Publish README.md from learning_curve_100pct_20260625_183546_best.ckpt

README.md

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+---
+tags:
+  - lithic-scar-segmentation
+  - onnx
+  - neurolithic
+library_name: onnx
+---
+
+# neurolithic/unet_v2
+
+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_20260625_183546_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.