V7

Uses machine learning models to automatically detect and label objects, regions, or features in images and video frames. Reduces manual annotation effort by pre-labeling visual content that can be reviewed and corrected by human annotators.

Provides tools for human annotators to manually draw, mark, and label regions of interest in images with various annotation types including bounding boxes, polygons, segmentation masks, and keypoints. Supports collaborative annotation workflows with multiple annotators.

Manages user roles, permissions, and access controls for team members working on shared datasets. Tracks who annotated what, enables task assignment, and maintains audit logs of all changes.

Allows integration of custom machine learning models for automated labeling or data analysis. Supports bringing your own models trained externally to assist with annotation tasks within V7.

Provides dashboards and reports showing annotation progress, dataset statistics, quality metrics, and team performance. Enables monitoring of annotation workflows and identification of bottlenecks.

Maintains complete version history of datasets including which annotations were added, modified, or removed in each version. Tracks data lineage showing how datasets were created, transformed, and used in model training for reproducibility and compliance.

Implements workflows for reviewing and validating annotations created by other team members. Includes consensus checking, inter-annotator agreement metrics, and tools for flagging inconsistencies or low-quality labels for correction.

Seamlessly connects with major cloud storage and compute platforms including AWS S3, Google Cloud Storage, and Azure Blob Storage. Enables direct data access without downloading, and integrates with MLOps pipelines for automated workflows.

Provides tools to filter datasets by annotation status, metadata attributes, or quality metrics, and to create representative samples for training, validation, or testing. Enables intelligent data selection without manual curation.

Automatically extracts frames from video files at specified intervals or key moments, and enables annotation of video sequences. Supports temporal relationships and frame-level labeling for video understanding tasks.

Allows creation and management of reusable annotation templates and class hierarchies (ontologies) that define what can be labeled and how. Ensures consistency across annotators by enforcing standardized labeling structures.

Uses trained models to identify the most informative or uncertain samples in a dataset that would benefit most from annotation. Prioritizes annotation work on high-value data points to maximize model improvement with minimal labeling effort.

Supports bulk import of images, videos, and metadata from various sources and formats, and export of annotated datasets in standard formats compatible with ML frameworks. Handles large-scale data ingestion and output.