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37 lines
1.4 KiB
Markdown
37 lines
1.4 KiB
Markdown
# Extracted features and embeddings
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The current API exposes a per-detection `features` vector.
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Older BatDetect2 workflows also exposed concepts such as `cnn_feats`, `spec_features`, and `spec_slices`.
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## What the current feature vector is
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In the current stack, each retained detection can carry an internal feature representation produced by the model output pipeline.
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This is useful for downstream exploration, comparison, and custom analysis.
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## What these features are not
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They are not automatically human-interpretable ecological variables.
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They are also not a substitute for careful validation.
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## Why people refer to them as embeddings
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In practice, users often treat these feature vectors as embeddings because they can be used as dense learned representations of detections.
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That usage is reasonable, but you should still treat them as model-derived internal representations whose meaning depends on the training setup.
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## Legacy terminology versus current terminology
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- legacy `cnn_feats` referred to CNN feature outputs in the older workflow,
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- legacy `spec_features` referred to lower-level extracted call features,
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- current `features` are the per-detection vectors attached to `Detection` objects.
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These are related ideas, but not necessarily one-to-one replacements.
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## Related pages
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- Inspect detection features in Python: {doc}`../how_to/inspect-detection-features-in-python`
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- Legacy feature extraction: {doc}`../legacy/feature-extraction`
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