Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Training and testing method, device and equipment for quantized neural network model

A neural network model and quantitative technology, which is applied in the field of training and testing of quantitative neural network models, can solve the problems of deep neural networks, inability to realize real-time reasoning, and inability to deploy neural networks on mobile terminals, so as to improve computing speed and recognition. Accuracy and recognition accuracy, and the effect of reducing the amount of computation

Active Publication Date: 2020-06-26
BEIJING SENSETIME TECH DEV CO LTD
View PDF7 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as the prediction of the neural network model becomes more and more accurate, the neural network layer becomes deeper and deeper, and the complex neural network cannot be deployed on the mobile terminal, let alone real-time reasoning

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Training and testing method, device and equipment for quantized neural network model
  • Training and testing method, device and equipment for quantized neural network model
  • Training and testing method, device and equipment for quantized neural network model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

[0055] It should be noted that the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of this application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The embodiment of the invention provides a training and testing method, device and equipment for a quantitative neural network model. The training method comprises the following steps: acquiring training data, wherein the training data comprises an image sample and a labe, and the label comprises an identity label of the image sample; inputting the training data into a quantitative neural networkmodel for quantitative processing to obtain quantitative features of the image sample; and training the quantitative neural network model according to the quantitative features to obtain a trained quantitative neural network model. By implementing the embodiment of the invention, the trained quantitative neural network model adopts low-bit quantization processing, the operand is reduced, the operation rate is improved, the method can be more conveniently applied to terminal equipment, the quantitative neural network model is used for face recognition, the quantization error can be minimized, and the recognition precision of the quantitative neural network model is improved.

Description

technical field [0001] The present application relates to the field of image processing, in particular to a training and testing method, device and equipment for a quantized neural network model. Background technique [0002] Face recognition technology has a wide range of applications in practice. With the help of deep learning and convolutional neural network technology, the accuracy of face recognition has made great progress in recent years. However, as the predictions of the neural network model become more accurate and the layers of the neural network become deeper and deeper, complex neural networks cannot be deployed on mobile terminals, let alone real-time reasoning. [0003] Therefore, there is an urgent need for a training method for a quantized neural network model to solve the above technical problems. Contents of the invention [0004] The embodiment of the present application provides a training and testing method, device, equipment and storage medium of a ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06K9/00
CPCG06N3/08G06V40/172
Inventor 吴玉东吴一超梁鼎于志鹏吕元昊
Owner BEIJING SENSETIME TECH DEV CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com