Data Labeling Tool
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A Data Labeling Tool is a annotation tool that enables the annotation of various types of data, such as audio, text, images, videos, and time series data, for the purpose of creating training data for machine learning models.
- Context:
- It can be designed to support a wide range of data types and annotation tasks, including but not limited to classification, segmentation, and object detection.
- It can (often) be integrated into machine learning workflows and AI pipelines, facilitating the development of more accurate models by improving the quality and specificity of training data.
- It can provide features for ML-assisted labeling, utilizing pre-existing models to automate or semi-automate the annotation process, thereby increasing efficiency and reducing manual labor.
- It can offer flexibility in deployment options, including local installations and cloud-based services, to accommodate different project scales and team sizes.
- It can support collaboration among multiple annotators and project managers, featuring user management, project segmentation, and progress tracking to streamline the annotation process.
- It can connect to various cloud storage services like Amazon S3 and Google Cloud Storage, enabling teams to work directly with data stored in the cloud.
- It can include quality control mechanisms, such as consensus-based annotation and review workflows, to ensure the high quality of labeled data.
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- Example(s):
- Label Studio, a versatile data labeling tool that supports various data types and annotation tasks.
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- Counter-Example(s):
- A Data Analysis Tool that focuses on analyzing and visualizing data rather than annotating it.
- A Database Management System that is used for storing, querying, and managing data, but not for labeling.
- See: Machine Learning Dataset, Annotation Workflow, Automated Data Labeling, Collaborative Annotation, Data Annotation Service, Open Source Data Labeling Tools.