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Method and device for determining image sample set for model training and electronic equipment

An image sample set and model training technology, which is applied in character and pattern recognition, payment systems, instruments, etc., can solve problems that affect training results, limited sample data, and low classification accuracy of classification model predictions, so as to ensure accuracy Effect

Pending Publication Date: 2020-07-28
HANGZHOU KUASHI TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the limited number of sample data in the current sample data set may affect the training results, resulting in low prediction classification accuracy of the trained classification model

Method used

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  • Method and device for determining image sample set for model training and electronic equipment
  • Method and device for determining image sample set for model training and electronic equipment
  • Method and device for determining image sample set for model training and electronic equipment

Examples

Experimental program
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Embodiment 1

[0061] In the case of large-scale model training, in view of the lack of data samples, a classification method (data+taxonomy=Dataonomy) is proposed to extract the internal correlation between large public data sets (such as ImageNet, MIT places) and limited task sample data, Thus creating a metadata set containing a large number of related data samples. The process mainly includes: using AHP (Analytic Hierarchy Process) to mine data similarity between data classes; using BIP (Binary Integer Program) to extract highly similar data classes from public data sets. This process involves a fully computational approach to quantify dataset relationships from which structure can be extracted. "Structure" refers to a set of relationships specifying which data set provides useful information to another data set, and how much information is provided.

[0062] refer to figure 1 As shown, it is a schematic diagram of steps of a method for determining an image sample set for model trainin...

Embodiment 2

[0175] refer to Figure 10 As shown, the device 1000 for determining an image sample set for model training provided by the embodiment of this specification may include:

[0176] Model selection module 1002, selects the pre-training model;

[0177] Matrix determination module 1004, determining the correlation matrix between the source data set and the target data set based on the pre-training model, wherein the number of images in the source data set is much larger than the number of images in the target data set, and the target The dataset contains image samples required for model training;

[0178] Normalization module 1006, using AHP to normalize the correlation matrix;

[0179] The sample amplification module 1008, according to the binary integer programming method, selects image samples satisfying the similarity conditions determined by the correlation matrix from the source data set as the image sample set based on the normalized correlation matrix.

[0180]Optionally...

Embodiment 3

[0191] Figure 11 It is a schematic structural diagram of an electronic device according to an embodiment of this specification. Please refer to Figure 10 , at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Wherein, the memory may include a memory, such as a high-speed random-access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. Of course, the electronic device may also include hardware required by other services.

[0192] The processor, the network interface and the memory can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture, industry standard architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnection standard) bus or an EISA (Extended Industry StandardArchitecture, extended industry standard arc...

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Abstract

One or more embodiments of the invention disclose a method and a device for determining an image sample set for model training, and electronic equipment. The method comprises the steps of selecting apre-training model; determining an incidence matrix between a source data set and a target data set based on the pre-training model, wherein the number of images in the source data set is far larger than the number of images in the target data set, and the target data set comprises image samples needed by model training; carrying out normalization processing on the incidence matrix by utilizing ananalytic hierarchy process; and according to a binary integer programming method, based on the incidence matrix after normalization processing, selecting an image sample meeting the similarity condition determined by the incidence matrix from the source data set as an image sample set. Therefore, the number of the image sample sets required by model training is increased, complete and comprehensive image samples are provided for subsequent model training, and the accuracy of the model obtained through training is ensured.

Description

technical field [0001] This document relates to the field of artificial intelligence technology, in particular to a method, device and electronic equipment for determining an image sample set for model training. Background technique [0002] Artificial Intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Among them, model training is an important operation of artificial intelligence technology. Specifically, the training model can be trained based on sample data to obtain the required classification model. [0003] However, the limited number of sample data in the current sample data set may affect the training results, resulting in low prediction classification accuracy of the trained classification model. Contents of the invention [0004] It is a purpose of this specification to provide one or more embodiments. [0005] In order to solv...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q20/38G06Q40/04
CPCG06Q20/3829G06Q20/389G06Q40/04G06F18/22G06F18/214
Inventor 顾红松
Owner HANGZHOU KUASHI TECH CO LTD
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