--- pipeline_tag: object-detection tags: - welding - defects - detection --- # 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 ![confusion_matrix_normalized.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/gflYDiDQL4P8ZUiij7Fnp.png) #### Labels ![labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/0ZIgleo87QYeOBP4swPJj.jpeg) ![labels_correlogram.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/bHGE-LOjH4jgmbBShGZvC.jpeg) #### Results ![results.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/83-tr_zbyudVR3EnRW6VU.png) #### Predict ![val_batch0_labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/13x44dJn8_6rSgm7eEXRz.jpeg) ![val_batch0_pred.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/8luLKVm-7OSvwfkDXV27x.jpeg) ``` 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 ``` #### Others models... https://huggingface.co/jparedesDS/