Mindkosh AI icon
Mindkosh AI icon

Mindkosh AI

Mindkosh is an all-in-one platform for curating, annotating and validating datasets for your AI/ML projects. Annotate your images, videos or lidar datasets with automated tools and track your project's progress and quality with efficient tools.

Interface for image annotation on the Mindkosh annotation platform. Label images with bounding boxes, polygons, polylines, key-points, cuboids and segmentation using intuitive AI-powered tools. OCR and rotated bounding boxes also supported.

Cost / License

  • Freemium (Subscription)
  • Proprietary

Platforms

  • Online
  • Software as a Service (SaaS)
Interface for video annotation on the Mindkosh annotation platform. Annotate long HD videos quickly with automatic interpolation. No need to break videos into images with Mindkosh's native video labeling tool.
Interface for point cloud annotation on the Mindkosh annotation platform. Label point clouds from a variety of devices including lidar with cuboids and segmentation using intelligent automatic tools. Sensor fusion labeling also supported.
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Mindkosh AI information

  • Developed by

    IN flagMindkosh technologies Private limited

  • Licensing

    Proprietary and Freemium product.

  • Pricing

    Subscription ranging between $26 and $220 per month + free version with limited functionality.

  • Alternatives

    0 alternatives listed

  • Supported Languages

    • English
Mindkosh AI was added to AlternativeTo by shikhardevgupta on and this page was last updated .
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What is Mindkosh AI?

Mindkosh is an AI-powered platform that allows teams to easily manage large data labeling projects.

The platform offers AI-assisted tools to quickly label large volumes of images, videos and 3D point-clouds (lidar data) for a variety of use cases.

The platform also offers a host of project management and quality measurement tools to help teams track their project's progress and measure the quality of their annotated datasets.

With Mindkosh, Machine Learning teams can curate, label and validate their datasets quickly and efficiently using Data Centric principles to create truly performant datasets that enable successful deployment of AI systems.

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