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46 lines
1.4 KiB
Markdown
46 lines
1.4 KiB
Markdown
# Target encoding and decoding
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batdetect2 turns annotated sound events into training targets, then maps model
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outputs back into interpretable predictions.
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## Encoding path (annotations -> model targets)
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At training time, the target system:
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1. checks whether an event belongs to the configured detection target,
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2. assigns a classification label (or none for non-specific class matches),
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3. maps event geometry into position and size targets.
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This behaviour is configured through `TargetConfig`, `TargetClassConfig`, and
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ROI mapper settings.
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## Decoding path (model outputs -> tags and geometry)
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At inference time, class labels and ROI parameters are decoded back into
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annotation tags and geometry.
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This makes outputs interpretable in the same conceptual space as your original
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annotations.
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## Why this matters
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Target definitions are not just metadata.
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They directly shape:
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- what events are treated as positive examples,
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- which class names the model learns,
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- how geometry is represented and reconstructed.
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Small changes here can alter both training outcomes and prediction semantics.
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## Related pages
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- Configure detection target logic:
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{doc}`../how_to/data/configure-target-definitions`
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- Configure class mapping:
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{doc}`../how_to/data/define-target-classes`
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- Configure ROI mapping:
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{doc}`../how_to/data/configure-roi-mapping`
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- Target config reference:
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{doc}`../reference/configs/data/targets-config-workflow`
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