ink9um-dense

9 um ink checkpoints trained with dense pseudo-labels

Four checkpoints in the same format as scrollprize/ink_9um (model / config / step, 138 MB each), so they load with the released inference CLI unchanged:

python -m koine_machines.inference.infer <segment_9um_iso.zarr> \
  dense9um-w016excluded-step075000.pth out.tif \
  --overlap 0.5 --blend-mode hann --batch-size 8

Results

Balanced accuracy at threshold 0.5 / AUC, on the three _validation_mask.zarr regions that ship with ink_9um, which are the only held-out ground truth in the release. Scored with the team's own BalancedAccuracy, masked.

Dense supervision beats the matched control on all three regions, by +0.077, +0.100 and +0.026. It exceeds the best released checkpoint on pherc0814-46527 by +0.041 and does not on the other two regions.

The larger effect is on overfitting. Supervised score minus held-out score falls from 0.21 to 0.29 for the control and the released checkpoints, to 0.007 to 0.099 for the dense models.

Read this before comparing against "the released model"

The 14 released checkpoints vary by up to 0.14 in held-out balanced accuracy depending on which one you pick, and seed42/step-075000, the most natural default, ranks last of 14 on w016. seed43 checkpoints are systematically better on two of three regions. Any comparison needs to name the checkpoint. Full per-region table for all 14 is in the repo.

Same-step seed noise is 0.061 to 0.130 on w016, 0.013 to 0.021 on 0814, and 0.066 to 0.072 on w029. Only the pherc0814 improvement above clearly exceeds it.

Balanced accuracy disagreed with the picture

On w016 the all-7 model scores +0.029 above the w016-excluded model and renders the same held-out letters as merged blobs, while AUC ranks them the other way round. The two runs differ only in one segment's labels. The checkpoint recommended above is the legible one, not the higher-scoring one. Figures showing this are in the repo.

Training

Unmodified aligned21_hybrid_3d2d.json from villa except out_dir and datasets: seed 42, 78,125 iterations, batch 64, SGD lr 0.01, fp16, robust_mad normalization, bce_label_smoothing 0.5, 17-of-21 jittered z window, fixed_scroll_prior quotas 0139:29 1667:22 Paris4:11 0814:2. Full 29-representation corpus. 9.5 hours per run on one RTX 5070 Ti at 2.3 it/s. Labels: domenicor046/ink9um-dense-labels. Code and full results: https://github.com/DomRusso2/ink9um-dense.

Provenance and licence

Scripts and documentation are MIT. The checkpoints are derived from Vesuvius Challenge data and models (scrollprize/ink_9um, scrollprize/ink_canonical_2um, the open-data S3 bucket) and remain subject to the terms of those sources.