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---
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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