Clarifai Automated Data Labeling addresses the challenges of manual data annotation, which is resource-intensive, error-prone, and slow to scale with rapidly growing datasets. This platform leverages AI-powered automation alongside efficient human review workflows to significantly accelerate the data labeling process. It is designed to decrease label creation time by up to 75% and cut human annotation costs by 50% or more, ultimately speeding up AI model development by up to 100x. The solution enables users to build AI datasets with confidence by simplifying how developers and teams create, share, and run AI at scale. Key capabilities include ingesting individual inputs or archives, with auto-labeling features that support customizable ontologies across imagery, video, and text formats. It automatically indexes input data using customizable embedding vectors for semantic similarity search, facilitating easier dataset management. Users can create, version, and manage datasets for data labeling, model training, and evaluation, with data export capabilities via SDK. The platform also offers AI Assist for faster supervised labeling, ensuring standardization across popular models and frameworks to foster collaboration and innovation.

Features & Benefits

• AI-Powered Automation
  • Automates data labeling using AI models, including those uploaded or custom-trained for specific tasks, and offers powerful semi-supervised auto annotation workflows.
• Enhanced Data Management & Indexing
  • Facilitates the ingestion of individual inputs or archives, auto-indexes input data with customizable embedding vectors for semantic similarity search, and provides tools to create, version, and manage datasets for labeling, model training, and evaluation.
  • Ingest individual inputs or archives
  • Auto-index inputs for search
  • Manage datasets for labeling
• Customization & Flexibility
  • Supports one-click custom model training and allows for the use of advanced hierarchical ontologies for complex use cases.
• Comprehensive Data Type Support
  • Offers versatile labeling capabilities for various data types and use cases.
  • Single label classification: Assigns a single, specific label to an image or text input.
  • Multi-label classification: Associates an image or text input with multiple labels simultaneously.
  • Bounding boxes for object detection: Localizes and identifies objects within an image using 2D bounding boxes.
  • Polygons for segmentation masks: Provides precise pixel-level annotations for custom semantic and instance segmentation models.
  • Video track labeling: Annotates objects or regions of interest within a video sequence over multiple frames, with AI-powered tracking.