| | --- |
| | 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 |
| |
|
