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87 lines
2.9 KiB
Markdown
87 lines
2.9 KiB
Markdown
# FAQ
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## Installation and setup
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### Do I need Python knowledge to use batdetect2?
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Not much. If you only want to run the model on your own recordings, you can
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use the CLI and follow the steps in {doc}`getting_started`.
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Some command-line familiarity helps, but you do not need to write Python code
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for standard inference workflows.
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### Are there plans for an R version?
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Not currently. Output files are plain formats (for example CSV/JSON), so you
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can read and analyze them in R or other environments.
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### I cannot get installation working. What should I do?
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First, re-check {doc}`getting_started` and confirm your environment is active.
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If it still fails, open an issue with your OS, install method, and full error
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output: [GitHub Issues](https://github.com/macaodha/batdetect2/issues).
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## Model behavior and performance
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### The model does not perform well on my data
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This usually means your data distribution differs from training data. The best
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next step is to validate on reviewed local data and then fine-tune/train on
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your own annotations if needed.
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### The model confuses insects/noise with bats
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This can happen, especially when recording conditions differ from training
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conditions. Threshold tuning and training with local annotations can improve
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results.
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See {doc}`how_to/tune-detection-threshold`.
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### The model struggles with feeding buzzes or social calls
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This is a known limitation of available training data in some settings. If you
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have high-quality annotated examples, they are valuable for improving models.
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### Calls in the same sequence are predicted as different species
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batdetect2 returns per-call probabilities and does not apply heavy sequence-
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level smoothing by default. You can apply sequence-aware postprocessing in your
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own analysis workflow.
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### Can I trust model outputs for biodiversity conclusions?
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Use caution. Always validate model behavior on local, reviewed data before
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using outputs for ecological inference or biodiversity assessment.
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### The pipeline is slow
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Runtime depends on hardware and recording duration. GPU inference is often much
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faster than CPU. If files are very long, splitting them into shorter clips can
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help throughput.
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If you need a clipping workflow, see the annotation GUI repository:
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[batdetect2_GUI](https://github.com/macaodha/batdetect2_GUI).
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## Training and scope
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### Can I train on my own species set?
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Yes. You can train/fine-tune with your own annotated data and species labels.
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### Does this work on frequency-division or zero-crossing recordings?
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Not directly. The workflow assumes audio can be converted to spectrograms from
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the raw waveform.
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### Can this be used for non-bat bioacoustics (for example insects or birds)?
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Potentially yes, but expect retraining and configuration changes. Open an issue
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if you want guidance for a specific use case.
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## Usage and licensing
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### Can I use this for commercial purposes?
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No. This project is currently for non-commercial use. See the repository
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license for details.
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