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

A training method and technology of a training system, which are applied to the training method/system of an intelligent model, computer-readable storage media and terminal fields, can solve the problems of poor model adaptability, high cost of data collection and labeling, etc., so as to improve training efficiency and save The effect of labeling costs

Inactive Publication Date: 2017-11-24
SHANGHAI ADVANCED RES INST CHINESE ACADEMY OF SCI +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 t

Method used

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

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] This embodiment provides a method for training an intelligent model, including:

[0040] Perform initial model training on the input first data set and annotation information related to the training task to obtain a benchmark model;

[0041] Adding new data with the same attributes as the data in the first data set, and merge them in the first data set to form a second data set;

[0042] Test the data in the second data set, evaluate the value of the data in the second data set, to select data whose label value is greater than the preset label value, and form the selected data into the third data set;

[0043] Label the data in the third data set that is not labeled with labeled information, and merge the data with labeled information in the third data set;

[0044] Based on the merged third data set, retrain the benchmark model to obtain an updated benchmark model;

[0045] The third data set is defined as a new first data set, new data is added, and the above steps are repeated ...

Embodiment 2

[0071] This embodiment provides an intelligent model training system, including:

[0072] The initial training module is used to perform initial model training on the input first data set and label information related to the training task to obtain a reference model;

[0073] The merging module is used to add new data with the same attributes as the data in the first data set, and merge them in the first data set to form a second data set;

[0074] The processing module is used to test the data in the second data set, evaluate the value of the data in the second data set, to select the data whose label value is greater than the preset label value, and form the selected data into the third data set; Annotate data in the data set that is not marked with annotated information, and merge the data with annotated information into the third data set;

[0075] The retraining module is used to retrain the benchmark model based on the merged third data set to obtain the updated benchmark model;...

Embodiment 3

[0105] This embodiment provides a terminal, including: a processor, a memory, a transceiver, a communication interface, and a system bus; the memory and the communication interface are connected to the processor and the transceiver through the system bus to complete mutual communication, and the memory is used to store the computer The program and the communication interface are used to communicate with other devices. The processor and the transceiver are used to run a computer program to make the terminal execute steps S11 to S18 of the intelligent model training system as described in the first embodiment.

[0106] The aforementioned system bus may be a Peripheral Pomponent Interconnect (PCI) bus or an Extended Industry Standard Architecture (Extended Industry Standard Architecture, EISA) bus. The system bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used in the figure, but it does not mean that there is ...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N99/00
CPCG06N20/00G06V40/172G06V20/584G06V2201/08
Inventor 汪宏邵蔚元郑莹斌叶浩
Owner SHANGHAI ADVANCED RES INST CHINESE ACADEMY OF SCI
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