Training method/system of intelligent model, computer readable storage medium and terminal

Abstract

The present invention provides a training method / system of an intelligent model, a computer readable storage medium and a terminal. The training method includes the following steps that: initial model training is performed on an inputted first data set and annotation information related to a training task, so that a reference model can be obtained; new data are added and are merged in the first data set, so that a second data set can be formed; data testing and value assessment are performed on the second data set, so that data of which the annotation values are larger than a preset annotation value are selected to form a third data set; unannotated information in the third data set is annotated, and the annotated information is merged into the third data set; the reference model is ret-rained, so that an updated reference model is obtained; and the third data set is defined as a new first data set, and new data are added into the new first data set, and the above steps are executed circularly until the precision of the iteratively-trained model is greater than preset accuracy. With the training method / system of the intelligent model, the computer readable storage medium and the terminal of the method adopted, the number of manual annotations can be deceased; it does not need to annotate all the data, and therefore, annotation costs can be saved, and the training efficiency of the model can be improved.

Description

Technical field [0001] The present invention belongs to the field of artificial intelligence technology, and relates to a training method and system, in particular to a training method / system of an intelligent model, a computer-readable storage medium and a terminal. Background technique [0002] The development of the field of artificial intelligence is changing with each passing day, especially with the widespread application of deep learning technology, it has made breakthrough progress in the fields of object detection and recognition. Different from traditional artificially designed features, deep learning technology allows the model to learn the feature representation of objects by inputting a large amount of data, which can often match or even exceed the recognition accuracy of humans. [0003] Generally, the learning process of a complete artificial intelligence model includes two steps: data set preparation and model training. The prepared data set includes the data and t...

AI Technical Summary

Problems solved by technology

[0007] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a training method / system, computer-readable storage medium and terminal for an intelligent model, which are used to solve the problems of high data collection and labeling costs and model failures in the prior art. The problem of poor adaptability

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