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

Commodity recognition model training method and device, electronic equipment and storage medium

A technology for identifying models and training methods, applied in the field of computer vision, can solve the problems affecting the normal operation of commodity retail, slow new product launch process, and lag in new commodity sales, so as to optimize commodity retail operations, shorten the training period, and reduce the number of products. Effect

Inactive Publication Date: 2020-02-07
COMMA SMART RETAIL CO LTD
View PDF0 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

If there are many new categories of products, the entire training cycle will be lengthened, which will further slow down the process of new products, leading to a lag in the sale of new products and affecting the normal operation of commodity retail.

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
  • Commodity recognition model training method and device, electronic equipment and storage medium
  • Commodity recognition model training method and device, electronic equipment and storage medium
  • Commodity recognition model training method and device, electronic equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] figure 1 A flow chart of a commodity recognition model training method provided in Embodiment 1 of the present application is given. The commodity recognition model training method provided in this embodiment can be executed by a commodity recognition model training device, and the commodity recognition model training device can use software and / or hardware, the commodity recognition model training device may be composed of two or more physical entities, or may be composed of one physical entity. Generally speaking, the product recognition model training device may be a product recognition device, a product self-service settlement device, and the like.

[0050] The following description will be made by taking the commodity recognition model training device as an example for executing the commodity recognition model training method. refer to figure 1 , the product recognition model training method specifically includes:

[0051] S110. Collect image samples of various ...

Embodiment 2

[0074] On the basis of the above examples, Figure 4 It is a schematic structural diagram of a product recognition model training device provided in Embodiment 2 of the present application. refer to Figure 4 , the product recognition model training device provided in this embodiment specifically includes: a collection module 21 , a training module 22 and a fine-tuning module 23 .

[0075] Wherein, the collection module 21 is used to collect various commodity image samples, and as a training data set, the commodity image samples are actual placement images of on-site commodities in stores;

[0076] The training module 22 is used to extract a set number of various commodity image samples from the training data set as a primary training set, and train the primary recognition model based on the Faster-RCNN network with the primary training set;

[0077] The fine-tuning module 23 is used to apply the primary recognition model to the training data set, and obtain some commodity i...

Embodiment 3

[0093] Embodiment 3 of the present application provides an electronic device, referring to Figure 5 , the electronic device includes: a processor 31 , a memory 32 , a communication module 33 , an input device 34 and an output device 35 . The number of processors in the electronic device may be one or more, and the number of memories in the electronic device may be one or more. The processor 31 , the memory 32 , the communication module 33 , the input device 34 and the output device 35 of the electronic device can be connected through a bus or in other ways.

[0094] Memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs and modules, such as program instructions / modules corresponding to the commodity recognition model training method described in any embodiment of the present application (for example, commodity recognition model acquisition module, training module and fine-tuning module in the training device). ...

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 discloses a commodity recognition model training method and device, electronic equipment and a storage medium. The method comprises the following steps of collecting various commodity image samples as a training data set; extracting a set number of various commodity image samples from the training data set as a primary training set; and training a primary recognition model based on a Faster-RCNN network, applying the primary recognition model to the training data set to obtain an effective sample, mixing the effective sample into the primary training set, and performing iterative fine tuning training to finally obtain a commodity recognition model. By adopting the technical means, the number of training samples can be reduced, the number of model training steps can be reduced, and the accuracy of the recognition model is ensured through error sample feedback training, so that the training period of the commodity recognition model is shortened, the new process on commodities is further accelerated, and commodity retail operation is optimized.

Description

technical field [0001] The embodiments of the present application relate to the technical field of computer vision, and in particular to a product recognition model training method, device, electronic equipment, and storage medium. Background technique [0002] At present, with the application and development of artificial intelligence technology in the retail industry, some new types of self-service settlement equipment have been born, one of which is a product recognition and settlement device based on computer vision, which uses target detection to identify what the user has in the process of purchasing goods. The purchased items, and then realize the automatic identification and settlement of the products purchased by the user, so as to simplify the product settlement process and optimize the user experience. [0003] However, when this kind of product identification and settlement equipment is used in operation, in order for the machine to acquire the ability to identif...

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): G06K9/62G06K9/00
CPCG06V10/94G06F18/214
Inventor 刘思伟宋志博郝叶林
Owner COMMA SMART RETAIL 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