All Classes and Interfaces
Class
Description
Author or maintainer.
Utility methods for Axis objects.
Base axis class for 0.4 and 0.5 axes.
The type of axis.
Model or repository badge.
The basic tensor representation for inputs and outputs.
Citation entry.
Dataset spec.
A file description (link and hash).
A fixed axis size.
Axes that relate to indexes rather than space/time/channel/batch, for example lists of objects.
Model input, including shape, axes, datatype and preprocessing.
Resource based on the main model spec.
Model parent.
Model output, including shape, axes, halo, datatype and postprocessing.
Describes a range of valid tensor axis sizes as `size = min + n*step`.
Base class for pre- and post-processing operations.
A binarize operation.
A clipping operation.
Requested mode for pre- or post-processing.
A processing mode that operates optionally on different sets of data.
A linear scaling operation.
A scaling operation to the mean and variance of a reference tensor.
A scaling operation to a reference tensor.
A sigmoid transofmration.
A scaling operation to zero mean and unit variqnce.
A tensor axis size (extent in pixels/frames) defined in relation to a reference axis.
General resource, based upon the RDF.
Axes that have both size and scale.
Shape of input or output tensor.
A shape that is determined based on the shape of another tensor.
A shape that is determined based on a minimum and a step size.
A Shape that wraps tensor Size objects from the 0.5 spec.
An axis size.
Axes relating to physical space.
Possible SI and imperial units for distance in physical space.
Descriptions of tensor data types, primarily ratio types or nominal/ordinal types represented by int-like numeric values.
A description of the possible ratio data values in a tensor.
Data type of ratio data.
A description of the possible discrete data values in a tensor.
Data type of nominal or ordinal data.
Axes relating to physical time.
Possible units for time.
Model weights and model weights accessories.
Model weights information for a specific format.
Enum representing supported model weights.
A map of weights types (e.g., TensorFlow, ONNX, PyTorch) to model weights.
An axis with a halo.