mirror of
https://github.com/macaodha/batdetect2.git
synced 2026-05-23 06:41:53 +02:00
50 lines
1.4 KiB
Markdown
50 lines
1.4 KiB
Markdown
# How to inspect detection features in Python
|
|
|
|
Use this guide when you want the per-detection feature vectors exposed by the current API.
|
|
|
|
## Get the feature vector for one detection
|
|
|
|
Each detection carries a `features` vector.
|
|
|
|
The API exposes it through `get_detection_features`.
|
|
|
|
```python
|
|
from pathlib import Path
|
|
|
|
from batdetect2.api_v2 import BatDetect2API
|
|
|
|
api = BatDetect2API.from_checkpoint(Path("path/to/model.ckpt"))
|
|
prediction = api.process_file(Path("path/to/audio.wav"))
|
|
|
|
for detection in prediction.detections:
|
|
features = api.get_detection_features(detection)
|
|
print(features.shape)
|
|
```
|
|
|
|
## Use features for exploration, not as ground truth labels
|
|
|
|
These features are internal model representations attached to detections.
|
|
|
|
They can be useful for:
|
|
|
|
- exploratory visualization,
|
|
- downstream clustering,
|
|
- comparison across detections,
|
|
- building extra analysis pipelines.
|
|
|
|
They do not replace validation.
|
|
|
|
They also do not automatically have a one-to-one interpretation as ecological variables.
|
|
|
|
## Save predictions with features included
|
|
|
|
If you need features on disk, use an output format that supports them, such as `raw` or `parquet`, and keep feature inclusion enabled.
|
|
|
|
See {doc}`save-predictions-in-different-output-formats`.
|
|
|
|
## Related pages
|
|
|
|
- Understanding features and embeddings: {doc}`../explanation/extracted-features-and-embeddings`
|
|
- Output formats reference: {doc}`../reference/output-formats`
|
|
- API reference: {doc}`../reference/api`
|