MSherbinii/efficientad-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

import torch

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

Citation

@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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