articleloha.blogg.se - Cs go aimbot in mouse

Cs go aimbot in mouse how to#
Cs go aimbot in mouse install#
Cs go aimbot in mouse zip file#
Cs go aimbot in mouse windows 10#
Cs go aimbot in mouse code#

For this tutorial, I wrote the YOLO_aimbot_main.py script, which you can find on my GitHubrepository. If you have experience in Python, it will be pretty easy to understand the main script. There is nothing to talk about about the code. So, I am sure that the latest generation video card could reach much better results, so even if my current results are not so bad, can you imagine what you would get with these new cards? A little about the code: I would like to have NVIDIA 3080 or even 3090 to see what I could get with it. As you can see, FPS is not that great, but I am sure that accuracy is very high. Mostly I would use this model for small maps, where our enemies come closer to us because it's not that accurate with small objects.Īnd the last one is the TensorRT INT8 model with an input size of 608. As you can see, FPS increased more than double. Then I converted my TensorFlow model to the TensorRT INT8 model with an input size of 416.

Cs go aimbot in mouse windows 10#

This is what you can get on Windows 10 with 1080TI GPU, but if you have a newer GPU, you can get better results. So, what do these Frames Per Second results tell us? At first, I used standard YOLO TensorFlow detection without TensorRT optimization.

TensorRT INT8 detection with 608 input size:.

TensorRT INT8 detection with 416 input size:.

TensorFlow detection with 416 input size:.

Anyway, I ran three different test instances: Best to understand my results would be to watch my YouTube video. To make it even more accurate, it's recommended to use more than 10 thousand images in different maps, and so on, then we would be sure that our model won't detect enemies wrong. Most of this training data I generated with the method I explained in my previous tutorial. Check my YouTube tutorial for more.įirst, I should tell you that I used only around 1500 images to train my aimbot model. Also, you may need to change sensitivity or other minor settings.

If the mouse is flying around in-game, open the game console and type m_rawinput 0, this will disable raw game input. When YOLO detects objects on the screen, it should start moving the mouse and shooting the enemies. Now, when you have running the CSGO game in the background, run the YOLO_aimbot_main.py script. You may change YOLO_INPUT_SIZE if you need better accuracy, but you will lose in FPS. My yolov3/configs.py file is already configured for custom trained object detection with an input_size of 416.

Cs go aimbot in mouse install#

Now you need to install all requirements: Now open the checkpoints folder and run linux_unzip_files.py script. Sudo apt-get install unzip unrar p7zip-full, Download P7ZIP with GUI and unzip everything.

Cs go aimbot in mouse how to#

If you are on Linux, there are two ways how to unzip:ġ. If you are on Windows, unzip it using 7zip. I already zipped my trained model, which I put into the checkpoints folder.

Cs go aimbot in mouse zip file#

You can clone it or download it as a zip file it doesn't matter. When you have Steam and CSGO downloaded, we can download my GitHub repository. In steam download Counter-Strike Global Offensive There still may be errors while installing or running Steam, but you should solve them with google help :).ĥ. Then run Steam with the following command in the terminal: But if you are on Linux, it's a little more complicated. Second, if you are on windows, it's pretty easy and obvious how to install Steam. Why ubuntu? Because it's much easier to run TensorRT on Ubuntu than Windows 10, you can try to run it on Windows 10 with the following repository.įirst, you must install TensorFlow, Python 3, Cuda, Cudnn, etc., packages to prepare the TensorFlow environment.

Cs go aimbot in mouse code#

The code was tested on Ubuntu and Windows 10 (TensorRT not supported officially).

Then I used the technique explained in my previous tutorial to generate training data for my model to detect enemies accurately.įirst, I would like to discuss instructions, how you can run it by yourself, results, and lastly, I will talk about code, so there will be three parts. I created a lot of tutorials to explain every part of it. So, first, I took my YOLOv4 GitHub code, on which I was working for half a year, to make it easily understandable and reusable. Why water? To avoid violence, of course.īut I am not talking about what we could do. Also, I thought it would be fun to create a water gun robot that could aim at approaching people and shoot the water. The same concept we could use in real life for some security system, for example, detect and recognize people around our private house and if it's an unknown person aim lights at that person or turn-on some alarm.

What is aimbot? The idea is to create an enemy detector, aim at that enemy and shoot it. After giving you a lot of explanations about YOLO, I decided to create something fun and exciting, which would be a Counter-Strike Global Offensive game aimbot. Welcome to my last YOLOv4 custom object detection tutorial from the whole series.