README.md

---
license: cc-by-4.0
library_name: scitomo
tags: [cryo-electron-tomography, deepdewedge, safetensors, scitomo, format-2]
---

# DeepDeWedge tutorial checkpoint — fresh Scitomo FORMAT 2 package

This is a fresh FORMAT 2 export from the authoritative original Lightning
checkpoint, not a migration of any earlier Hugging Face package. Normal runtime
uses Scitomo's generic FORMAT 2 loader and Safetensors only; it does not require
PyTorch Lightning or the upstream DeepDeWedge source checkout.

## Package identity

- package id: `deepdewedge_tutorial`; package revision: `3`
- learned-checkpoint format: `2`; manifest schema: `4`
- Scitomo conversion checkout: `2832957f69daff0d7baec5df17a7c54954623eed`
- minimum Scitomo version: `0.7.3`
- previous Hugging Face commit: `87db06570dd874a99af1289e62b79ea99f87f006` — **HISTORICAL ONLY; NOT CONVERSION INPUT**

## Authoritative provenance

- upstream repository: <https://github.com/MLI-lab/DeepDeWedge>
- upstream revision: `072075692a44a8f17394214369e6e762abe52bc3`
- Figshare DOI: <https://doi.org/10.6084/m9.figshare.25043435.v1>; file id: `45582309`
- original archive SHA-256: `7c871342e51f5a66a773fe427d72944b5d2cc8ff41c5b7415ab38dbfc9ac6d58`
- original checkpoint member: `tutorial_data/fitted_model.ckpt`
- original checkpoint size: `327952642` bytes
- original checkpoint SHA-256: `5262f6c11e85fd662b02e59efe936fa7b69913758e841235be2683f7bd03ec76`

DeepDeWedge Tutorial Data is attributed to Simon Wiedemann and is distributed
under CC BY 4.0. The pinned DeepDeWedge implementation is BSD-2-Clause; its
license text is included below `LICENSES/`. See `ATTRIBUTION.md`.

## Scientific inference semantics

The pure persisted Network owns only the lowered U-Net architecture and its 54
canonical tensors. The fitted affine values remain outside Network state in the
typed `deepdewedge_inference` profile:

- `network_affine_loc`: `-0.14898751676082611`
- `network_affine_scale`: `1.3237642049789429`
- input layout: `(..., Z, Y, X)`; Network layout: `(..., C, Z, Y, X)`
- paired halves are refined independently then averaged; full-width missing wedge: 50 degrees
- 96³ patches, 32³ overlap, trailing-reflection coverage, linear-ramp reassembly
- preconditioning recomputes patch statistics; output uses the checkpoint-fitted affine

## Fresh conversion and validation

`refresh_format2.py` is the exact one-off implementation and records
the verified source, explicit 54-tensor mapping, strict Network lowering, and
generic export. It was run with Python `3.12.13`, Torch
`2.12.1+cpu`, Lightning `2.6.5`, Safetensors
`0.8.0`, and Scitomo `0.7.3.dev0` on `Windows-11-10.0.22631-SP0`.

The generic exporter freshly serializes `weights.safetensors`; no previous
Hugging Face Safetensors, manifest, construction, or inference record is read.
The conversion record lists every source checkpoint tensor to canonical target
mapping. The validation record binds package state closure, generic loader
reload, external-affine semantics, and deterministic forward parity.

For a deterministic directional, non-symmetric CPU float32 input of 4,096 elements,
authoritative upstream output versus FORMAT 2 pure-Network-plus-profile output
passed `rtol=1e-5`, `atol=1e-6`: maximum absolute error
`0`, relative L2 error `0`.

## Files and closure

`manifest.json` is the authoritative, closed inventory of every package file,
with each fresh size and SHA-256. It declares only FORMAT 2 construction,
inference, Safetensors, conversion, validation, and documentation/license
resources; there is no format-1 or migration artifact. Validate and load with:

```python
import scitomo as st
loaded = st.api.load_learned_network("/path/to/package")
```

This operation uses the generic Scitomo FORMAT 2 loader and does not import
Lightning or DeepDeWedge. It is a checkpoint package, not a claim of scientific
approval for a new dataset or acquisition protocol.