# Data Classes

Anomalib's dataclasses provide type-safe data containers with automatic validation. They support both PyTorch and NumPy backends for flexible data handling.

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:::{grid-item-card} {octicon}`package` Generic Classes
:link: generic
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Base dataclasses that define common data structures and validation logic:

- Generic Item/Batch
- Input/Output Fields
- Validation Mixins

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[Learn More »](generic)
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:::{grid-item-card} {octicon}`cpu` PyTorch Classes
:link: torch
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PyTorch tensor-based implementations:

- Image, Video, Depth Items
- Batch Processing Support
- Type-safe Validation

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[Learn More »](torch)
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:::{grid-item-card} {octicon}`database` NumPy Classes
:link: numpy
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NumPy array-based implementations:

- Efficient Data Processing
- Array-based Containers
- Conversion Utilities

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[Learn More »](numpy)
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## Documentation

For detailed documentation and examples, see:

- {doc}`Generic Base Classes <generic>`
- {doc}`PyTorch Classes <torch>`
- {doc}`NumPy Classes <numpy>`

```{toctree}
:hidden:

generic
torch
numpy
```