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Labeling AI Alternatives

Labeling AI is described as 'deep learning-based auto labeling solution that develops and auto-labels custom AI by learning minimal manual labeling data. Labeling AI innovative tool that can save your time' and is an app. There are more than 10 alternatives to Labeling AI for a variety of platforms, including Online / Web-based, Mac, Windows, Linux and SaaS. The best alternative is UniversalDataTool, which is both free and Open Source. Other great apps like Labeling AI are HyperLabel, Supervisely, Appen and Label Box.

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  • Online
  • Software as a Service (SaaS)

Labeling AI is a deep learning-based auto labeling solution that develops and auto-labels custom AI by learning minimal...

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  1. UniversalDataTool icon

    UniversalDataTool

    • FreeOpen Source
    • Mac
    • Windows
    • Linux
    • Online
    14 alternatives to UniversalDataTool 

    The Universal Data Tool is a User Interface for editing and annotating Images (Computer Vision, Bounding Boxes, Segmentation), Text (Named Entity Recognition, Classification) or general purpose data entry. to view and edit any data defined in the extensible the .udt.

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    1.  PDF annotationUniversalDataTool support PDF Annotation for highlighting, notes etc
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  2. HyperLabel icon

    HyperLabel

    • Free PersonalProprietary
    • Mac
    • Windows
    19 alternatives to HyperLabel 

    A desktop application with a UX built for through-put, HyperLabel is a complete toolset for quality labeling process management and training data creation.

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  3. Supervisely icon

    Supervisely

    • FreemiumProprietary
    • Online
    21 alternatives to Supervisely 

    Supervisely helps people with and without machine learning expertise to create state-of-the-art computer vision applications. We care about entire workflow from raw data to building and deploying neural networks for your special task without coding.

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    This app doesn't have any features at the moment.
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  4. Appen icon

    Appen

    • Online
    20 alternatives to Appen 

    Appen's enterprise crowdsourcing solutions offer a cost-conscious, elastic workforce which gives companies immediate, high-quality work.

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    1.  CrowdsourcedAppen uses suggestions, information, etc., from a large group of people to improve the quality of the content.
  5. Label Box icon

    Label Box

    • Online
    23 alternatives to Label Box 

    A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features.

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    This app doesn't have any features at the moment.
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  6. ByteBridge icon

    ByteBridge

    • Software as a Service (SaaS)
    8 alternatives to ByteBridge 

    Data labeling outsourced service: get your ML training datasets cheaper and faster!.

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  7. Segments.ai icon

    Segments.ai

    • Online
    • Software as a Service (SaaS)
    22 alternatives to Segments.ai 

    The platform for computer vision engineers and labeling teams to iterate between data labeling, model training and failure case discovery.

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    This app doesn't have any features at the moment.
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  8. SuperAnnotate icon

    SuperAnnotate

    • Software as a Service (SaaS)
    6 alternatives to SuperAnnotate 

    SuperAnnotate is the world's leading platform for building the highest quality training datasets for computer vision and NLP. With advanced tooling and QA, ML and automation features, data curation, robust SDK, offline access, and integrated annotation services, we enable...

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  9. TIDE icon

    TIDE

    • FreeOpen Source
    • Mac
    • Windows
    • Linux
    • Python
    4 alternatives to TIDE 

    A General Toolbox for Identifying Object Detection Errors - GitHub - dbolya/tide: A General Toolbox for Identifying Object Detection Errors

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    This app doesn't have any features at the moment.
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  10. FiftyOne icon

    FiftyOne

    • FreeOpen Source
    • Mac
    • Windows
    • Linux
    7 alternatives to FiftyOne 

    FiftyOne provides the building blocks for optimizing your dataset analysis pipeline, including visualizing complex labels, evaluating your models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more!.

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