公开(公告)号：US20190213443A1

公开(公告)日：2019-07-11

申请号：US16238462

申请日：2019-01-02

Applicant: Whirlpool Corporation

Inventor： Kane Cunningham ,  Gregory Allen Druck, JR. ,  Brian Witlin ,  Yuri Yuryev ,  Vadim Geshel

IPC: G06K9/62 ,  G06N3/08 ,  G06K9/32 ,  G06K9/78

CPC classification number: G06K9/627 ,  G06K2209/17

Abstract: A system trains a computer model to classify images and to draw bounding boxes around classified objects in the images. The system uses a combination of partially labeled training images and fully labeled training images to train a model, such as a neural network model. The fully labeled training images include a classification label indicating a class of object depicted in the image, and bounding box or coordinate labels indicating a number of objects of the class in the image as well as the location of the objects of the class in the image. The partially labeled training images include a classification label but no indication of where in the image any objects of the class are located. Training the model using both types of training data makes it possible for the model to recognize and locate objects of classes that lack available fully labeled training data.

公开(公告)号：US10860888B2

公开(公告)日：2020-12-08

申请号：US16238462

申请日：2019-01-02

Applicant: Whirlpool Corporation

Inventor： Kane Cunningham ,  Gregory Allen Druck, Jr. ,  Brian Witlin ,  Yuri Yuryev ,  Vadim Geshel

IPC: G06K9/62 ,  G06K9/32 ,  G06K9/78 ,  G06N3/08

Abstract: A system trains a computer model to classify images and to draw bounding boxes around classified objects in the images. The system uses a combination of partially labeled training images and fully labeled training images to train a model, such as a neural network model. The fully labeled training images include a classification label indicating a class of object depicted in the image, and bounding box or coordinate labels indicating a number of objects of the class in the image as well as the location of the objects of the class in the image. The partially labeled training images include a classification label but no indication of where in the image any objects of the class are located. Training the model using both types of training data makes it possible for the model to recognize and locate objects of classes that lack available fully labeled training data.