Models trained on your own data.
We train our own models for anonymization and ISA validation. When a model meets footage it does not know yet, we retrain it on that footage inside the EU, and it ships only after it passes gates written before the run and a person signs it off.
The models we train.
- Service
Anonymization modelAEON
Faces and plates in camera data from vehicles and robots, masked in the EU, with evidence per file.
Learn more - Service
ISA validation model
ISA validation under Delegated Regulation (EU) 2021/1958: speed limits from two independent sources, the vehicle graded automatically.
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How a model gets better, and how it ships.
Every release goes through the same loop. A model that fails a gate is recorded as rejected, with the reason.
- 01
Harvest
Frames and detections are sampled from production batches.
- 02
Audit
Two teacher models label every frame. Where they disagree, every frame is reviewed by eye.
- 03
Dataset
Verified false detections become hard negatives, verified misses become positives. Splits are made by recording, with provenance hashes.
- 04
Retrain
Training starts from the exact production checkpoint, proven by its hash. The gates are written before the run.
- 05
Gates
False detections on held-out data must drop, plates must hold, the full dataset is swept and other domains are checked.
- 06
Sign-off
A person signs the model off before anything ships.
- 07
Release
Weights with checksums and a model card, kept beside every earlier version.
- 08
Deploy
A model swap and engine rebuild take about two and a half minutes. Rollback is one file.
What fixing a domain looks like.
The problem
On footage from a new region, the model in use found 1,072 faces that were not there in one recording: sand, grilles, signs and wheels.
The data
26,695 frames from that region, labelled by teacher models, every face reviewed.
The first run
Rejected at the gates: it lost 61 % of the plates.
The fix
Plates relabelled by the model in use, false detections added as hard negatives four times over.
Where training runs.
Apart from production
Training has its own resources in an EU region. Production only reads released models; nothing is pushed into it directly.
In the EU
Customer footage never touches GitHub. All data stays in the EU.
Every version on record
Which model runs, which ran before, and every rejected run with its reason.
Pinned by checksum
The products that use a model pin it by its checksum.
Also from Marsstein
Anonymization modelAEON
Faces and plates in camera data from vehicles and robots, masked in the EU, with evidence per file.
Learn moreData center
Runs in the Marsstein EU cloud: your footage lands in an EU bucket and is masked inside the EU.
Learn moreOn-premise
Marsstein on your own servers: in your own cloud account or on your own GPU servers.
Learn more