This tutorial is one of the last tutorials from my YOLO object detection tutorial series. So, after covering I think almost everything about YOLO, I thought that it would be useful to implement something interesting and fun. Then I remembered my old  Counter-Strike Global Offensive TensorFlow aimbot, where I used TensorFlow to detect enemies. This project was unsuccessful because when TensorFlow was receiving an image where it detects enemies there the bottleneck was coming. FPS was dropping to 4–5 frames per second, and it becomes impossible to play this game for our bot. Right now I have YOLO, which is much better, so I can revive my project.

Also, right now I am on Linux before I was on Windows 10. But, because I plan to use TensorRT ( in my previous tutorial I did speed comparison) I decided to say on Linux. It’s simpler to use TensorRT on Linux than on Win10. Off-course, there is a  method on how to install TensorRT on Win10, but for me, it’s simpler to  install Steam on Linux.

To start with, I downloaded the same CSGO_training data I used last time. I followed my own YOLOv4 tutorial to train a custom object detector that I used to generate new training data, to achieve better accuracy than last time.

#deep-learning #computer-vision #machine-learning #tensorflow