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GB/T 45225-2025Artificial intelligence - Deep learning algorithms evaluation (English PDF)

人工智能 深度学习算法评估

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Issued by

SAMR; SAC

Level / Type

National · Recommended

Issue date

January 24, 2025

Implementation date

January 24, 2025

Scope

GB/T 45225-2025 is the English-translated version of 人工智能 深度学习算法评估.

GB/T 45225-2025 is the Chinese standard "Artificial intelligence - Deep learning algorithms evaluation".

Its scope clause reads: This document establishes an evaluation index system for artificial intelligence deep learning algorithms and describes evaluation methods, etc. This document is intended to guide deep learning algorithm developers, users, third parties and other related organizations to deep learning algorithms and their training.

The obtained deep learning model is used for evaluation. Its clauses include terms and definitions.

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Document preview — GB/T 45225-2025

National Standard of the People's Republic of China

ICS
35.100.05
Classification
L79

Issued by: State Administration for Market Regulation; Standardization Administration of the PRC

Contents

  • Preface
  • 1 Scope
  • 2 Normative references
  • 3 Terms and Definitions
  • 4 Evaluation Index System
  • 5 Assessment Level
  • 6 Evaluation Process
  • Appendix A (Informative) Selection of Deep Learning Algorithm Evaluation Indicators and Threshold Setting
  • Appendix B (Informative) Method for calculating weights of deep learning algorithm evaluation indicators
  • Appendix C (Informative) Deep Learning Algorithm Evaluation Implementation Case

1 Scope

This document establishes an evaluation index system for artificial intelligence deep learning algorithms and describes evaluation methods, etc.

This document is intended to guide deep learning algorithm developers, users, third parties and other related organizations to deep learning algorithms and their training.

The obtained deep learning model is used for evaluation.

2 Normative references

The contents of the following documents constitute the essential clauses of this document through normative references in this document.

For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to

This document.

GB/T 35273-2020 Information security technology Personal information security specification

GB/T 40660-2021 Information security technology Basic requirements for biometric information protection

GB/T 41867-2022 Information Technology Artificial Intelligence Terminology

3 Terms and definitions

The terms and definitions defined in GB/T 41867-2022 and the following apply to this document.

3.1 Deep learning

A method for creating rich hierarchical representations by training neural networks with many hidden layers.

Note. Deep learning is a subset of machine learning.

[Source. GB/T 41867-2022, 3.2.27]

3.2

A code snippet that uses a deep neural network structure to learn and reason to complete a specific function.

3.3 deep learning model

A mathematical framework that produces inferences or predictions based on input data or information.

3.4 Test datatestdata

Data used to evaluate the performance of the final machine learning model.

[Source. GB/T 41867-2022, 3.2.3]

3.5 Adversarial examples

Adding input samples formed by slight interference in the data set can induce the deep learning algorithm to give wrong output with a high probability, or even is to give a specific result.

......
This preview omits tables, figures, formulas and parts of the technical clauses. The complete document — 30 pages — is available in the English PDF.

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GB/T 45225-2025

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