A method for generating a training image set using automated semantic segmentation labelling, wherein the training image set is used to train a machine vision system to recognize a target object, wherein the method is implemented on a computing system comprising at least one processor in communication with at least one memory device, the method comprising using the at least one processor to: retrieve, from the at least one memory device, target object model data corresponding to a three-dimensional, 3-D, model of the target object, wherein the target object includes at least one element that is configurable, that is, at least one element for which geometry is variable according to a configuration of the at least one element, wherein the target object model data includes configuration data representative of each of a plurality of configurations the target object is presentable, wherein configuration data defines how geometry of each element that is configurable is modified for each configuration of said element; create a plurality of two-dimensional, 2-D, synthetic images from the target object model data, each of the 2-D synthetic images corresponding to one of a plurality of views of the 3-D model, wherein creating the plurality of 2-D synthetic images includes the steps of: selectively applying, to the 3-D model, the configuration data for each of the configurations; and creating the plurality of 2-D synthetic images at each of the plurality of views for each of the configurations; create a plurality of semantic segmentation images, including the steps of, for each 2-D synthetic image: identifying a plurality of boundary pixels that define a perimeter of the target object in the 2-D synthetic image, and automatically building, from the boundary pixels, a semantic segmentation image corresponding to the 2-D synthetic image, comprising assigning a semantic segmentation label to pixels enclosed by the boundary pixels; generate linking data associating the semantic segmentation image with the corresponding 2-D synthetic image; and store, in the at least one memory device, the training image set including the 2-D synthetic images, the semantic segmentation images, and the linking data. retrieve, from the at least one memory device, target object model data corresponding to a three-dimensional, 3-D, model of the target object, wherein the target object includes at least one element that is configurable, that is, at least one element for which geometry is variable according to a configuration of the at least one element, wherein the target object model data includes configuration data representative of each of a plurality of configurations the target object is presentable, wherein configuration data defines how geometry of each element that is configurable is modified for each configuration of said element; create a plurality of two-dimensional, 2-D, synthetic images from the target object model data, each of the 2-D synthetic images corresponding to one of a plurality of views of the 3-D model, wherein creating the plurality of 2-D synthetic images includes the steps of: selectively applying, to the 3-D model, the configuration data for each of the configurations; and creating the plurality of 2-D synthetic images at each of the plurality of views for each of the configurations; selectively applying, to the 3-D model, the configuration data for each of the configurations; and creating the plurality of 2-D synthetic images at each of the plurality of views for each of the configurations; create a plurality of semantic segmentation images, including the steps of, for each 2-D synthetic image: identifying a plurality of boundary pixels that define a perimeter of the target object in the 2-D synthetic image, and automatically building, from the boundary pixels, a semantic segmentation image corresponding to the 2-D synthetic image, comprising assigning a semantic segmentation label to pixels enclosed by the boundary pixels; identifying a plurality of boundary pixels that define a perimeter of the target object in the 2-D synthetic image, and automatically building, from the boundary pixels, a semantic segmentation image corresponding to the 2-D synthetic image, comprising assigning a semantic segmentation label to pixels enclosed by the boundary pixels; generate linking data associating the semantic segmentation image with the corresponding 2-D synthetic image; and store, in the at least one memory device, the training image set including the 2-D synthetic images, the semantic segmentation images, and the linking data.