| --- |
| 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} |
| } |
| ``` |
|
|
| Generated with Lumina Tech Platform |
|
|
