CS2 YOLO - Object Detection
					Collection
				
				6 items
				β’ 
				Updated
					
				β’
					
					4
['CT_Body', 'CT_Head', 'T_Body', 'T_Head']
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\cs2_yolo12s.pt')
# Run inference on 'image.png' with arguments
model.predict(
    'image.png',
    save=True,
    device=0
    )
YOLOv12s summary (fused): 159 layers, 9,232,428 parameters, 0 gradients, 21.2 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|ββββββββββ| 3/3 [00:00<00:00,  3.64it/s]
                   all         90        255      0.873      0.556      0.717      0.453
               CT_Body         63         74      0.915      0.595      0.786      0.574
               CT_Head         59         68      0.848      0.485      0.685      0.361
                T_Body         49         58      0.822      0.655      0.747      0.547
                T_Head         45         55      0.905      0.491      0.648      0.329