The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 12, 2025
Filed:
Sep. 16, 2020
Beijing Baidu Netcom Science and Technology Co., Ltd., Beijing, CN;
Tuobang Wu, Beijing, CN;
En Shi, Beijing, CN;
Yongkang Xie, Beijing, CN;
Xiaoyu Chen, Beijing, CN;
Lianghuo Zhang, Beijing, CN;
Jie Liu, Beijing, CN;
Binbin Xu, Beijing, CN;
BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD., Beijing, CN;
Abstract
The present disclosure discloses a method and an apparatus for adapting a deep learning model, an electronic device and a medium, which relates to technology fields of artificial intelligence, deep learning, and cloud computing. The specific implementation plan is: obtaining model information of an original deep learning model and hardware information of a target hardware to be adapted; querying a conversion path table according to the model information and the hardware information to obtain a matched target conversion path; and converting, according to the target conversion path, the original deep learning model to an intermediate deep learning model in the conversion path, and converting the intermediate deep learning model to the target deep learning model. Therefore, the deep learning model conversion is performed based on the model conversion path determined by the model information of the original deep learning model and the hardware information of the target hardware, which realizes converting any type of original deep learning model into the target deep learning model adapted to any target hardware, and solves the problem that the deep learning model is difficult to be applied to different hardware terminals.