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97 lines
3.1 KiB
Markdown
97 lines
3.1 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.
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If you only want to run the model on your own recordings, you can use the CLI
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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.
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Output files are plain formats (for example CSV/JSON), so you can read and
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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:
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[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.
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The best next step is to validate on reviewed local data and then
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fine-tune/train on 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.
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Threshold tuning and training with local annotations can improve results.
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See {doc}`how_to/inference/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.
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If you have high-quality annotated examples, they are valuable for improving
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models.
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### Calls in the same sequence are predicted as different species
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Currently we do not do any sophisticated post processing on the results output
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by the model.
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We return a probability associated with each species for each call.
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You can use these predictions to clean up the noisy predictions for sequences of
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calls.
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### Can I trust model outputs for biodiversity conclusions?
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The models developed and shared as part of this repository should be used with
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caution.
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While they have been evaluated on held out audio data, great care should be
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taken when using the model outputs for any form of biodiversity assessment.
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Your data may differ, and as a result it is very strongly recommended that you
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validate the model first using data with known species to ensure that the
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outputs can be trusted.
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### The pipeline is slow
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Runtime depends on hardware and recording duration.
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GPU inference is often much faster than CPU.
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## Training and scope
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### Can I train on my own species set?
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Yes.
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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.
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The workflow assumes audio can be converted to spectrograms from the raw
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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.
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Open an issue 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.
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This project is currently for non-commercial use.
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See the repository license for details.
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