Build and manage datasets for machine learning
Find and view synthetic data pipelines on Hugging Face
Manage and orchestrate AI workflows and datasets
Launch and explore labeled datasets
Explore and manage datasets for machine learning
Search for Hugging Face Hub models
Review and rate queries
Organize and invoke AI models with Flow visualization
Data annotation for Sparky
Download datasets from a URL
Provide feedback on AI responses to prompts
Create a report in BoAmps format
Create and validate structured metadata for datasets
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.
• 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.
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.