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61 lines
1.3 KiB
Markdown
61 lines
1.3 KiB
Markdown
# How to configure ROI mapping
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Use this guide to control how annotation geometry is encoded into training
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targets and decoded back into boxes.
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## 1) Set the default ROI mapper
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The default mapper is `anchor_bbox`.
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```yaml
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roi:
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default:
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name: anchor_bbox
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anchor: bottom-left
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time_scale: 1000.0
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frequency_scale: 0.001163
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```
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## 2) Choose an anchor strategy
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Typical options include `bottom-left` and `center`.
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- `bottom-left` is the current default.
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- `center` can be easier to reason about in some workflows.
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## 3) Set scale factors intentionally
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- `time_scale` controls width scaling.
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- `frequency_scale` controls height scaling.
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Use values that are consistent with your model setup and keep them fixed when
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comparing experiments.
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## 4) (Optional) override ROI mapping for specific classes
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Add class-specific mappers under `roi.overrides`.
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```yaml
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roi:
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default:
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name: anchor_bbox
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anchor: bottom-left
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time_scale: 1000.0
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frequency_scale: 0.001163
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overrides:
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species_x:
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name: anchor_bbox
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anchor: center
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time_scale: 1000.0
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frequency_scale: 0.001163
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```
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## Related pages
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- Target definitions:
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{doc}`configure-target-definitions`
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- Class definitions:
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{doc}`define-target-classes`
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- Target encoding overview:
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{doc}`../../explanation/target-encoding-and-decoding`
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