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albtad01/STHELAR-Adapt-CellViT-SAM-H-x40
STHELAR-Adapt-CellViT-SAM-H-x40 is a image segmentation model from albtad01. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as other.
This repository contains verified, architecture-specific STHELAR-Adapt states for the CellViT-SAM-H-x40 backbone. STHELAR-Adapt specializes CellViT for nuclei instance segmentation and spatial-transcriptomics-informed…
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From the Hugging Face model README
This repository contains verified, architecture-specific STHELAR-Adapt states for the CellViT-SAM-H-x40 backbone. STHELAR-Adapt specializes CellViT for nuclei instance segmentation and spatial-transcriptomics-informed cell typing in STHELAR 40× H&E images.
The official CellViT-SAM-H-x40 base checkpoint is required separately. It is not redistributed here.

Q/V LoRA and AdaptFormer specialize the pretrained encoder while the decoder body remains frozen and only the final nucleus heads are trained.
The adapter state uses the selected MSc-thesis PEFT configuration:
The state has 1.1176% trainable parameters relative to the instantiated CellViT-SAM-H-x40 PEFT model. This is a custom composite adapter format, not a drop-in standard PEFT/LoRA package.
STHELAR-derived foreground identities are grouped into five classes: Immune, Stromal, Epithelial, Melanocyte, and Other. Background is retained as the non-foreground class.
Obtain CellViT-SAM-H-x40.pth from the official
CellViT project. The expected SHA-256 is:
b324c10fddb0f80f5ab03a0459453a4c4848866934daf63435b46749a6b278cf
The official base weights, FullFT weights, training checkpoints, optimizer state, datasets, and run logs are not part of this release.
The manifest contains 32 unique verified payloads:
| Cohort | Count | Scientific role |
|---|---|---|
| Complete-slide evaluation | 20 | KLT plus nine tissues, reciprocal Fold A/B, seed 42 |
| All-slides deployment | 10 | KLT plus all nine tissues, seed 42 |
| Supplemental verified KLT | 2 | Fold A/B seed-43 evaluation states |
Evaluation adapters were trained without the reciprocal held-out complete slide. All-slides adapters use all designated slides with the established within-slide training/validation construction; they are deployment states and carry no held-out-slide performance claim. Supplemental states are kept separate from the seed-42 core.
The KLT headline aggregate uses all three training seeds. Two seed-44 SAM-H states remain excluded from this publication set because their historical source-config hash provenance is unresolved, even though their result rows remain part of the audited thesis aggregate.
Directory conventions:
adapters/complete_slide_v1/klt/{fold_a,fold_b}/seed42/
adapters/complete_slide_v1/klt/all_slides/seed42/
adapters/complete_slide_v1/tissue_specific/<tissue>/{fold_a,fold_b}/seed42/
adapters/complete_slide_v1/tissue_specific/<tissue>/all_slides/seed42/
adapters/supplemental_klt_replicates/{fold_a,fold_b}/seed43/
Each directory contains the adapter safetensors plus adapter_config.json,
checksums.json, and verification.json. The corresponding training config
is recorded in adapter_manifest.csv and included under configs/.

One official architecture-matched base can be combined with one verified compact state at a time. SAM-H and CellViT-256 states are not interchangeable.
The final predictive evaluation uses reciprocal complete-slide holdout. KLT values are equal-weight reciprocal-fold means after averaging each fold over seeds 42, 43, and 44. Nine-tissue values use seed 42 and equal weighting over the nine tissue-level reciprocal-fold means.
| Evaluation | Method | bPQ | mPQ | F1 detection | F1 type |
|---|---|---|---|---|---|
| KLT complete-slide | PEFT | 0.564 | 0.228 | 0.853 | 0.492 |
| KLT complete-slide | FullFT | 0.564 | 0.214 | 0.848 | 0.508 |
| Nine-tissue complete-slide | PEFT | 0.442 | 0.175 | 0.808 | 0.381 |
| Nine-tissue complete-slide | FullFT | 0.445 | 0.187 | 0.806 | 0.418 |
Nucleus detection and class-agnostic segmentation are preserved particularly closely; ST-informed typing remains more difficult.

Illustrative SAM-H overlays across the nine tissues, shown as Ground truth, Linear Probing, and the selected PEFT adapter. Colors denote the five grouped cell identities; the selected examples are not a statistically representative sample.
| Method | Trainable | Peak allocated VRAM | Serialized state |
|---|---|---|---|
| PEFT | 1.1176% | 9.362 GiB | 30.369 MiB |
| FullFT | 100% | 15.508 GiB | 2.607 GiB |
For nine architecture-compatible domains, independent FullFT states require 23.463 GiB, compared with 2.874 GiB for one shared base plus nine PEFT states. Parameter efficiency does not imply universal runtime acceleration; timings depend on hardware, data movement, validation, checkpointing, and postprocessing.
Install a compatible checkout of STHELAR-Adapt and its dependencies, then use the repository's verified release loader:
from utils.sthelar_release_loader import load_verified_adapter
model, evidence = load_verified_adapter(
base_checkpoint="/path/to/CellViT-SAM-H-x40.pth",
package_dir=(
"/path/to/STHELAR-Adapt-CellViT-SAM-H-x40/"
"adapters/complete_slide_v1/klt/fold_a/seed42"
),
device="cpu",
)
The loader validates the base hash, payload hash, tensor inventory, expected trainable and mutable-buffer keys, and verification metadata before returning an eval-mode model.
To repeat the package-level checks and deterministic forward sanity test:
PYTHONPATH=/path/to/STHELAR-Adapt \
python scripts/verify_public_release_package.py \
--base-checkpoint /path/to/CellViT-SAM-H-x40.pth \
--package adapters/complete_slide_v1/klt/fold_a/seed42
The original finalization standard required
state_reconstruction: exact_all_tensors and
forward_verification: exact_all_output_tensors. See adapter_manifest.csv
and verification_summary.json for the release-wide inventory.
This release is intended for research, reproducibility, and evaluation of STHELAR-specific CellViT adaptation. It is not clinically validated and must not be used as a medical device.
Prepared STHELAR revision:
e32a8cdd50eff2d38e237f3729e9ac85bbb5203b. The adapter manifest records
payload hashes, roles, configs, and held-out metrics. No unpublished paper
citation is asserted.
See CITATION.cff, LICENSE, and NOTICE. Applicable CellViT/HIPT-derived
components include the upstream Apache 2.0 and Commons Clause conditions
reproduced in LICENSE. Cite the upstream CellViT, STHELAR, Segment Anything,
HIPT, and PanNuke resources as applicable. STHELAR data and the official base
checkpoint retain their own terms and are not redistributed here.