Detection & segmentation
Boxes, polygons, and semantic and instance masks for perception models.
Multimodal & vision labeling
Precise annotation for images and video.
Pixel-accurate annotation by trained labelers, with domain experts in the loop for anything specialized.
What it is
Dense, consistent labels across every frame.
Vision and multimodal models are only as good as their labels. We annotate images, video, and multi-view media to your spec and schema.
- Bounding boxes and polygons
- Semantic and instance segmentation
- Keypoints and pose
- Video object tracking and event tagging
- Dense captioning and visual Q&A
Dense visual annotationVision
Who it’s for
Built for teams training models that see.
Pose & tracking
Keypoints, pose, and object tracking across video frames and multi-view captures.
Multimodal models
Dense captions and visual Q&A that pair images and video with language.
How it works
Labeled, reviewed, delivered in your schema.
Scope
We agree label types, ontology, media, and format with you on a short scoping call.
Annotate
Trained labelers annotate to written guidelines, with domain experts in the loop for specialized content.
Consensus QA
Multi-pass consensus review checks every label before it ships to your storage.
What’s in a record
Every asset ships with labels you can train on.
- Image or video reference with frame timestamps
- Boxes, polygons, masks, or keypoints
- Track IDs and event tags for video
- Captions and visual Q&A pairs
- Privacy flags where relevant
- Review passes and approval status
FormatsCOCO, YOLO, JSON, custom
MediaImages, video, multi-view
QAMulti-pass consensus review
DeliveryYour storage, your schema
Every delivered recordYour schema
- Source
- Domain, task, modality, expert role & experience
- Content
- Media or text, with timestamps where relevant
- Labels
- Steps, actions, objects, intents, rubrics, scores
- Expert notes
- Rationale, technique, edge cases
- Privacy
- Face blur & PII redaction flags
- Consent
- Contributor agreement & site/owner consent
- QA
- Review passes and approval status
- Format
- JSON · JSONL · CSV · parquet · COCO
FAQ
Vision & multimodal labeling: common questions
What vision annotation types do you support?
Bounding boxes and polygons, semantic and instance segmentation, keypoints and pose, video object tracking and event tagging, and dense captioning and visual Q&A.
Which formats do you deliver?
COCO, YOLO, JSON, or a custom format, in your schema.
Do you label video and multi-view data?
Yes. We annotate images, video, and multi-view media, including object tracking and event tagging across frames.
How is quality controlled?
Every label goes through multi-pass consensus review before delivery.
Who does the labeling?
Trained labelers, with domain experts in the loop for anything specialized.
Where is data delivered?
To your storage, in your schema — for example S3, a private Hugging Face repo, or your own bucket.
Start with a sample
See the data before you commit.
Tell us what your model needs to learn. We’ll scope a pilot and send a sample set in your schema.