---
license: mit
tags:
- anomaly-detection
- efficientad
- mvtec-ad
- cable
---

# EfficientAD - Cable

EfficientAD model for detecting bent wires, cable swaps, and cut insulation in cables

## Model Details

- **Architecture**: EfficientAD (Teacher-Student-Autoencoder)
- **Model Size**: Medium (512-dimensional features)
- **Dataset**: MVTec AD - Cable
- **AU-ROC**: 94.2%
- **Training**: Custom training on Apple Silicon (MPS)

## Files

- `teacher.pth`: Pre-trained teacher network (31MB)
- `student.pth`: Trained student network (44MB)
- `autoencoder.pth`: Trained autoencoder (4.2MB)

## Usage

```python
import torch

# Load models
teacher = torch.load('teacher.pth')
student = torch.load('student.pth')
autoencoder = torch.load('autoencoder.pth')
```

## Citation

```bibtex
@article{efficientad2023,
  title={EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies},
  author={Batzner, Kilian and Heckler, Lars and König, Rebecca},
  journal={arXiv preprint arXiv:2303.14535},
  year={2023}
}
```

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