Extracted features and embeddings

The current API exposes a per-detection features vector.

Older BatDetect2 workflows also exposed concepts such as cnn_feats, spec_features, and spec_slices.

What the current feature vector is

In the current stack, each retained detection can carry an internal feature representation produced by the model output pipeline.

This is useful for downstream exploration, comparison, and custom analysis.

They are not automatically human-interpretable ecological variables.

They are also not a substitute for careful validation.

In practice, users often treat these feature vectors as embeddings because they can be used as dense learned representations of detections.

That usage is reasonable, but you should still treat them as model-derived internal representations whose meaning depends on the training setup.

legacy cnn_feats referred to CNN feature outputs in the older workflow,
legacy spec_features referred to lower-level extracted call features,
current features are the per-detection vectors attached to Detection objects.

These are related ideas, but not necessarily one-to-one replacements.

Inspect detection features in Python: {doc}../how_to/inspect-detection-features-in-python
Legacy migration guide: {doc}../legacy/migration-guide