Receiving and understanding tasks: Data annotators usually receive annotated tasks from project managers or supervisors to understand the background, purpose and annotation rules of the tasks.
Use labeling tools to label data: According to the task requirements, data labeling personnel need to use specific labeling tools to process data. This may include image annotation (such as points and boxes), text annotation (such as screening, annotation, correction translation), voice annotation (such as transcribing voice) or video annotation (such as screenshot or video annotation).
Check and correct labeling data: In order to ensure data quality, data annotators need to check and correct their labeling results regularly. This may involve communication and collaboration with team members or quality control personnel.
Submit and record the work results: after completing the labeling task, the data annotator needs to submit the work results and record the relevant work details and precautions for subsequent reference and improvement.
Participate in training and improve skills: In order to cope with different types and difficulties of labeling tasks, data labeling personnel may need to constantly learn and improve their skills. This may include attending training, sharing sessions or self-study organized by the company.
Keep communication and cooperation with the team: In daily work, the data annotator needs to keep close communication and cooperation with the project manager, technical team and other relevant departments to ensure the smooth progress of the work and the smooth completion of the project.
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As a supplier of AI basic data industry, Jinglianwen Technology can help artificial intelligence enterprises solve the corresponding problems of data labeling in the whole artificial intelligence chain.
At present, there are four large-scale data processing bases in China, and the intelligent labeling platform covers labeling workbench and capacity management system, providing complete data processing capabilities of voice, image, text and video in various fields.
The labeling platform is equipped with SAM-related algorithm to improve labeling efficiency and develop automatic labeling function, which can preprocess the data, adjust the model according to the labeling results, and flexibly configure the labeling process according to the scene to further ensure the labeling accuracy.
The tag platform supports multi-dimensional flexible data retrieval, graphical display of tag results, intuitive visual interface, version management of tag data and fine authority management functions, which effectively improves the productivity of AI data and helps enterprises and teams to manage data and develop AI more efficiently.
It can provide customized data labeling ability according to different requirements, break through the boundary between data and application scenarios, support comprehensive quality inspection, acceptance and management, open the acceptance channel of Party A, and support online export of multi-format labeling results, with the highest labeling accuracy of 99%, achieving high standards, high quality and fast delivery.
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In terms of data security compliance, Jinglianwen Technology has passed ISO900 1 Quality, ISO2700 1 Information Security and ISO2770 1 International Privacy Security Management Certification, and participated in the formulation of eight national data exchange formats and data security standards.
Deliver massive and high-quality AI algorithm training data to thousands of artificial intelligence companies and university research institutions around the world in the fields of intelligent driving, intelligent security, intelligent medical care, intelligent education, intelligent finance, intelligent customer service and new retail.