Annotated 1.2M images for a perception model
An autonomous robotics startup
A continuous semantic-segmentation pipeline at 1.2M-image scale with weekly delivery, gold-set calibration, and inter-annotator agreement tracking — production-grade training data on a release cadence.
This case study is part of our AI Annotation work — see how the same approach scales for other teams in our portfolio.
Challenge
What we walked into.
Their perception team needed clean, consistent labels at a scale and cadence that an in-house team couldn't sustain. Throughput was capping the rate at which they could iterate on the model, and quality drift between batches was hurting eval scores.
What we did
The work, step by step.
Designed a labelling ontology and gold-set in collaboration with the model team, used to calibrate every annotator before they touched real data
Ran a continuous semantic-segmentation pipeline at 1.2M-image scale with weekly delivery
Tracked inter-annotator agreement per class and shipped a QA report alongside every batch
Tuned guidelines mid-programme based on model-error analysis, closing the loop between data and predictions
Results
What it shipped.
Outcomes measured against the brief we agreed up front, not vanity metrics.
- Images labeled1.2M
- Throughput+62%
- IAA score0.93
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