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Cap regional failure oversampling
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Jens
2026-07-29 17:51:32 +02:00
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docs/BELGIUM_BUILDING_TRAINING_LOOP.md
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@@ -108,6 +108,11 @@ modes without copying a protected AOI into training. The sampling evidence
records both context sets and repeat factors. When no matching train context
exists, regional sampling remains active and the missing context becomes a
concrete input for the next immutable corpus expansion.
Failure weighting may not let one region exceed 65% of the sampled entries.
The deterministic cap removes only repeated entries and retains every unique
train tile at least once; manifests record pre-cap counts, final counts and the
number of dropped repeats. This keeps a weak region prominent without turning
the national detector into a single-region expert.
The checkpointed orchestrator invokes this builder after every rejected
iteration, stores its checksum in `training-loop-state.json`, and uses the
resulting dataset YAML for the next checkpoint. A restart resumes both the