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49 lines
1.4 KiB
Markdown
49 lines
1.4 KiB
Markdown
# How to tune detection threshold
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Use this guide to compare detection outputs at different threshold values.
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The goal is not to find a universal threshold.
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The goal is to choose a threshold that fits your reviewed local data and the project trade-off between missed calls and false positives.
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## 1) Start with a baseline run
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Run an initial prediction workflow and keep outputs in a dedicated folder.
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## 2) Sweep threshold values
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Run `predict` multiple times with different thresholds (for example `0.1`,
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`0.3`, `0.5`) and compare output counts and quality on the same validation
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subset.
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```bash
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batdetect2 predict directory \
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path/to/model.ckpt \
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path/to/audio_dir \
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path/to/outputs_thr_03 \
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--detection-threshold 0.3
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```
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Keep each threshold run in a separate output directory.
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That makes it easier to compare counts and inspect example files without mixing results.
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## 3) Validate against known calls
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Use files with trusted annotations or expert review to select a threshold that
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fits your project goals.
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Check both:
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- obvious false positives,
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- obvious missed calls.
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If class interpretation matters downstream, inspect class ranking behavior as well, not just detection counts.
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## 4) Record your chosen setting
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Write down the chosen threshold and rationale so analyses are reproducible.
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For conceptual trade-offs, see
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{doc}`../explanation/model-output-and-validation`.
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