
Train an air model by collecting and labeling in-game data, build a target-detection system, and share training materials, while noting it cannot be used in ranked or normal games.
Hello, I leave the sources here. You can follow the course using it.
Write a Python script to collect data inside games by capturing centered screenshots and saving them, after setting up a virtual environment and installing opencv-python, numpy, and keyboard.
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.
Master data labeling for object detection by organizing a dataset into labeled folders, annotating upper-body regions, exporting label files, and preparing training data for model training.
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.
Explore the limits of game input manipulation and safety systems, introducing the mouse driver theorem and a hands-on, educational installation built from scratch.
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.
Attention Please: The ai model in this course is trained to detects bots only. Cannot be used in online matches.
Watch this course for information purposes. Using the methods in this course may get you banned in the game.
Rapidly developing artificial intelligence has outperformed humans in the esports sector, as in most sectors.
I think the use of 3rd party artificial intelligence software in games will increase in the future.
In this course we give a simple introduction to this technology.
This course aims to make auto aiming bot at fps games.
This course contains:
how to easily collect data from games,
how to label your dataset,
how to train your custom model with Yolov5,
how to use detecting with your custom model,
how to make mouse driver for be able to give mouse input to all games.
With the mouse driver we have made, you can give mouse input to all games.
I published on udemy all materials used in this course like dataset, scripts, arduino leonardo scripts, trained model.
You can follow course easily with use this materials.
I did not explain the codes I wrote in this course in detail. It would be helpful to have basic python knowledge. It will be beneficial for you to know how to install programs such as cuda and python on your computer.
If you use the techniques in this course, there is a possibility that you will be punished by the game be carefull.