CS2 YOLO - Object Detection
Collection
5 items
β’
Updated
β’
1
['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