resnet50_7.9um_scroll1_frags (repaired)

Code is also mirrored on GitHub: https://github.com/Nieuwlaar/resnet50-scroll1-checkpoint-repair

Verify the defect yourself in 60 seconds

file:   stock/model.safetensors
size:   342,867,728 bytes
sha256: fa00728db74d714769960dd77e9911a0e0938b3f272933c153632b5bed543c34

decoder conv weights vs PyTorch default init U(-b, +b), b = 1/sqrt(fan_in)
tensor                        absmax/b  mean|w|/(b/2)  std/(b/sqrt(3))
decoder.convs.0.0.weight        1.0000         1.0004           1.0002
decoder.convs.1.0.weight        1.0000         1.0001           1.0000
decoder.convs.2.0.weight        1.0000         0.9999           0.9999
decoder.logit.weight            0.9835         0.9990           0.9898
backbone.conv1.weight          48.2277   (trained contrast)

BatchNorm num_batches_tracked (training batches seen):
  decoder.convs.0.1.num_batches_tracked        0
  decoder.convs.1.1.num_batches_tracked        0
  decoder.convs.2.1.num_batches_tracked        0
  backbone (53 layers)                        2,234,791

VERDICT: decoder head is at PyTorch default init (UNTRAINED).
Full-model ink predictions from this file will be chance-level.

The defect in the original checkpoint

1. Weight statistics. Every decoder conv sits exactly on its init bound, with mean and spread matching the untrained uniform distribution to 3–4 significant digits (table above). The backbone's first conv, by contrast, is ~48x its bound. That is what trained weights normally look like.
2. Batch-norm counters. All three decoder batch-norms read num_batches_tracked = 0 with pristine default statistics (mean 0, variance 1); every backbone batch-norm reads 2,234,791. A supporting oddity: config.json declares n_classes=1139, and the 1139-way classification head from pretraining is still present in the file as backbone.fc, trained (absmax ~14x its init bound) but unused by the ink pipeline. That fits a checkpoint exported from a pretraining setup, with the segmentation decoder that replaced that head left at default init.
3. It doesn't detect ink. On a Scroll 1 surface crop that is 26% labeled ink, the model card's own inference settings give output confined to ~0.50 ± 0.03 and ROC-AUC 0.504. That is chance level. The same crop through the same code scores 0.89 with the 2023 Grand Prize TimeSformer checkpoint, so the data pipeline and labels are sound; the failure is specific to this checkpoint's decoder.

Corpus audit: a second broken release, and the rest verify clean

• resnet50_3um_01122024/model.safetensors is broken in exactly the same way: a different, independently trained backbone (batch-norm counters 2,231,011 vs 2,234,791; different weights) with its own never-trained decoder: all decoder convs on the untrained-init bound, decoder batch-norm counters 0. The unmodified verify_checkpoint.py detects it and exits 1.
• The other 17 files verify clean, including all 14 ink_9um checkpoints (head-group tensors 3.7–5.5x off their init bounds, the ink task head itself 6.1x or more, and the head weights move smoothly between released training steps, which is what an actively trained head looks like) and the TimeSformer-family files (ink_detection_pipeline is byte-identical to timesformer_GP_scroll1).

Using the repaired checkpoint

Training recipe

1. Decoder only: decoder re-initialized (seeded), backbone frozen with its batch-norm statistics kept intact, 5 epochs at lr 1e-3. This alone reaches tile AUC ≈ 0.926 on the validation segment, confirming the backbone features were good all along and only the head was missing.
2. Light full fine-tune: whole model, 4 epochs at lr 1e-5. Best checkpoint: epoch 3, validation tile AUC 0.9314.

Repository contents

• model.safetensors: repaired weights, drop-in replacement (same key layout as the original checkpoint); sha256 6133ef74905d32a40d0e053d9378bf76207c29614cb77210218f1df898b8d3cf
• verify_checkpoint.py: the 60-second CPU diagnosis script
• scripts/: train_decoder.py, prep_data.py, eval_panel.py, common.py, the exact training and evaluation code, segment lists, and seed
• eval/: before/after panels and per-segment AUC JSONs for both held-out segments
• training/: metrics.json (full training history), best_info.json (selected checkpoint, seed, segment lists)
• audit/: corpus audit of the public scrollprize checkpoints, with CORPUS_AUDIT.md (findings), results.csv (per-file statistics), audit_checkpoint.py (the verifier generalized to the other architectures)

Relation to the newer ~9 µm models

Limitations

• Trained and evaluated only on Scroll 1 (PHerc. Paris 4) surface volumes at the native ~7.9 µm, 54 keV scan. Not validated on other scrolls, resolutions, or scan energies.
• The AUC is measured against the Grand Prize ink labels, which are themselves imperfect; it is agreement with those labels, not with a human reading. These are the GP-era labels shipped with the official training code; the team's newer curated ink-labels dataset (2026-07) refines labels on updated segmentations and was not used here.
• 20231210121321 was used to choose the best epoch, and the stock model card lists it among the backbone's pretraining fragments. Both are reasons 20230827161847 is the cleaner held-out number.
• The light fine-tune means the backbone is no longer bit-identical to the released one; if the team publishes the originally intended trained decoder, that should supersede this repair.

License