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from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\cs2-yolo12-weapon-detection.pt')
# Run inference on 'image.png' with arguments
model.predict(
'image.png',
save=True,
device=0
)
YOLOv12m summary (fused): 169 layers, 20,119,561 parameters, 0 gradients, 67.2 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:01<00:00, 2.61it/s]
all 87 158 0.963 1 0.993 0.871
AK47 11 14 0.979 1 0.995 0.853
M4A1-S 8 12 0.987 1 0.995 0.849
M4A1 9 13 0.978 1 0.995 0.844
GALIL 8 11 0.974 1 0.995 0.859
FAMAS 7 7 0.963 1 0.995 0.926
TEC-9 4 4 0.985 1 0.995 0.917
FIVE-SEVEN 5 5 0.96 1 0.995 0.903
GLOCK-18 4 4 0.932 1 0.995 0.905
USP-S 10 10 0.966 1 0.995 0.9
EAGLE 4 4 0.933 1 0.995 0.847
BERETTAS 4 4 0.929 1 0.995 0.948
MAC10 6 6 0.953 1 0.995 0.891
MP5 7 11 0.978 1 0.995 0.852
MP9 7 11 0.978 1 0.995 0.888
P90 9 13 0.978 1 0.995 0.842
P250 6 6 0.977 1 0.995 0.861
SSG08 8 11 0.9 1 0.95 0.785
AWP 8 12 0.99 1 0.995 0.805
Speed: 0.2ms preprocess, 9.3ms inference, 0.0ms loss, 1.2ms postprocess per image