System and method for procedurally synthesizing datasets of objects of interest for training machine-learning models

A method of operating a machine, comprising:

• receiving data corresponding to a plurality of objects;
• and
• recognizing the plurality of objects using a machine-learning model that has been trained via a training dataset that has been procedurally synthesized by:
• generating training image definitions in accordance with variations in content of training images to be included in the training dataset;
• rendering the training images corresponding to the training image definitions;
• and
• generating, at least partially in parallel with the rendering, ground truth corresponding to the training images, the training images and the ground truth comprising the training dataset.

A machine, comprising:

• a machine-learning model;
• and
• a machine vision processor configured to identify objects employing the machine-learning model, wherein the machine-learning model has been trained via a training dataset that has been procedurally synthesized by:
• generating training image definitions in accordance with variations in content of training images to be included in the training dataset;
• rendering the training images corresponding to the training image definitions;
• and
• generating, at least partially in parallel with the rendering, ground truth corresponding to the training images, the training images and the ground truth comprising the training dataset.