Sample labeling method and device, server and machine readable storage medium

Abstract

The embodiment of the invention provides a sample labeling method and device, a server and a machine readable storage medium. The method comprises the steps of obtaining a sample set, taking a plurality of labeled samples in the sample set as the training samples, and respectively training a plurality of preset network models of different network structures to obtain the plurality of trained network models; aiming at any one unlabeled sample in the sample set, respectively inputting the unlabeled sample into the plurality of trained network models to obtain an output result of each network model, fusing the output results to obtain the labeling information of the unlabeled sample, and labeling the unlabeled sample by utilizing the labeling information. The sample set comprises the plurality of labeled samples and the plurality of unlabeled samples, the ratio of the number of the labeled samples to the total number of the samples in the sample set is smaller than a preset ratio, and thelabeling personnel only need to label a small number of samples, so that the workload of manual labeling is reduced, the risks of wrong labeling and missing labeling of manual labeling are reduced, and the sample labeling efficiency is improved.

Description

technical field [0001] The present invention relates to the technical field of intelligent analysis, in particular to a sample labeling method, device, server and machine-readable storage medium. Background technique [0002] As an emerging field in machine learning research, deep learning technology parses data by imitating the mechanism of the human brain. It is a technology for analyzing and learning by establishing and simulating the human brain. Deep learning technology provides an end-to-end network model, such as convolutional neural network model, recurrent neural network model, long-short-term memory network model, etc., input the data to be analyzed into the network model, without caring about the internal structure of the network model, you can The data analysis results can be quickly obtained, and the data analysis efficiency is very high. Therefore, it has been widely used in the fields of artificial intelligence such as image classification, target detection, t...

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Problems solved by technology

[0004] The above-mentioned nominal information usually refers to the annotation information of the training samples. In the traditional method, the annotation information of the samples is often manually annotated. However, as the complexity of the network structure becomes higher and higher, the number of samples required is also increasing. The more and more samples are labeled, the labelers are prone to fatigue, and it is very easy to have wrong labels and missing labels, resulting in low efficiency of sample labeling

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