Why people look for a CVAT alternative
CVAT (Computer Vision Annotation Tool) is excellent for large-scale ML training dataset production. If you need to annotate tens of thousands of images for model training with a full team, CVAT is hard to beat.
But CVAT is frequently reached for when the actual task is much simpler: defining a handful of detection zones or exclusion regions on a reference frame so a deployed CV system knows where to look. For that workflow, CVAT is overkill — and its setup requirement is a real obstacle.
Setting up CVAT involves pulling a Docker Compose stack, configuring volumes, exposing a port, and maintaining the server. That's appropriate for a production annotation pipeline. It's not appropriate for a CV engineer who needs to sketch three polygons on a camera frame and export the coordinates before lunch.
What RegionKit does instead
RegionKit is a browser-based ROI editor built specifically for the zone-definition workflow. You open it in any browser — Chrome, Firefox, Edge, Safari — load an image, draw your zones, and export the coordinates. Nothing to install, no server to maintain, no account to create.
Key differences in the zone-definition use case:
Layers — RegionKit organises regions into named layers (Detection, Exclusion, Tripwires) with independent visibility and lock controls. CVAT has labels and attributes, which work differently and are designed around per-object classification rather than spatial zone organisation.
Shared vertices — When two adjacent zones share a boundary, you can link their shared vertices in RegionKit. Dragging one vertex updates both zones simultaneously, keeping boundaries consistent. This is essential for floor plans and tightly packed detection areas.
Offline-first — Once the page loads, RegionKit works without an internet connection. CVAT requires a running server at all times.
Publish and share — If you need to share a scene for review, RegionKit lets you publish to a URL with one click. The recipient can view — or, if you share an edit link, publish their own changes.
When to use CVAT instead
CVAT wins when you need:
- Scale — annotating thousands of images for model training
- Team workflows — task assignment, review queues, annotation statistics
- AI assistance — automatic pre-labelling with SAM or Grounding DINO
- More export formats — Pascal VOC, Datumaro, LabelMe, and others
If you're building a training dataset with a team, use CVAT. If you're defining zones for a deployed system, try RegionKit — you'll be drawing zones in under a minute.