Overview

Supports bounding boxes, polygons, key points, cuboids, polylines, and semantic segmentation.

Allows classification, tagging, and hierarchical annotation for complex labeling needs.

Enables automation features like model-assisted annotation and active learning to speed up labeling.

Focuses on precise object segmentation using polygons and pixel-wise masks.

Supports automatic segmentation models and interactive tools like smart annotation.

Ideal for tasks requiring fine-grained object differentiation in images.

Provides frame-by-frame object tracking, interpolation, and temporal segmentation.

Supports event classification and action recognition for video AI models.

Includes smart tracking and pre-labeling features to optimize efficiency.

Designed for annotating speech, music, environmental sounds, and more.

Supports audio segmentation, speaker dialyzation, and multi-channel audio labeling.

Enables transcription and classification tasks, often used for NLP models.

Supports text classification, named entity recognition (NER), relation extraction, and sentiment analysis.

Enables multi-language annotation, including token-level, sentence-level, and document-level tagging.

Integrates GenAI models for AI-assisted labeling to speed up NLP tasks.

Data Extraction: Annotators label key fields (like dates, totals, names) in PDFs such as invoices or contracts to convert unstructured content into structured data for use in applications.

Content Review: Teams highlight, comment, and collaborate directly on PDFs for tasks like legal reviews, policy updates, or editorial checks, streamlining feedback and approval workflows.

ML/NLP Training Data: Annotated PDFs serve as ground truth for training machine learning models in tasks like entity recognition, or document classification.

Supports 3D point cloud annotation with bounding boxes, segmentation, and classification.

Works with multi-sensor fusion (LiDAR + images) for autonomous vehicle applications.

Provides semi-automatic tools for annotation acceleration.

Designed for human preference labeling to fine-tune AI models.

Supports comparative ranking, rating scales, and open-ended feedback.

Primarily used in GenAI model training, such as LLMs and chatbots.

Geospatial data labeling with polygon and bounding box tools.

Satellite and aerial image segmentation.