Data Classes#
Anomalib’s dataclasses provide type-safe data containers with automatic validation. They support both PyTorch and NumPy backends for flexible data handling.
Generic Classes
Base dataclasses that define common data structures and validation logic:
Generic Item/Batch
Input/Output Fields
Validation Mixins
PyTorch Classes
PyTorch tensor-based implementations:
Image, Video, Depth Items
Batch Processing Support
Type-safe Validation
NumPy Classes
NumPy array-based implementations:
Efficient Data Processing
Array-based Containers
Conversion Utilities
Documentation#
For detailed documentation and examples, see: