Training method and device of classification model, mobile terminal, and readable storage medium

Abstract

The application relates to a training method and device of a classification model, a mobile terminal, and a computer readable storage medium. The method comprises the following steps: training the classification model based on a preset data set until the precision of the classification model meets the standard value, wherein the data in the preset data set carries annotation information; identifying each data in the preset data set based on the trained classification model so as to acquire class information of each data; when the class information of the data and the annotation information areinconsistent, cleaning the data so as to acquire the cleaned target data set; re-training the classification model based on the cleaned target data set, thereby guaranteeing the quality of each datain the target data set based on a semi-automatic cleaning way. The data quality can be guaranteed without performing multi-stage artificial auditing mechanism, the manpower cost is greatly saved, thedata cleaning efficiency is improved, and the precision and the performance of the classification model can be improved by training the classification model based on the target data set.

Description

technical field [0001] The present application relates to the field of computer applications, in particular to a classification model training method and device, a mobile terminal, and a computer-readable storage medium. Background technique [0002] The field of artificial intelligence (AI) is developing rapidly, especially with the wide application of deep learning technology, which has made breakthrough progress in the fields of object detection and recognition. Generally, artificial intelligence AI algorithms are mainly based on supervised learning deep learning technology, and training data is the driving force of artificial intelligence models. [0003] The current training data acquisition methods mainly include open source datasets, web crawling, and offline collection. However, in order to obtain a large amount of data related to learning tasks, it is generally necessary to manually filter and classify open source datasets and web crawled data. In the manual scree...

AI Technical Summary

Problems solved by technology

In the manual screening stage, due to the large number of manpower involved and the uneven screening and classification standards, a large number of classification errors are often caused

In order to reduce the classification error, the classification error is generally corrected through a multi-level manual review mechanism to ensure data quality, but this method will consume a lot of manpower and time, and the efficiency of data cleaning is low

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