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58 lines
1.5 KiB
Markdown
58 lines
1.5 KiB
Markdown
# Inference config reference
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`InferenceConfig` controls how files are clipped and batched during
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prediction-time workflows.
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Defined in `batdetect2.inference.config`.
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## Top-level fields
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- `compile_model`
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- compile the detector before batch prediction. This is off by default.
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- `loader`
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- data-loader settings for inference.
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- `clipping`
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- controls how recordings are split into clips before batching.
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## `loader`
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Current built-in loader field:
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- `batch_size` (int, default `8`)
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## `clipping`
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Fields:
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- `enabled` (bool)
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- `duration` (float, seconds)
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- `overlap` (float, seconds)
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- `max_empty` (float)
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- `discard_empty` (bool)
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## When to override this config
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Override `InferenceConfig` when:
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- long recordings need different clipping behavior,
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- you want to tune batch size for your hardware,
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- you want to opt into runtime model compilation for repeated predictions,
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- you need reproducible prediction settings across runs.
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## Runtime compilation
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Set `compile_model: true` to compile the detector before batch inference. This
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can help when you run repeated predictions with stable input shapes. For a
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single short run, the compile step can cost more time than it saves.
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In Python, you can also compile explicitly with `BatDetect2API.compile()` or by
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passing `compile_model=True` to `BatDetect2API.from_checkpoint(...)` or
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`BatDetect2API.from_config(...)`.
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## Related pages
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- Tune inference clipping:
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{doc}`../../../how_to/inference/tune-inference-clipping`
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- Predict CLI reference:
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{doc}`../../cli/predict`
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