US 12,423,617 B2
Scale selective machine learning system and method
Bradley Quinton, Vancouver (CA); Trent McClements, Burnaby (CA); Michael Scott Lee, North Vancouver (CA); and Scott Chin, Vancouver (CA)
Assigned to Singulos Research Inc., Burnaby (CA)
Filed by Singulos Research Inc., Burnaby (CA)
Filed on Feb. 14, 2022, as Appl. No. 17/671,159.
Claims priority of provisional application 63/176,762, filed on Apr. 19, 2021.
Prior Publication US 2022/0335334 A1, Oct. 20, 2022
Int. Cl. G06T 3/40 (2024.01); G06N 20/00 (2019.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01)
CPC G06N 20/00 (2019.01) [G06T 3/40 (2013.01); G06V 10/771 (2022.01); G06V 10/774 (2022.01)] 12 Claims
OG exemplary drawing
 
1. A method of generating scale selective training data for use in training a machine learning system to support scale selective image classification tasks, comprising:
obtaining a plurality of images comprising an object of interest at a plurality of image scales;
assigning a desired label to each of the plurality of images based on an image scale of the object of interest in the each image, wherein the desired label comprises an in-scope response when the image scale comprises an in-scope image scale, and
generating a set of training data for use in training the machine learning system to predict a scale of the object of interest, the training data comprising the plurality of images and corresponding desired labels.