mc35367/aimaide

Object Detection-based external aim aiding for CS

AimAide for CS

External realtime object detection-based aim aiding powered by YOLOv8, CUDA and TensorRT
Twitter for further development updates

Features

YOLOv8 Models trained on mirage with various CT and T agents (body and head).
Simple smooth linear mouse mover locking onto target closest to crosshair.

Hardware Requirements

To get this to work the detector has to run at 30fps at least.
NVIDIA GTX1070 runs at 30fps on a 640x640 model or 60fps on a 320x320 model with TensorRT.
NVIDIA RTX4090 should max out at ~120fps on a 640x640 model. (also TensorRT)

Usage

I) Disable windows mouse acceleration
II) Disable raw input in CSGO
III) Cap max_fps in CSGO at your native display refresh rate

1) Run "python launcher.py"
2) Depending on your hardware choose from 3 different models (nano, small, medium)
nano (highest framerate, lowest detection performance),
medium (lowest framerate, best decetion performance)
3) Run in benchmark mode first to see what framerate you get (over 60fps increase sensitivity mode)
4) Adjust mouse sensitivity in CS and/or sensitivity mode of AimAide

Arguments

arg	default	Description
--input_size	320	dimension of the input image for the detector
--grabber	'win32'	select screen grabber (win32, d3d_gpu, d3d_np)
--model	models/yolov8s_csgo_mirage-320-v41-al-gen-bg	selected engine (TensorRT) or weights (YOLOv8)
--side	'dm'	which side your are on, 'ct', 't' or 'dm' (deathmatch)
--minconf	0.75	minimum detection confidence
--sensitivity	1	sensitivity mode, increase when having a high framerate or chaotic aim
--flickieness	4	how flicky the mouse mover behaves (4 is slow, 16 is very flicky)
--visualize	False	show live detector output in a new window
--view_only	False	run in view only mode (disarmed)
--benchmark	False	launch benchmark mode
--no_engine_check	False	skips engine checking and building (run_tensorrt.py only)