Home Cart Quotation Policy About-Us
www.ChineseStandard.net
SEARCH

GB/T 42755-2023 English PDF

Q: How long will the true-PDF of English version of GB/T 42755-2023 be delivered?

Upon your order, we will start to translate GB/T 42755-2023_English as soon as possible, and keep you informed of the progress. The lead time is typically 1 ~ 3 working days. The lengthier the document the longer the lead time.

Q: Can I share the purchased PDF of GB/T 42755-2023_English with my colleagues?

Yes. The purchased PDF of GB/T 42755-2023_English will be deemed to be sold to your employer/organization who actually pays for it, including your colleagues and your employer's intranet.

Q: Does the price include tax/VAT?

Yes. Our tax invoice, downloaded/delivered in 9 seconds, includes all tax/VAT and complies with 100+ countries' tax regulations (tax exempted in 100+ countries). Avoidance of Double Taxation Agreements (DTAs) between Singapore and 100+ Countries: https://www.iras.gov.sg/taxes/international-tax

Q: Do you accept my currency other than USD?

Yes. If you need your currency to be printed on the invoice, please write an email to Sales@ChineseStandard.net. In 2 working-hours, we will create a special link for you to pay in any currencies. Otherwise, follow the normal steps: Add to Cart -- Checkout -- Select your currency to pay.

US$259.00 · In stock
Delivery: <= 3 days. True-PDF full-copy in English will be manually translated and delivered via email.
GB/T 42755-2023: Artificial intelligence - Code of practice for data labeling of machine learning
Status: Valid

Standard ID	USD	BUY PDF	Lead-Days	Standard Title (Description)	Status
GB/T 42755-2023	259	Add to Cart	3 days	Artificial intelligence - Code of practice for data labeling of machine learning	Valid

Similar standards

GB/T 38665.1 GB/T 38665.2 GB/T 9813.3 GB/T 45434.3 GB/T 45434.2

Basic data

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: 12,153
Date of Issue: 2023-05-23
Date of Implementation: 2023-12-01
Issuing agency(ies): State Administration for Market Regulation, China National Standardization Administration

GB/T 42755-2023: Artificial intelligence - Code of practice for data labeling of machine learning

---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
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

Preface III 1 Scope 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: ......

Image

Tips & Frequently Asked Questions:

Question 1: How long will the true-PDF of GB/T 42755-2023_English be delivered?

Answer: Upon your order, we will start to translate GB/T 42755-2023_English as soon as possible, and keep you informed of the progress. The lead time is typically 1 ~ 3 working days. The lengthier the document the longer the lead time.

Question 2: Can I share the purchased PDF of GB/T 42755-2023_English with my colleagues?

Answer: Yes. The purchased PDF of GB/T 42755-2023_English will be deemed to be sold to your employer/organization who actually pays for it, including your colleagues and your employer's intranet.

Question 3: Does the price include tax/VAT?

Answer: Yes. Our tax invoice, downloaded/delivered in 9 seconds, includes all tax/VAT and complies with 100+ countries' tax regulations (tax exempted in 100+ countries) -- See Avoidance of Double Taxation Agreements (DTAs): List of DTAs signed between Singapore and 100+ countries

Question 4: Do you accept my currency other than USD?

Answer: Yes. If you need your currency to be printed on the invoice, please write an email to Sales@ChineseStandard.net. In 2 working-hours, we will create a special link for you to pay in any currencies. Otherwise, follow the normal steps: Add to Cart -- Checkout -- Select your currency to pay.