公开(公告)号：US20230169351A1

公开(公告)日：2023-06-01

申请号：US18060705

申请日：2022-12-01

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Haifeng Wang ,  Zhihua Wu ,  Dianhai Yu ,  Yanjun Ma ,  Tian Wu

IPC: G06N3/098

CPC classification number: G06N3/098

Abstract: A distributed training method based on end-to-end adaption, a device and a storage medium. The method includes: obtaining slicing results by slicing a model to be trained; obtaining an attribute of computing resources allocated to the model for training by parsing the computing resources, in which the computing resources are determined based on a computing resource requirement of the model, computing resources occupied by another model being trained, and idle computing resources, and the attribute of the computing resources is configured to represent at least one of a topology relation and a task processing capability of the computing resources; determining a distribution strategy of each of the slicing results in the computing resources based on the attributes of the computing resources; and performing distributed training on the model using the computing resources based on the distribution strategy.

公开(公告)号：US20230115163A1

公开(公告)日：2023-04-13

申请号：US17989644

申请日：2022-11-17

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Haifeng Wang ,  Xiaoguang Hu ,  Dianhai Yu ,  Xiang Lan ,  Yanjun Ma

IPC: G06F11/34 ,  G06N3/063

Abstract: The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.

公开(公告)号：US11620815B2

公开(公告)日：2023-04-04

申请号：US17938457

申请日：2022-10-06

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Guanghua Yu ,  Qingqing Dang ,  Haoshuang Wang ,  Guanzhong Wang ,  Xiaoguang Hu ,  Dianhai Yu ,  Yanjun Ma ,  Qiwen Liu ,  Can Wen

IPC: G06V10/77 ,  G06V10/82 ,  G06V10/80 ,  G06V10/764

Abstract: A method for detecting an object in an image includes: obtaining an image to be detected; generating a plurality of feature maps based on the image to be detected by a plurality of feature extracting networks in a neural network model trained for object detection, in which the plurality of feature extracting networks are connected sequentially, and input data of a latter feature extracting network in the plurality of feature extracting networks is based on output data and input data of a previous feature extracting network; and generating an object detection result based on the plurality of feature maps by an object detecting network in the neural network model.

公开(公告)号：US20230096921A1

公开(公告)日：2023-03-30

申请号：US17657118

申请日：2022-03-29

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Shengyu Wei ,  Yuning Du ,  Xueying Lyu ,  Ying Zhou ,  Qiao Zhao ,  Qiwen Liu ,  Ran Bi ,  Xiaoguang Hu ,  Dianhai Yu ,  Yanjun Ma

IPC: G06V10/764 ,  G06V10/40 ,  G06V10/74

Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.