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---
language:
- en
- ru
tags:
- computer-vision
- object-detection
- yolov8
- welding
- ndt
- defect-detection
license: mit
datasets:
- synthetic-welding-defects
metrics:
- precision
- recall
- mAP
---
# 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
```python
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: |