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---
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language:
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- en
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- ru
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tags:
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- computer-vision
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- object-detection
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- yolov8
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- welding
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- ndt
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- defect-detection
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license: mit
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datasets:
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- synthetic-welding-defects
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metrics:
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- precision
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- recall
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- mAP
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---
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# XVL: X-Ray Vision Lab - Welding Defect Detector
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YOLOv8-based model for automated detection of welding defects in X-ray images.
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## Model Details
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- **Architecture**: YOLOv8n (custom)
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- **Input Size**: 512x512
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- **Classes**: 5 defect types
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- **Training Data**: Synthetic X-ray images (8000+ samples)
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- **Validation Data**: Real industrial X-ray scans (200+)
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## Performance
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| Metric | Value | Epoch |
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|------------|--------|-------|
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| Precision | 95.6% | 37 |
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| Recall | 88.9% | 39 |
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| mAP@50 | 93.3% | 39 |
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| mAP@50-95 | 78.8% | 37 |
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## Usage
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### With PyTorch
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```python
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import torch
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from models.yolo_custom import load_model
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model = load_model(
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weights="best.pt",
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config="config.yaml"
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)
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With XVL Project
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bash
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git clone https://github.com/your-username/XVL.git
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python scripts/download_weights.py
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python src/run.py
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Defect Classes
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Cracks
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Pore clusters
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Incomplete fusion
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Slag inclusions
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Absence of defects
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Training Configuration
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See config.yaml for full details.
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Citation
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If you use this model, please reference:
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@software{xvl2026,
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title={XVL: X-Ray Vision Lab},
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author={Alex Watchman},
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year={2026},
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url={https://github.com/Passenger1993/XVL}
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}
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## License
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MIT License
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Copyright (c) 2024 Your Name
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions: |
