US 12,468,932 B2
Automated end-to-end machine learning model optimization
Chao Xue, Beijing (CN); Chang Xu, Beijing (CN); Yu Ling Zheng, Beijing (CN); and Leonid Karlinsky, Mazkeret Batya (IL)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Dec. 30, 2020, as Appl. No. 17/137,588.
Prior Publication US 2022/0207350 A1, Jun. 30, 2022
Int. Cl. G06N 3/08 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 20/00 (2019.01)
CPC G06N 3/08 (2013.01) [G06N 3/047 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
dividing a dataset into a training portion and a validation portion, wherein the dataset is an initial dataset, and wherein the validation portion of the dataset is further divided into a first portion, a second portion, and a third portion;
training, using the training portion of the dataset, a set of component parameters, the set of component parameters comprising parameters of a component of an object detection model;
training, using the trained set of component parameters and the first portion of the validation portion of the dataset, a set of backbone component weights, wherein each backbone component weight in the set of backbone component weights corresponds to a possible backbone component type in a backbone portion of the object detection model;
training, using the trained set of component parameters and the second portion of the validation portion of the dataset, a set of backbone link weights, wherein each backbone link weight within the set of backbone link weights corresponds to a possible link between two backbone components within the backbone portion of in the object detection model;
training, using the trained set of component parameters and the third portion of the validation portion of the dataset, a set of head component weights, a head component weight in the set of head component weights comprising a weight of a component type in a head portion of the object detection model;
configuring, using the trained set of component parameters, the trained set of backbone component weights, the trained set of backbone link weights, and the trained set of head component weights, a trained object detection model;
causing the trained object detection model to perform object detection; and
re-dividing the initial dataset into a second training portion and a second validation portion.