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Clarify ONNX export steps in README #8

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Clarify ONNX export steps in README
cangyeone Dec 17, 2025
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Merge branch 'main' into codex/update-readme-documentation-leyuox
cangyeone Dec 17, 2025
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9 changes: 8 additions & 1 deletion README.md
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Expand Up @@ -144,7 +144,14 @@ selidx = torch.masked_select(selidx, torch.abs(ref - ntime) > 1000) # NMS windo
* `1000` samples (10 seconds at 100 Hz) enforce a single pick per class within that window. Reduce the window if multiple phases are expected in short succession.

#### 3.2 Building `.onnx` pickers
Use the companion `makeonnx.XXX.py` scripts to export ONNX versions of each network. **The onnx model can use config/picker.py for post-processing as it is outside of the model itself**
All ONNX pickers share the `OnnxSlidingWindowPicker` interface defined in `onnx_picker_base.py`. To regenerate the exported ONNX
files:
1. Run the corresponding script (for example `python makeonnx.unet.py`, `python makeonnx.unetpp.py`, `python makeonnx.rnn.py`,
`python makeonnx.pnsn.py`, or `python makeonnx.eqt.py`).
2. Each script builds the model (`self.model = UNet()`/`BRNN()`/`EQTransformer()`, etc.), loads checkpoints (auto-prefixing with
`model.` when needed), and wraps it with the shared sliding-window preprocessing.
3. Post-processing (probability threshold and NMS) remains outside the ONNX graph; reuse `config/picker.py` together with the
`post` helpers in `picker.onnx.py` or `picker.py` when running inference.

#### 3.2 Building `.onnx` pickers
Use the companion `makeonnx.XXX.py` scripts to export ONNX versions of each network. **The onnx model can use config/picker.py for post-processing as it is outside of the model itself**
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