Computer vision MLOps

CVLabel Cloud

Dataset and model versioning for teams building computer-vision systems. Organize images, annotations, validation runs, model artifacts, and collaboration in one workspace.

Example dataset pipeline

Detection Dataset / v12

Ready

Images

42k

Annotations

119k

Classes

18

Models

7

Import images

Batch upload, archives, or cloud import.

Attach labels

Map classes and annotation formats.

Validate

Find orphan labels and invalid geometry.

Version chain

Dataset version

v12 - validation clean

Training run

YOLO / experiment

Model version

detector-production

Team roles

Separate dataset managers, annotators, reviewers, model managers, and viewers.

Dataset Registry

Create dataset workspaces, track image batches, annotation sets, class taxonomy, and review status.

Image and Annotation Imports

Bring in image batches with COCO, YOLO, Pascal VOC, CVAT, or Label Studio annotations.

Dataset Versioning

Freeze trainable dataset snapshots with changelogs, validation reports, and reproducible exports.

Model Registry

Upload model artifacts, link them to dataset versions, and compare versions by metrics.

Built around trainable datasets, not generic folders.

Every upload should become part of a dataset, annotation set, validation report, or model version. The workspace tracks lineage from raw images to shipped artifacts.

Image and annotation pair validation
Class taxonomy and label drift review
Model artifact version history
Workspace roles for dataset teams

Start with a dataset workspace.

Upload images, import annotations, validate labels, then version models against the dataset.

Create workspace