Welding Defects Detector
Supported Labels
['Defect', 'Welding Line', 'Workpiece', 'porosity']
How to use
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\welding_defects_yolo11x.pt')
# Run inference on 'image.png' with arguments
model.predict(
'image.png',
save=True,
device=0
)
Confusion matrix normalized
Labels
Results
Predict
YOLO11x summary (fused): 464 layers, 56,831,644 parameters, 0 gradients, 194.4 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:06<00:00, 1.11it/s]
all 116 773 0.826 0.827 0.842 0.632
Defect 56 131 0.552 0.427 0.445 0.202
Welding Line 116 294 0.873 0.966 0.966 0.679
Workpiece 110 307 0.941 0.987 0.992 0.938
porosity 35 41 0.939 0.927 0.965 0.71
Speed: 0.5ms preprocess, 26.3ms inference, 0.0ms loss, 2.9ms postprocess per image