| Notebook | GitHub | Colab |
|---|---|---|
| BTech | btech |  |
| MVTec | mvtec | |
| Folder | folder | |
| Tiling | tiling | |
| Tabular | tabular | |
The notebooks in this section demonstrate the mechanics of anomalib data modules, with a specific focus on benchmarks such as MVTec AD, BTech, and custom datasets via the Folder module. Anomalib data modules are structured as follows: Each data collection implements the Torch Dataset and the PyTorch Lightning DataModule objects.
The Torch Dataset inherits torch.utils.data.Dataset and implement the __len__ and __getitem__ methods. This implementation might therefore be utilized not just for anomalib, but also for other implementations.
The DataModule implementation inherits the PyTorch Lightning DataModule object. The advantage of this class is that it organizes each step of data from download to creating the Torch dataloader.
Overall, a data implementation has the following structure:
anomalib
├── __init__.py
├── data
│ ├── __init__.py
│ ├── btech.py
│ │ ├── BTechDataset
│ │ └── BTech
│ ├── folder.py
│ │ ├── FolderDataset
│ │ └── Folder
│ ├── inference.py
│ │ ├── InferenceDataset
│ │ mvtec.py
│ │ ├── MVTecADDataset
└── └── └── MVTecLet's deep dive into each dataset supported in anomalib and check their functionality.
If you have not installed all required dependencies, follow the Installation Guide.