Unified Labeling Interface for Multimodal Data Annotation
Annotate multiple data types together in a single labeling interface, with shared context, synchronized views, and consistent annotation workflows.
Images · VideoAudio · TextDocs · Medical
Multimodal Annotation Platform
Multimodal Annotation Platform for AI Training Data
Annotate connected image, video, audio, text, document, and sensor data in one governed workspace with shared ontologies, relations, and review workflows.
No credit card required
Multimodal annotation features
Everything You Need for Complex Multimodal Annotation
Organize related image, video, audio, text, and document files into one governed case using Data Groups, filename patterns, and custom layouts.
Dynamic Layouts
Arrange related image, PDF, audio, and other files into resizable custom panels for each grouped workflow.
Advanced Ontologies
Define shared classes, nested attributes, and structured metadata consistently across modalities.
Auto-Grouping
Automatically group related multimodal data into unified annotation tasks for synchronized labeling and review.
Multi-Camera Data4 angle view
Multi-Camera Annotation
Annotate four camera views of one robotics task to follow the robot, gripper, cartons, and pallet across the work cell.
Four camera viewsShared task context
Cross-Modal Annotation
Label and connect information across different modalities within the same dataset or task.
Why AI Teams Choose Unitlab for Multimodal Annotation
Annotate complex multimodal datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
15X
Faster Multimodal Annotation
90%
Automated Multimodal Annotation
10X
Lower Annotation Costs
Supported multimodal annotation types
All multimodal annotation types in one platform.
Use compatible native annotation types across grouped image, video, audio, text, PDF, and medical viewers, plus Item Properties and Relations at case level.
01
Bounding box
Mark rectangular regions in supported visual viewers; tracking applies only to compatible video sequences.
02
Segmentation
Create pixel-accurate masks in compatible image, video, or medical viewers.
03
Polygon
Outline irregular regions in compatible visual viewers, including document markup when supported.
04
Audio event / range
Label sound events or time ranges, segment speakers, and transcribe speech in the native audio viewer.
05
Line / polyline
Trace paths, edges, and document markup in compatible native viewers.
06
Entity / span
Mark entities and spans, then connect them with ontology-backed relations in the native text viewer.
07
Native text selection
Select document text natively and add compatible visual markup when PDF review requires spatial context.
08
Item Properties
Label source, severity, quality, and other properties that describe the grouped case.
09
Relations
Connect evidence, entities, and compatible annotations across the case using explicit relations.
Multimodal dataset curation & annotation workflows
Curate multimodal data and automate annotation workflows.
Search, version, and inspect multimodal datasets, connect AI models, and move annotations through review and approval.
Dataset Versions
Create and manage dataset versions as multimodal data evolves. Track changes, assign work, and keep every version auditable and production-ready.
Semantic Search
Search mixed-source cases and return consistent image, document, audio, text, or video evidence for one query.
Embedding View
Inspect supported case- or asset-level clusters, similar samples, outliers, and quality issues before training.
Integrated Workflows
Route a grouped task through native-view annotation, case review, quality assurance, rework, and release while preserving context.
Bring Your Multimodal Model
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world multimodal datasets.
Questions, answered
Multimodal Annotation Platform FAQs
Answers about grouping related files, native viewers, shared ontologies, review, curation, and model-assisted workflows.
Talk with the Unitlab teamWhat multimodal annotation types does Unitlab support?+
Use boxes, masks, polygons, and lines in visual viewers; audio events, ranges, segmentation, and transcription in audio; entities, spans, and relations in text; native text selection and compatible visual markup in PDF; plus Item Properties and Relations for grouped-case context.
Annotation documentationHow do Data Groups keep related multimodal files together?+
Filename patterns can group related uploads into a Data Group. The group becomes one assignable case, and a configurable layout presents each file through its compatible native viewer.
Multimodal annotation documentationHow do active and passive panels work in a multimodal layout?+
One active panel hosts the compatible native editor for the selected file. Passive panels preserve supported context from the same case without originating annotations or edits.
Multimodal layout documentationHow do shared ontologies and case properties work?+
A shared project ontology defines classes, classifications, properties, and supported relations. Item Properties can capture case-level context while file annotations remain compatible with each native viewer.
Properties and relations documentationHow does Unitlab manage multimodal annotation quality and review?+
Configurable workflows route multimodal tasks through annotation, review, rework, and approval. Instructions, assignments, comments, issues, annotation history, dataset versions, and releases keep quality decisions traceable for individual experts and enterprise teams.
Annotation and review documentationCan teams use their own AI models for multimodal pre-labeling?
Yes. Unitlab can bring custom models into the annotation workflow to generate pre-labels and predictions. Annotators review and correct model output instead of starting from zero, while human approval remains part of the governed quality process.
Model integration documentationCan Unitlab curate, annotate, and version multimodal datasets in one platform?
Yes. Teams can curate multimodal data with metadata filters, semantic search, embeddings, similarity, and outlier discovery, then annotate selected samples, review results, and publish controlled dataset versions for reproducible AI development.
Dataset management documentationMULTIMODAL ANNOTATION PLATFORM
Build Production-Ready Multimodal Datasets with Unitlab
Annotate, track, review, and manage complex multimodal data in one AI-assisted workspace. Move faster from raw footage to production-ready datasets with automated tracking and built-in quality control.
