Multi-version data set labeling and management method supporting large model training

Abstract

The invention discloses a multi-version data set annotation and management method supporting large model training. The method comprises the following steps: firstly, carrying out data annotation; secondly, version control is carried out, the change of each time of labeling can be automatically recorded, and a new version is generated; secondly, integration is achieved, data annotation and a version control system are integrated, and it is ensured that a new version can be automatically generated through each time of annotation change and is associated with operation of a user; and finally, developing an interface which is used for enabling a user to switch versions, check historical annotations and perform comparison and recovery. Through systematic labeling and version control, the consistency and accuracy of a data set are ensured, so that the model training effect is improved; a user can easily switch and compare data sets of different versions to adapt to constantly changing training requirements; rapid generation and version modification are supported, and rapid testing and iteration of the model on different data sets are facilitated; through the operation log and the version history, the data change can be traced, and the transparency of data management is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of software development technology, and in particular to a multi-version data set annotation and management method that supports large model training. Background Art

[0002] In the application of machine learning and deep learning, the quality and diversity of the data set directly affect the performance of the model. As the application scenarios continue to expand, the scale and complexity of the training data set are also increasing.

[0003] The proposed method of labeling and managing multi-version datasets for model training aims to solve the pain points in dataset labeling and management by integrating existing technologies, improve the efficiency and quality of model training, and promote the development of intelligent data management. It provides a visual display of the dataset to help users understand the data structure and labeling process, and enhance the intuitiveness of dataset labeling and management. Summary of the invention

[0004] In response to the above technical problems in the related art, the present invention proposes a multi-version dataset annotation and management method that supports large model training, which can overcome the above shortcomings of the prior art.

[0005] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows: A method for labeling and managing multi-version datasets that supports large model training; The multi-version dataset annotation and management method supporting large model training specifically includes the following steps: Step S1: data labeling; Step S2: Version control, which is used to implement a version management system that can automatically record each annotation change and generate a new version; Step S3: Integration implementation: Integrate data annotation with the version control system to ensure that each annotation change can automatically generate a new version and associate it with the user's operation; Step S4: Develop an interface to allow users to easily switch versions, view historical annotations, and perform comparison and recovery operations.

[0006] Furthermore, the step S1 includes the following specific steps: Step S101: Data preparation: collect and clean data to ensure data quality; perform preliminary classification of data for easy labeling; Step S102: annotation tool selection: select or develop an annotation tool that meets the project requirements and supports the required annotation types; Step S103: Formulate annotation specifications: formulate annotation specifications and guidelines to ensure the consistency and accuracy of annotation; Step S104: Annotation execution: the annotator annotates the data using tools; Step S105: Quality check: Implement a real-time quality control mechanism to check the annotation results; Step S106: Automatic backup: Implement a regular backup mechanism for the data set to prevent data loss.

[0007] Furthermore, step S2 includes the following specific steps: Step S201: Version identification: Generate a unique identifier for each version of the data set, and record information such as creation time and modifier; Step S202: Version comparison: providing tools to support users to compare different versions of data and view specific changes; Step S203: Backtracking function: By setting the current version to quickly restore to a certain historical version, users can switch versions according to their needs to ensure the flexibility of data management; Step S204: Audit and log: record all versions of operation logs, including who performed what operations, to ensure data traceability and security; Step S205: Version storage: select a storage solution that ensures data security and easy access.

[0008] Furthermore, in step S104, the data is annotated manually, semi-automatically or fully automatically.

[0009] Furthermore, in step S105, the quality control mechanism is to use an automated tool to check the annotation results or to manually review the annotation results.

[0010] Furthermore, in step S205, the storage solution is cloud storage, database or local file system.

[0011] The beneficial effects of the present invention are as follows: Through systematic labeling and version control, the consistency and accuracy of the data set are ensured, thereby improving the effect of model training. Users can easily switch and compare different versions of data sets to adapt to changing training needs. It supports rapid generation and modification of versions, which facilitates rapid testing and iteration of models on different data sets. Through detailed operation logs and version histories, users can trace data changes and enhance the transparency of data management. It supports multi-user collaboration, and team members can work in parallel on the same project to improve labeling and management efficiency. Using historical versions of data sets for retrospective analysis will help find the best data set combination and improve model performance. The combined effect of this method will significantly optimize data labeling and management during model training. DETAILED DESCRIPTION

[0012] According to the multi-version dataset annotation and management method supporting large model training described in an embodiment of the present invention, the consistency and accuracy of the data are ensured through a systematic multi-version dataset annotation and management process, thereby improving the training effect and performance of the model. Provide users with flexible data version selection and switching capabilities, support rapid adaptation under different training requirements, and facilitate experiments and optimization of models. Through integrated collaboration tools, support multiple users to annotate and manage datasets at the same time, and promote efficient communication and collaboration among teams. Provide a complete version control mechanism to record the history of each dataset modification, so that users can easily track data changes and trace back to any historical version.

[0015] The dataset annotation service supports image classification, target detection, semantic segmentation, target tracking, text annotation, audio annotation, etc. It supports multiple data annotation formats such as pictures, videos, text, tables, audio, and custom formats, and can intuitively present the effects of annotation algorithm tasks.

