• It's an improved version of a very old version of YOLOv3, YOLOv7 will be used in the next release
• It's the CPU 80%-GPU 20% version only by using OpenCV , in the next release it will be developed to work on the GPU by CUDA to make it fast and work more accurately, because building OpenCV with CUDA is a complicated process , and i will make it in easy way.
• You don't need RTX to make it work.
• This release is currently optimized for cs2, valorant, fps games, etc..
• Objects detector gui to make you see how it recognizes the model.

PS: if you get this messege [WARN:[email protected]], it's a warning issued by the opencv library because it's unable to recognize the CPU because it interferes with CUDA because GPU is disabled in this version, It's okay, it's just a warning, ignore this message because it doesnt stop the program from working, just a warning I will solve this bug in the next release.

when use AIMi you will see slight vibration in aimbot because its a beta version, everything will be improved in the next releases, it will be better more accurate in the next releases because it works very well for some people and it works well for me, it varies according to the processors that can analyze the neural network , but still work fine .

1. Download Python.
2. Download AIMi.
3. Extract it.
4. Disable mouse enhance pointer precision, Mouse Properties->Pointer Options->Enhance Pointer Precision

PS: You do not need to do anything else, just open the program from CMD or double-click, and it will download all the required libraries by it self.

5. Python start.py or double-click.

• (F1) Aimbot: Always On/Hold Mode
• (Mouse4) Hold Mode: Press/Release
• (0) Exit
• With gui objects detector.

Neural network aimbots are great for a lot of reasons... Probably most importantly, it never access the memory of the game, so it's practically invisible to "Anti-Cheat" sofware, additionally, they can abstract it capabilities to many different games without code modifications.

The detection network is trained on a combination of video game images and the COCO dataset, it is optimized to recognize human-like objects as quickly as possible, the detection engine can be abstracted to many FPS games.

To know more about yolov3

it provide an abstraction layer for working with the screen capture data, but it it also allows us to harness the power of GPU hardware by interfacing CUDA and OpenCL, it uses OpenCV to process that image into a form that is recognizable to the object-detection engine.