公开(公告)号：US20190311494A1

公开(公告)日：2019-10-10

申请号：US15946580

申请日：2018-04-05

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ganesan RAMALINGAM ,  Ramachandran RAMJEE ,  Romil BHARDWAJ ,  Gopi Krishna TUMMALA

IPC: G06T7/80 ,  G06T7/73 ,  G06K9/00 ,  G06K9/62

Abstract: This document relates to camera calibration. One example uses real-world distances and image coordinates of object features in images to determine multiple candidate camera calibrations for a camera. This example filters out at least some of the multiple candidate camera calibrations to obtain remaining calibrations, and obtains a final calibration for the camera from the remaining calibrations

公开(公告)号：US20220188569A1

公开(公告)日：2022-06-16

申请号：US17124172

申请日：2020-12-16

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ganesh ANANTHANARAYANAN ,  Yuanchao SHU ,  Tsu-wang HSIEH ,  Nikolaos KARIANAKIS ,  Paramvir BAHL ,  Romil BHARDWAJ

IPC: G06K9/62 ,  G06F9/50 ,  G06N20/00 ,  G06K9/00

Abstract: Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.

公开(公告)号：US20230030499A1

公开(公告)日：2023-02-02

申请号：US17948736

申请日：2022-09-20

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ganesh ANANTHANARAYANAN ,  Yuanchao SHU ,  Tsu-wang HSIEH ,  Nikolaos KARIANAKIS ,  Paramvir BAHL ,  Romil BHARDWAJ

IPC: G06K9/62 ,  G06N20/00 ,  G06V20/58 ,  G06F9/50

Abstract: Examples are disclosed that relate to methods and computing devices for allocating computing resources and selecting hyperparameter configurations during continuous retraining and operation of a machine learning model. In one example, a computing device configured to be located at a network edge between a local network and a cloud service comprises a processor and a memory storing instructions executable by the processor to operate a machine learning model. During a retraining window, a selected portion of a video stream is selected for labeling. At least a portion of a labeled retraining data set is selected for profiling a superset of hyperparameter configurations. For each configuration of the superset of hyperparameter configurations, a profiling test is performed. The profiling test is terminated, and a change in inference accuracy that resulted from the profiling test is extrapolated. Based upon the extrapolated inference accuracies, a set of selected hyperparameter configurations is output.