• Extremely fast detection on GPU as well as good inference time on cpu
• Supports both YoloV3 Darknet(CPU) as well as YoloV5 Pytorch(GPU)(Still in devlopment not recommended to use)
• Currently supported maps: Dust(but you can also try other maps)

If you want to use a gpu, you should stick to the pytorch version(model is not accurate right now) and change "device" field in .ini file to gpu. If you want to use cpu, please compare which one is faster(darknet or pytorch) version on your own machine.

For Darknet Choose either detectionTkInterGui.py(still in beta) or detectionOpenCvGui.py(you will be able to see a screen with boxex around predicted models ) For better gpu inference you should use pytorch

Download yolov3-tiny.weights and yolov3-tiny.cfg(you can also use yolov3-tiny-prn_last.weights and yolov3-tiny-prn.cfg for greater speed but lower accuracy) or yolo5s-1.pt for pytorch detection from the following link

Edit friendlyTeam.txt file to add the classes that you want to detect (0 ,1 for Terrorist, Terrorist Head and 2,3 for Counter Terrorist and CT head)

Change capture params in .ini file according to your screen

If you dont want to compile the file or you dont have python you can just go to the release page and run .exe file in the archive

• Using a config file instead of txt(done)
• Add a yolov3-tiny-prn model(done)
• Use Multithreading to read the screen(In progress)
• Make a convinient recording utility to get more data(In progress)

I would highly appreciate anybodys help with my project! If you are interested in working in a team you can contact me!(kir486680@protonmail.com)