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README.md

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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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