公开(公告)号：US20220004930A1

公开(公告)日：2022-01-06

申请号：US17480292

申请日：2021-09-21

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Qingqing DANG ,  Kaipeng DENG ,  Lielin JIANG ,  Sheng GUO ,  Xiaoguang HU ,  Chunyu ZHANG ,  Yanjun MA ,  Tian WU ,  Haifeng WANG

IPC: G06N20/00 ,  G06K9/62

Abstract: Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.

公开(公告)号：US20230031579A1

公开(公告)日：2023-02-02

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 ,  G06V10/764 ,  G06V10/80

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.