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neural-network-aimbot

This is an undetected aimbot that works with the YOLOv3-tiny neural network object detection. You can train her on any of your favorite games.

Requirements:

How does aimbot work?

Aimbot uses WinAPI functions to move the mouse cursor to the center of the area detected by the neural network each frame.

Why YOLOv3-tiny?

YOLOv3 is optimized to recognize human-like entities well. YOLO is often cited as one of the fastest architectures for deep learning-based object recognition, achieving a higher frame rate per second. YOLOv3, for example, is more than 1000 times faster than R-CNN, and 100 times faster than Fast R-CNN.

YOLOv3-tiny is a "lightweight" (so to speak) model of YOLOv3, optimized for higher FPS and work on devices like the Raspberry Pi, but it pays for its speed with accuracy.

image

Performance on the COCO dataset

Model Train Test mAP FLOPS FPS Cfg Weights
SSD300 COCO trainval test-dev 41.2 - 46 link
SSD500 COCO trainval test-dev 46.5 - 19 link
YOLOv2 608x608 COCO trainval test-dev 48.1 62.94 Bn 40 cfg weights
Tiny YOLO COCO trainval test-dev 23.7 5.41 Bn 244 cfg weights
SSD321 COCO trainval test-dev 45.4 - 16 link
DSSD321 COCO trainval test-dev 46.1 - 12 link
R-FCN COCO trainval test-dev 51.9 - 12 link
SSD513 COCO trainval test-dev 50.4 - 8 link
DSSD513 COCO trainval test-dev 53.3 - 6 link
FPN FRCN COCO trainval test-dev 59.1 - 6 link
Retinanet-50-500 COCO trainval test-dev 50.9 - 14 link
Retinanet-101-500 COCO trainval test-dev 53.1 - 11 link
Retinanet-101-800 COCO trainval test-dev 57.5 - 5 link
YOLOv3-320 COCO trainval test-dev 51.5 38.97 Bn 45 cfg weights
YOLOv3-416 COCO trainval test-dev 55.3 65.86 Bn 35 cfg weights
YOLOv3-608 COCO trainval test-dev 57.9 140.69 Bn 20 cfg weights
YOLOv3-tiny COCO trainval test-dev 33.1 5.56 Bn 220 cfg weights
YOLOv3-spp COCO trainval test-dev 60.6 141.45 Bn 20 cfg weights

How detection works:

https://youtu.be/F1cYlHfw2To

More information about training, testing, and configuring the YOLO model: https://github.com/AlexeyAB/darknet

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