[0016] Dataset multi-version management supports the creation, modification, and annotation of custom datasets. It also supports importing existing datasets. Command line tools can be used to perform user authentication, file upload, and dataset import operations. A version management system is implemented that can automatically record each annotation change and generate a new version.

[0017] The implementation process of data annotation and version control can be divided into several key steps: 1. Data labeling process: 1.1 Data preparation: Collect and clean data to ensure data quality. Perform preliminary classification of data for easy labeling.

[0018] 1.2 Annotation tool selection: Select or develop an annotation tool that suits the project requirements and supports the required annotation types (such as images, text, audio, etc.).

[0019] 1.3 Marking specification development: Develop marking specifications and guidelines to ensure consistency and accuracy of marking.

[0020] 1.4 Annotation execution: Annotators use tools to annotate data, which may be manual, semi-automatic or fully automatic.

[0021] 1.5 Quality Check: Implement real-time quality control mechanisms and use automated tools or manual review to check annotation results.

[0022] 1.6 Automatic backup: Implement a regular backup mechanism for data sets to prevent data loss.

[0023] 2. Version control process: Implement a version management system that can automatically record each annotation change and generate a new version. Newly created datasets have no version information and can only be used for model training or development after the release operation is executed. The dataset versions are uniformly named according to the V00001, V00002, ... rules.

[0024] 2.1 Version Identification: Generate a unique identifier for each version of the dataset, and record information such as creation time and modifier.

[0025] 2.2 Version comparison: Provide tools to support users to compare data of different versions and view specific changes.

[0026] 2.3 Backtracking function: You can quickly restore to a historical version by setting the current version. Users can switch versions as needed to ensure the flexibility of data management.

[0027] 2.4 Audit and Log: Record all versions of operation logs, including who performed what operations, to ensure data traceability and security.

[0028] 2.5 Storage: Choose a suitable storage solution (such as cloud storage, database, or local file system) to ensure data security and easy access.

[0029] 3. Integration Implementation: Integrate data annotation with the version control system to ensure that each annotation change can automatically generate a new version and be associated with the user's operations.

[0030] 4. Development interface: Allow users to easily switch versions, view historical annotations, and perform comparison and recovery operations.

[0031] In summary, with the help of the above technical solutions of the present invention, the consistency and accuracy of the data set are ensured through systematic labeling and version control, thereby improving the effect of model training. Users can easily switch and compare different versions of data sets to adapt to changing training needs. It supports rapid generation and modification of versions, which facilitates rapid testing and iteration of models on different data sets. Through detailed operation logs and version histories, users can trace data changes and enhance the transparency of data management. It supports multi-user collaboration, and team members can work in parallel on the same project to improve labeling and management efficiency. Using historical versions of data sets for retrospective analysis helps to find the best data set combination and improve model performance. The combined effect of this method will significantly optimize data labeling and management during model training.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for labeling and managing multi-version datasets that supports large model training, characterized in that: The specific steps include: Step S1: data labeling; Step S2: Version control, which is used to implement a version management system that can automatically record each annotation change and generate a new version; Step S3: Integration implementation: Integrate data annotation with the version control system to ensure that each annotation change can automatically generate a new version and associate it with the user's operation; Step S4: Develop an interface to allow users to easily switch versions, view historical annotations, and perform comparison and recovery operations.

2. According to claim 1, a multi-version dataset annotation and management method supporting large model training is characterized in that: The step S1 comprises the following specific steps: Step S101: Data preparation: collect and clean data to ensure data quality; perform preliminary classification of data for easy labeling; Step S102: annotation tool selection: select or develop an annotation tool that meets the project requirements and supports the required annotation types; Step S103: Formulate annotation specifications: formulate annotation specifications and guidelines to ensure the consistency and accuracy of annotation; Step S104: Annotation execution: the annotator annotates the data using tools; Step S105: Quality check: Implement a real-time quality control mechanism to check the annotation results; Step S106: Automatic backup: Implement a regular backup mechanism for the data set to prevent data loss.

3. According to claim 1, a multi-version dataset annotation and management method supporting large model training is characterized in that: The step S2 comprises the following specific steps: Step S201: Version identification: Generate a unique identifier for each version of the data set, and record information such as creation time and modifier; Step S202: Version comparison: providing tools to support users to compare different versions of data and view specific changes; Step S203: Backtracking function: By setting the current version to quickly restore to a certain historical version, users can switch versions according to their needs to ensure the flexibility of data management; Step S204: Audit and log: record all versions of operation logs, including who performed what operations, to ensure data traceability and security; Step S205: Version storage: select a storage solution that ensures data security and easy access.

4. According to claim 2, a multi-version dataset annotation and management method supporting large model training is characterized in that: In step S104, the data is annotated manually, semi-automatically or fully automatically.

5. According to claim 2, a multi-version dataset annotation and management method supporting large model training is characterized in that: In step S105, the quality control mechanism is to use an automated tool to check the annotation results or to manually review the annotation results.

6. According to claim 3, a multi-version dataset annotation and management method supporting large model training is characterized in that: In step S205, the storage solution is cloud storage, database or local file system.