XVL: X-Ray Vision Lab - Welding Defect Detector

YOLOv8-based model for automated detection of welding defects in X-ray images.

Model Details

Architecture: YOLOv8n (custom)
Input Size: 512x512
Classes: 5 defect types
Training Data: Synthetic X-ray images (8000+ samples)
Validation Data: Real industrial X-ray scans (200+)

Performance

Metric	Value	Epoch
Precision	95.6%	37
Recall	88.9%	39
mAP@50	93.3%	39
mAP@50-95	78.8%	37

Usage

With PyTorch

import torch
from models.yolo_custom import load_model

model = load_model(
    weights="best.pt",
    config="config.yaml"
)
With XVL Project
bash
git clone https://github.com/your-username/XVL.git
python scripts/download_weights.py
python src/run.py
Defect Classes
Cracks

Pore clusters

Incomplete fusion

Slag inclusions

Absence of defects

Training Configuration
See config.yaml for full details.

Citation
If you use this model, please reference:

@software{xvl2026,
  title={XVL: X-Ray Vision Lab},
  author={Alex Watchman},
  year={2026},
  url={https://github.com/Passenger1993/XVL}
}

## License
MIT License

Copyright (c) 2024 Your Name

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

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