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Picture classification model training method and system and computer equipment

A technology for image classification and model training, applied in the field of data processing, can solve the problems of low image recognition accuracy, slow image training and recognition speed, and few types of distinguishable images, so as to improve image training speed, improve image classification efficiency, The effect of image recognition accuracy improvement

Pending Publication Date: 2020-01-07
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In view of this, it is necessary to provide a picture classification model training method, system, computer equipment and computer-readable storage medium based on a small amount of data, to solve the current problems that require a large amount of data for model training, image training and recognition speed are slow, and the Resolving technical issues such as fewer types of images and low image recognition accuracy

Method used

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  • Picture classification model training method and system and computer equipment
  • Picture classification model training method and system and computer equipment
  • Picture classification model training method and system and computer equipment

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Embodiment 1

[0050] refer to figure 1 , shows a flow chart of the steps of the method for training a picture classification model based on a small amount of data according to an embodiment of the present invention. It can be understood that the flowchart in this method embodiment is not used to limit the sequence of execution steps. An exemplary description is given below taking the computer device 2 as the execution subject. details as follows.

[0051] Step S100, constructing a pre-training model according to the ImageNet model, and the ImageNet model is a network model trained according to preset pre-sample pictures.

[0052] Exemplarily, the pre-sample pictures are massive arbitrary pictures, and the arbitrary pictures can be obtained from an existing picture database or directly downloaded from the Internet.

[0053] Specifically, the step S100 may further include:

[0054] In step S100a, each pre-sample picture is input into the network model, and each pre-sample picture is associa...

Embodiment 2

[0097] Figure 4 It is a schematic diagram of program modules in Embodiment 2 of the image classification model training system based on a small amount of data in the present invention. The picture classification model training system 20 may include or be divided into one or more program modules, one or more program modules are stored in a storage medium, and executed by one or more processors to complete the present invention, and may Implement the above-mentioned image classification model training method based on a small amount of data. The program module referred to in the embodiment of the present invention refers to a series of computer program instruction segments capable of completing specific functions, which is more suitable than the program itself to describe the execution process of the image classification model training system 20 in the storage medium. The following description will specifically introduce the functions of each program module of the present embod...

Embodiment 3

[0104] refer to Figure 5 , is a schematic diagram of the hardware architecture of the computer device according to Embodiment 3 of the present invention. In this embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and / or information processing according to preset or stored instructions. The computer device 2 may be a rack server, a blade server, a tower server or a cabinet server (including an independent server, or a server cluster composed of multiple servers) and the like. As shown in the figure, the computer device 2 at least includes, but is not limited to, a memory 21 , a processor 22 , a network interface 23 , and a picture classification model training system 20 that can communicate with each other through a system bus.

[0105] In this embodiment, the memory 21 includes at least one type of computer-readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type...

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Abstract

The embodiment of the invention provides a picture classification model training method based on a small amount of data, and the method comprises the steps: building a pre-training model according toan ImageNet model, and enabling the ImageNet model to be a network model trained according to a preset pre-sample picture; obtaining a plurality of sample pictures, and obtaining a sample picture training set according to the plurality of sample pictures, the plurality of sample pictures including a plurality of target picture types of pictures; and training a pre-training model through the samplepicture training set to obtain a picture classification model. According to the embodiment of the invention, through carrying out model training on a small amount of data, the image training speed, the image recognition speed and the image recognition precision can be improved, distinguishable image types can be increased, and the image classification efficiency is greatly improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of data processing, and in particular to a small amount of data-based image classification model training method, system, computer equipment, and computer-readable storage medium. Background technique [0002] With the improvement of the level of science and technology, new image data information is being further understood by humans, resulting in the possibility of failure of traditional image data sets and the limited number of new image data sets. The existence of an efficient image classification system is of great significance. However, the current image classification software on the market has technical problems such as requiring a large amount of data for model training, slow image training and recognition speed, fewer types of distinguishable images, and low image recognition accuracy. Contents of the invention [0003] In view of this, it is necessary to provide a picture classif...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06F16/55G06F16/583
CPCG06N3/08G06F16/55G06F16/583G06N3/045G06F18/214G06F18/241
Inventor 沈吉祥
Owner PING AN TECH (SHENZHEN) CO LTD
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