From raw frames to training-ready labels, in hours.
Four stages from ingest to export. Each one designed to shrink the gap between raw data and a shippable model checkpoint.
Four stages. No manual coordination.
Annotgrove handles the pipeline so your team handles the labels.
Push image batches via REST API, drag-drop, or direct cloud storage sync (S3, GCS, Azure Blob). Any resolution, any format.
Annotgrove runs your selected model (detection, segmentation, or pose estimation) across every frame. Outputs include per-label confidence scores.
Low-confidence labels surface first. Annotators see side-by-side AI output and correction interface. One click accept, drag-to-adjust, or reject.
Download validated annotations as COCO JSON, YOLO, Pascal VOC, or CSV. One API call. Ready to feed your training script.
Accuracy by object type
Internal benchmark on held-out COCO-style test sets. Accuracy = IoU > 0.5 match rate. Results vary by image quality and domain.
| Object type | Annotation type | Accuracy range | Accuracy bar |
|---|---|---|---|
| Vehicle (car, truck, bus) | Bounding box | 93-96% | |
| Person (pedestrian, cyclist) | Bounding box | 91-94% | |
| Retail product | Bounding box | 88-92% | |
| Medical region of interest | Polygon mask | 82-88% | |
| Human pose (keypoints) | Skeletal keypoints | 89-93% |
Three annotation types, one pipeline
Pick the annotation type your model needs. Annotgrove handles the rest.
Bounding boxes
Axis-aligned rectangles for object detection. Works with YOLO, Faster R-CNN, and DETR architectures out of the box.
Semantic masks
Polygon and pixel-level segmentation for instance or semantic segmentation tasks. Exports as COCO RLE or PNG mask.
Skeletal keypoints
Body pose estimation with configurable skeleton definitions. Compatible with MediaPipe and OpenPose annotation formats.
Quality review, not from-scratch drawing
Your annotators see the AI's output first. Accept, drag-adjust, or reject. The low-confidence queue surfaces what needs attention.
Try the pipeline on your own dataset
Free plan covers 10,000 labels per month. No credit card required.