AI-Assisted Annotation on Datature: SAM, IntelliBrush, and Auto-Labeling
Manual annotation is the bottleneck in most computer vision projects. This tutorial covers every AI-assisted labeling tool available on Datature Nexus: Segment Anything (SAM), IntelliBrush, model-assisted labeling, and auto-detection. Each one reduces the clicks per annotation from dozens to a handful.
What This Tutorial Covers
- SAM (Segment Anything Model): click a point or draw a box, get a pixel-perfect mask
- IntelliBrush: Datature's proprietary smart brush that snaps to object edges
- Model-assisted labeling: run a trained model on unlabeled data to pre-generate annotations
- Auto-detection: batch-apply a model across your entire dataset
- When to use which tool depending on object complexity and dataset size
The Speed Difference
Manual polygon annotation for a complex object takes 30-60 seconds per instance. SAM-assisted annotation takes 2-5 seconds. On a dataset of 10,000 images with 3 objects each, that is the difference between 250 hours of manual work and 40 hours of assisted labeling. IntelliBrush sits between the two: faster than manual, more controllable than SAM for tricky boundaries.
Who This Is For
Annotation teams looking to speed up labeling without sacrificing quality. ML engineers who want to use an existing model to bootstrap labels on new data. Anyone comparing annotation platforms and evaluating smart tooling.
