Real-world computer vision data
Real-WorldComputer Vision Data.
Train detection, segmentation, and video intelligence systems with diverse real-world imagery, expert annotation, and production-ready delivery.
One platform. Three data engines.
Contributor Network
- In-the-wild capture
- Multi-scene diversity
- Hard negative collection
- Lighting variation
- Edge condition capture
Expert Network
- CV researchers
- Annotation specialists
- Domain experts
- QA validators
- Dataset engineers
Evaluation Layer
- mAP gap analysis
- False positive mining
- Occlusion testing
- Low-light failure modes
- Distribution shift
Build for the real world
Object Detection
Bounding boxes, Multi-class, Dense scenes
Instance Segmentation
Pixel-level masks, Panoptic, Part segmentation
Multi-Object Tracking
Re-identification, Occlusion handling, Trajectory labels
Action Recognition
Temporal modelling, Clip labelling, Temporal boundaries
Low-Light & Adverse Scenes
Night, Fog, Rain, Motion blur
Retail & Surveillance
Shelf monitoring, People counting, Anomaly detection
The Harbor Data Flywheel
From in-the-wild capture to production-ready vision.
A continuous loop that turns diverse real-world imagery into robust, accurate visual intelligence.
Learn moreData Types
Annotated Images
High-resolution images with bounding boxes, segmentation masks, and keypoints across diverse scenes and conditions.
- Multi-class bounding boxes
- Instance and semantic masks
- Keypoint and pose labels
- Structured lighting variation
Why teams choose Harbor
Ship Faster
Managed annotation workflows mean your dataset is ready in days, not months.
Diversity at Scale
Global contributors capture the visual diversity production models need — not curated stock photos.
Annotation Quality
Multi-stage expert review with consensus scoring ensures labels your model can depend on.
Example Computer Vision Programs
Large-Scale Detection Dataset
Panoptic Segmentation Dataset
Action Recognition Dataset
Better Vision Models Start With Better Data.
Real contributors. Expert annotation. Computer vision datasets at scale.