Get startedData · Versioned evidence
Build and inspect datasets
Create specialist or multimodal datasets around the outcome the system must understand.
Source reviewed 2026-09-16. Availability depends on your installation, permissions, and compatible runtimes. A supported path is not a guarantee of model quality or production readiness.
Outcome
Create a named collection of media and labels. Review and validation are still required before it can be used for training or testing.
Start here
What you need to know first
Check the inputs below before starting. If you are new, begin with the first-project guide. A dataset holds media and labels; a model produces results; a deployment makes a selected model version callable. Creating one does not create the others.
Bring these inputs
- — Images, video frames, or connected object storage
- — An objective-specific annotation schema
Expected output and limits
- — Immutable dataset versions
- — Split manifests, statistics, and lineage
Core workflow
Choose the objective
Select detection, segmentation, classification, phrase grounding, VQA, or captioning.
Bring the evidence
Upload images or clips, connect storage, or select existing data. To generate images, provide a scene description, objects, and variations; a dataset name is not a generation brief. Review the server quote and accept its maximum before starting. Managed generation costs $0.20 per image saved; unused reserved credit is released. Connected providers bill you directly.
Inspect dataset health
Review splits, classes, balance, annotation coverage, dimensions, and spatial statistics.
Version the contract
Create the next immutable version before downstream training or evaluation.
What this surface supports
Specialist and VLM schemas
Aggregated dataset statistics
Train/validation/test splits
Working versions, frozen inputs, and lineage
Expert section
Contracts, signals, and failure modes
Use this section when you are defining acceptance criteria, automating the surface, or reviewing whether its output is safe to promote downstream.
Quality and operating signals
- — Coverage and class balance
- — Duplicate and corrupt assets
- — Spatial and dimensional distributions
Common failure modes
- — Train/test leakage
- — Schema drift between versions
- — Representative classes with too few examples
Expert release checklist
□ Inputs and dependencies are pinned to immutable versions.
□ Acceptance metrics include critical classes and operating slices.
□ Failure, retry, cost, and rollback behavior are understood.
□ The resulting artifact has an owner and a downstream review path.
Engineering safeguards
- — Schema follows the selected objective
- — Warnings expose imbalance and coverage gaps
- — Downstream runs bind exact versions