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--- |
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license: mit |
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tags: |
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- anomaly-detection |
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- efficientad |
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- mvtec-ad |
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- metal_nut |
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--- |
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# EfficientAD - Metal Nut |
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EfficientAD model for detecting scratches, bends, and flips in metal nuts |
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## Model Details |
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- **Architecture**: EfficientAD (Teacher-Student-Autoencoder) |
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- **Model Size**: Medium (512-dimensional features) |
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- **Dataset**: MVTec AD - Metal Nut |
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- **AU-ROC**: 99.7% |
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- **Training**: Custom training on Apple Silicon (MPS) |
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## Files |
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- `teacher.pth`: Pre-trained teacher network (31MB) |
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- `student.pth`: Trained student network (44MB) |
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- `autoencoder.pth`: Trained autoencoder (4.2MB) |
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## Usage |
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```python |
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import torch |
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# Load models |
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teacher = torch.load('teacher.pth') |
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student = torch.load('student.pth') |
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autoencoder = torch.load('autoencoder.pth') |
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``` |
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## Citation |
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```bibtex |
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@article{efficientad2023, |
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title={EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies}, |
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author={Batzner, Kilian and Heckler, Lars and König, Rebecca}, |
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journal={arXiv preprint arXiv:2303.14535}, |
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year={2023} |
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} |
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``` |
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Generated with Lumina Tech Platform |
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