| 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 | |