ONNX models created outside Breeze can be used to make classification and quantification predictions during runtime. To import an existing model using the Import ONNX model in Model view in Breeze.

The model must follow the standard format specified below for both name and datatype. Inputs and outputs are bound by name so the names must match exactly according to the specification below.

Classification model

The model INPUTS and OUTPUTS of the model should be as below, the names and data types must match. The dimensions for the INPUTS.Features must be the same as the number of wavelengths in the Breeze model. In the example below 29 wavelengths are included in the Breeze model and also then in the ONNX model.

The INPUTS must have the name Features.

For the classification model, the above OUTPUTS should in the listed format. The dimension for the Score.output variable must be the same as the number of classes available in Breeze. PredictedLabel.output is the prediction result in a single dimension variable, the shape of this node is not required to be known - it is assumed to be [-1,1].

If the PredictedLabel.output is missing the argument of the maxima index + 1 will be used as the class assigned.

Quantification model

The INPUTS are the same as for Classification (wavelengths in model).

For OUTPUTS only Score.output is applicable for quantification. The dimensions are the number of variables used in the Breeze model.