---
tags:
  - lithic-scar-segmentation
  - onnx
  - neurolithic
library_name: onnx
---

# neurolithic/best

ONNX export of the neurolithic lithic-scar segmentation model
(UNet++ / EfficientNet-B5, 6-channel input, soft-edge output).

Used directly in-browser by the `lithicjs` web app (onnxruntime-web). The model is the
2D segmentation network applied to 6 orthographic renders of a
PCA-aligned mesh at 512x512; per-view predictions are back-projected
and merged on the mesh client-side.

**Source checkpoint:** `learning_curve_100pct_20260305_121700_best.ckpt`

**Files:**
- `model_fp32.onnx`
- `model_fp16.onnx`
- `config.json` — input/inference metadata (channels, resolution, views, etc.)

The exported graph bakes in the per-channel input/output normalization (the checkpoint's
real transform stats), so it matches the PyTorch `predict` path exactly. fp32 is exact;
fp16 is ~half the size with a small accuracy trade-off.