batdetect2/docs/source/reference/configs/inference/inference-config.md

Inference config reference

InferenceConfig controls how files are clipped and batched during prediction-time workflows.

Defined in batdetect2.inference.config.

Top-level fields

• compile_model
  • compile the detector before batch prediction. This is off by default.
• loader
  • data-loader settings for inference.
• clipping
  • controls how recordings are split into clips before batching.

loader

Current built-in loader field:

• batch_size (int, default 8)

clipping

Fields:

• enabled (bool)
• duration (float, seconds)
• overlap (float, seconds)
• max_empty (float)
• discard_empty (bool)

When to override this config

Override InferenceConfig when:

• long recordings need different clipping behavior,
• you want to tune batch size for your hardware,
• you want to opt into runtime model compilation for repeated predictions,
• you need reproducible prediction settings across runs.

Runtime compilation

Set compile_model: true to compile the detector before batch inference. This can help when you run repeated predictions with stable input shapes. For a single short run, the compile step can cost more time than it saves.

In Python, you can also compile explicitly with BatDetect2API.compile() or by passing compile_model=True to BatDetect2API.from_checkpoint(...) or BatDetect2API.from_config(...).

Related pages

• Tune inference clipping: {doc}../../../how_to/inference/tune-inference-clipping
• Predict CLI reference: {doc}../../cli/predict