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.
- OpenCV. To install OpenCV for Visual Studio 2019 use this guide https://www.opencv-srf.com/2017/11/install-opencv-with-visual-studio.html
Aimbot uses WinAPI functions to move the mouse cursor to the center of the area detected by the neural network each frame.
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.
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 |
More information about training, testing, and configuring the YOLO model: https://github.com/AlexeyAB/darknet