Search result: GB/T 42755-2023
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Artificial intelligence - Code of practice for data labeling of machine learning
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GB/T 42755-2023
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| Standard ID | GB/T 42755-2023 (GB/T42755-2023) | | Description (Translated English) | Artificial intelligence -- Code of practice for data labeling of machine learning | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | L60 | | Classification of International Standard | 35.240 | | Word Count Estimation | 12,183 | | Date of Issue | 2023-05-23 | | Date of Implementation | 2023-12-01 | | Issuing agency(ies) | State Administration for Market Regulation, National Standardization Management Committee |
GB/T 42755-2023: Artificial intelligence data labeling procedures for machine learning
ICS 35:240
CCSL60
National Standards of People's Republic of China
Artificial Intelligence-Oriented Data Labeling Procedure for Machine Learning
Released on 2023-05-23
2023-12-01 implementation
State Administration for Market Regulation
Released by the National Standardization Management Committee
table of contents
Preface III
1 Range 1
2 Normative references 1
3 Terms and Definitions 1
4 Data labeling process 2
5 Preliminary preparation for labeling tasks 3
5:1 Labeling task 3
5:2 Annotators 4
5:3 Annotation environment 4
6 Labeling task execution 4
6:1 Process control 4
6:2 Quality Assurance 5
6:3 Management Mechanism 6
7 Annotation result output 7
7:1 Internal Quality Inspection 7
7:2 Data Delivery 8
7:3 Post-maintenance 8
Figure 1 Data labeling process framework 2
foreword
This document is in accordance with the provisions of GB/T 1:1-2020 "Guidelines for Standardization Work Part 1: Structure and Drafting Rules for Standardization Documents"
drafting:
Please note that some contents of this document may refer to patents: The issuing agency of this document assumes no responsibility for identifying patents:
This document is proposed and managed by the National Information Technology Standardization Technical Committee (SAC/TC28):
This document is drafted by: Beijing University of Aeronautics and Astronautics, China Institute of Electronic Technology Standardization, Beijing Baidu Netcom Technology Co:, Ltd:, Lang
Chaosoft Technology Co:, Ltd:, Shandong Provincial Institute of Artificial Intelligence, Midea Group (Shanghai) Co:, Ltd:, Beijing Zhipu Huazhang Technology Co:, Ltd:,
Beijing Aishu Smart Technology Co:, Ltd:, Tencent Cloud Computing (Beijing) Co:, Ltd:, Beijing Aerospace Automatic Control Research Institute, Zhengzhou Zhongye Branch
Technology Co:, Ltd:, Neusoft Group Co:, Ltd:, Beijing Haitian AAC Technology Co:, Ltd:, Yuncong Technology Group Co:, Ltd:
Division, Shenzhen Yuntian Lifei Technology Co:, Ltd:, Institute of Software, Chinese Academy of Sciences, Shanghai Yitu Network Technology Co:, Ltd:, Chinese Academy of Medicine
Institute of Biomedical Engineering, Ping An Technology (Shenzhen) Co:, Ltd:, Shanghai SenseTime Intelligent Technology Co:, Ltd:, Shanghai Artificial Intelligence Experiment
Laboratory, Shanghai Computer Software Technology Development Center, China Aviation Technology Research Institute, Xinjiang Institute of Physics and Chemistry, Chinese Academy of Sciences, China Quality
Quantity Certification Center, China Automotive Data (Tianjin) Co:, Ltd:, Beijing Eye Technology Co:, Ltd:, Shanghai Artificial Intelligence Research Institute Co:, Ltd:, Zhejiang University
China Technology Co:, Ltd:, Hangzhou Qulian Technology Co:, Ltd:, Changzhou Weiyi Intelligent Manufacturing Technology Co:, Ltd:, Changchun Boli Electronic Technology Co:, Ltd:
Company, Luokejiahua Technology Group Co:, Ltd:, Shanghai Jiaotong University, Shanghai Computer Software Technology Development Center:
The main drafters of this document: Wu Wenjun, Dong Jian, Ma Shanshan, Liu Xianglong, Xu Yang, Jia Yijun, Meng Lingzhong, Ren Jian, Chen Bin, Zhao Haojie, Liu Haitao,
Chen Shangyi, Tuo Liheng, Zuo Jiaping, Wang Lina, Xu Song, Wang Jianzong, Zhang Nan, Cai Yasen, Wang Gongming, Chen Mingang, Zhao He, Jin Zhu, Hao Yufeng, Liu Yonghui,
Li Wei, Zhao Chunhao, Huang Zhilong, Yang Chunlin, Wang Xiaoman, Shi Jialiang, Shu Minglei, Wang Yinglong, Kuang Lizhong, Chen Xiaofeng, Wu Geng, Jiang Hui, Pu Jiangbo,
Ma Yuanwei, Xing Jing, Qiao Yu, He Conghui, Yang Yating, Ma Bo, Tao Jian, Hu Jinwei, Chu Sisi, Li Jun, Song Haitao, Shen Hao, Cheng Miao, Zheng Zhongbin,
Li Shuang:
Artificial Intelligence-Oriented Data Labeling Procedure for Machine Learning
1 Scope
This document specifies the data labeling framework process for machine learning in the field of artificial intelligence:
This document is applicable to guide machine learning-oriented data annotation in the field of artificial intelligence and related research, development and application:
2 Normative references
The contents of the following documents constitute the essential provisions of this document through normative references in the text: Among them, dated references
For documents, only the version corresponding to the date is applicable to this document; for undated reference documents, the latest version (including all amendments) is applicable to
this document:
GB/T 35274-2017 Information Security Technology Big Data Service Security Capability Requirements
GB/T 37973-2019 Information Security Technology Big Data Security Management Guidelines
3 Terms and Definitions
The following terms and definitions apply to this document:
3:1
Data annotation datalabeling
The process of specifying target variables and assigning values to data samples:
3:2
Labeling task labelingtask
The activity of labeling data according to the data labeling instructions:
3:3
Data annotation party datalabeler
The person or institution undertaking the task of data labeling:
3:4
Data demand side datauser
The person or organization that proposed the data labeling requirement:
3:5
The person or organization that manages the evaluation, distribution, delivery, acceptance, and quality control of data labeling tasks:
3:6
Labeling tool labelingtool
The tools used by the data labeling party to perform data labeling, the tools used by the labeling manager to manage data labeling, and the data demander’s acceptance
All process-related tools such as the tools used for data labeling:
3:7
The written expression used by the data demander to clarify the labeling task to the labeling manager and the data labeling party:
Note: The labeling task description usually includes a description of the labeling task to be performed, labeling methods, positive and negative examples, acceptance methods, and acceptance indicators:
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