Game Hacking With Artificial Intelligence - FPS Aimbot

Collect data from inside Valorant to build an image processing dataset with diverse angles, distances, and backgrounds, aiming for at least 1,000 samples; I will share all components via Udemy.

Train a yolov5 model from the ground up by setting up client libraries, organizing datasets in drive folders, and labeling images with consistent class order for effective object detection.

Train a yolov5 model by adjusting data size to 640, tuning the base size and epochs for speed, address memory errors by lowering to 64, and save the model safely.

Install CUDA by selecting your operating system and architecture, downloading the correct installer, and following prompts to accept terms, choose the same version, and complete the setup.

Code a target detecting script using the previous model, install required libraries, and visualize detections as rectangles around targets in real time.

Learn how to install Arduino Leonardo, configure USB host, upload, and confirm a successful setup on the new system.

Learn to prepare the USB host on the VSP host board, install the USB host shield library, configure the port, upload the example code, and troubleshoot installation issues.

Explore the mouse driver script server, detailing algorithms to generate movement distance, parse incoming messages, and drive pixel motion directions with testing and master driver logic.

Connects a detecting algorithm with a mouse driver to calibrate aim parameters, imports keyboard and serial packages, and tests an fps aimbot in game via serial communication.

The video demonstrates testing the aimbot system in a game, showing how the software detects targets and automatically hits them. The test covers difficult settings to evaluate reliability.