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Dataset Creation
Tbilisi AI Lab Annotation

Tbilisi AI Lab Annotation

Build and manage datasets for machine learning

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What is Tbilisi AI Lab Annotation ?

Tbilisi AI Lab Annotation is a cutting-edge tool designed for building and managing datasets for machine learning applications. It provides a comprehensive platform for data annotation, enabling users to create high-quality training data efficiently. This tool is particularly useful for data scientists, machine learning engineers, and researchers who need to prepare datasets for various AI models.

Features

• Intuitive Annotation Interface: Streamline the annotation process with a user-friendly interface.
• Multi-Format Support: Handle diverse data types, including text, images, and videos.
• Collaborative Workflow: Invite team members to collaborate in real-time for faster dataset creation.
• Data Validation: Ensure consistency and accuracy with built-in validation checks.
• Integration with ML Pipelines: Seamlessly export annotated data to machine learning workflows.

How to use Tbilisi AI Lab Annotation ?

  1. Sign Up/Log In: Create an account or log in to the Tbilisi AI Lab Annotation platform.
  2. Upload Your Data: Import the data you wish to annotate (text, images, videos, etc.).
  3. Annotate Data: Use the annotation tools to label and categorize your data.
  4. Manage Annotations: Review, edit, or delete annotations as needed.
  5. Export Data: Once done, export the annotated dataset in a format compatible with your machine learning model.
  6. Monitor Progress: Track the status of your dataset creation and collaborate with team members.

Frequently Asked Questions

What types of data can I annotate with Tbilisi AI Lab Annotation?
You can annotate text, images, and videos, making it suitable for a wide range of machine learning tasks.

Can I collaborate with others in real-time?
Yes, Tbilisi AI Lab Annotation supports real-time collaboration, allowing multiple users to work on the same dataset simultaneously.

How do I export annotated data?
Once your annotations are complete, you can export the dataset in formats such as CSV, JSON, or COCO for direct integration into machine learning pipelines.

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