom_last.weights TESTDATA/JEN.jpeg -thresh 0.3 imShow('predictions.jpg')
  • Test against video
    !./darknet detector demo data/mask.data cfg/yolov4-custom.cfg backup/yolov4-custom_best.weights -dont_show TESTDATA/MASK_TEST.mp4 -i 0 -out_filename TESTDATA/prediction.avi

• Model Evaluation:

Parameter	Count
TRAIN DATA	5000
MASK	ap = 92.73%
NOT MASK	ap = 82.81%
precision	0.87
recall	0.89
F1-score	0.89
mean average precision ([email protected])	0.877713, or 87.77 %
FPS	AVG:52
Total Detection Time	5 Seconds

Weight Files:

Trained Weights

Predictions: