batdetect2/docs/source/how_to/configure-roi-mapping.md

How to configure ROI mapping

Use this guide to control how annotation geometry is encoded into training targets and decoded back into boxes.

1) Set the default ROI mapper

The default mapper is anchor_bbox.

roi:
  name: anchor_bbox
  anchor: bottom-left
  time_scale: 1000.0
  frequency_scale: 0.001163

2) Choose an anchor strategy

Typical options include bottom-left and center.

• bottom-left is the current default.
• center can be easier to reason about in some workflows.

3) Set scale factors intentionally

• time_scale controls width scaling.
• frequency_scale controls height scaling.

Use values that are consistent with your model setup and keep them fixed when comparing experiments.

4) (Optional) override ROI mapping for specific classes

You can set class-level roi in classification_targets when needed.

classification_targets:
  - name: species_x
    tags:
      - key: class
        value: Species X
    roi:
      name: anchor_bbox
      anchor: center
      time_scale: 1000.0
      frequency_scale: 0.001163

Related pages

• Target definitions: {doc}configure-target-definitions
• Class definitions: {doc}define-target-classes
• Target encoding overview: {doc}../explanation/target-encoding-and-decoding