From imagery to a working model
Reduce the manual work between imagery and model training
Drawing every mask from scratch can slow dataset preparation. Automated first-pass labelling gives reviewers suggestions to accept, reject, or correct. A small, representative seed set can therefore help expand a dataset with less manual drawing. This can shorten the path to the first useful model iteration while keeping specialists in control of label quality.
- 1
Start with a reviewed seed set
Define the feature with representative labels or visual examples.
- 2
Expand labels faster
AI proposes repeated features so experts can review instead of starting blank.
- 3
Train and validate your model
Only reviewed labels enter training, with separate images held out for testing.
- 4
Keep an approved model version
Link the chosen checkpoint to its dataset version and validation results.
Why it matters: teams can spend less time drawing repeated shapes, apply one review standard across a dataset, and reach measured model experiments sooner.
Customer model pathway: reviewed labels can support a detector trained for your assets. SmartData's planned model lifecycle will store customer-owned checkpoints with the dataset version and validation evidence used to create them.
This demonstration covers assisted labelling and human review. The amount of training data required depends on asset variation, operating conditions, class balance, and label quality. Training, held-out validation, checkpoint storage, and deployment remain separate controlled stages.