| 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. | |