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siglip-lora-osteosarcoma-pkg-hpo-best
lfniederauer/siglip-lora-osteosarcoma-pkg-hpo-best
siglip-lora-osteosarcoma-pkg-hpo-best is a machine learning model from lfniederauer. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for jax. The card lists the license as apache-2.0.
Private Optuna HPO on the decontaminated PKG clinic (clientpkg), initialized from basev2. This is the client update that is later FedAvg-merged into basev3.
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At a glance
- Library
- jax
- License
- apache-2.0
- Model type
- siglip_lora_classifier
- Access
- Public
- Created
- Jul 13, 2026
- Updated
- Jul 13, 2026
- SHA
- 352d439b
Model card
From the Hugging Face model README
SigLIP-LoRA Osteosarcoma — pkg_hpo_best (PKG client)
Private Optuna HPO on the decontaminated PKG clinic (client_pkg), initialized from base_v2. This is the client update that is later FedAvg-merged into base_v3.
Only LoRA adapters + head leave the site. Raw PKG images never leave the client in the federated story.
Model family
| Checkpoint | Role | Hugging Face |
|---|---|---|
| base_v2 | Roots single-site HPO best (init) | siglip-lora-osteosarcoma-base-v2 |
| pkg_hpo_best (this repo) | PKG private Optuna update | siglip-lora-osteosarcoma-pkg-hpo-best |
| base_v3 | Federated merge (roots + PKG) | siglip-lora-osteosarcoma-base-v3 |
| fedavg-v1 | Historical FL round (superseded by base_v3) | siglip-lora-osteosarcoma-fedavg-v1 |
Results
| Eval | Metric | Value |
|---|---|---|
| PKG local test (training_metadata) | Accuracy | 94.89% |
| Roots clean holdout (410, transfer) | Accuracy | ~90.2% |
| Roots clean holdout | Tumor sensitivity | 0.982 |
Analysis notebooks: notebooks/pkg_hpo_best_qualitative_research.ipynb, notebooks/base_client_pkg_qualitative_research.ipynb.
Model description
Same architecture as base_v2: frozen SigLIP-SO400M + LoRA (r=8, α=16) on attention Q/V + linear head. Not a transformers checkpoint.
| Property | Value |
|---|---|
| Framework | JAX / Flax |
| Base model | google/siglip-so400m-patch14-384 |
| Init checkpoint | checkpoints/base_v2 |
| Client | data/client_pkg (contamination-filtered vs roots test) |
| n_train (FedAvg weight) | 544 |
| Image size / norm | 384 / SigLIP |
| PKG test accuracy (metadata) | 94.89% |
Classes
| Label | ID |
|---|---|
| Non-Tumor | 0 |
| Non-Viable-Tumor | 1 |
| Viable | 2 |
Training (PKG private Optuna)
- Init from roots base_v2
- Selected:
lr≈5.63e-4,epochs=7,color_jitter≈0.019,max_shift=0,scale [0.752, 1.213],hflip=True - Fixed: batch=1 + grad accum 4, LoRA r=8/a=16, image=384
- Seed: 1122
make pkg-hpo-best
# or: make pkg-hpo-from-roots
Deploy / usage
git clone https://github.com/lfniederauer/FederatedLoRA-OsteosarcomaClassification
cd FederatedLoRA-OsteosarcomaClassification
pip install -r requirements.txt
export BIG_VISION_ROOT=~/git/big_vision
python scripts/infer.py --checkpoint checkpoints/pkg_hpo_best --image patch.jpg
python scripts/infer.py --hf-repo lfniederauer/siglip-lora-osteosarcoma-pkg-hpo-best --image patch.jpg
Publish:
make sync-hf-assets
make publish-hf-pkg
Merge into the global federated checkpoint:
make merge CHECKPOINT_A=checkpoints/base_v2 CHECKPOINT_B=checkpoints/pkg_hpo_best OUT=checkpoints/base_v3
# published as lfniederauer/siglip-lora-osteosarcoma-base-v3
Files in this repo
| File | Description |
|---|---|
orbax_checkpoint/ | Trainable weights (LoRA + head) |
config.json | Model + HPO provenance |
preprocessor_config.json | Size + norm |
training_metadata.json | Hyperparams + test_accuracy |
assets/ | Family figures (UMAP, data hygiene) |
Datasets
| ID | Reference |
|---|---|
| PKG (clean) | TCIA Osteosarcoma Tumor Assessment, ingested as client_pkg with near-duplicates vs sacred roots test removed (h≤5) |
| DS_PRIMARY | tatsuyaryu/OsteosarcomaHistopathologyClassification (roots / init) |
Limitations
- Research use only.
- PKG local accuracy is on the client's own held-out split; roots transfer metrics are for safety auditing, not cherry-picking.
- Requires JAX +
big_vision.
Citation
@misc{siglip_lora_osteosarcoma_pkg_hpo_best,
title={SigLIP-LoRA PKG Client HPO Update for Osteosarcoma Histopathology},
author={lfniederauer},
year={2026},
howpublished={\url{https://huggingface.co/lfniederauer/siglip-lora-osteosarcoma-pkg-hpo-best}}
}
@dataset{leavey2019osteosarcoma_tcia,
author = {Leavey, Patrick and Sengupta, Aniruddha and Rakheja, Dinesh and Daescu, Ovidiu and Arunachalam, Harish Babu and Mishra, Rashika},
title = {Osteosarcoma data from {UT Southwestern}/{UT Dallas} for Viable and Necrotic Tumor Assessment ({Osteosarcoma-Tumor-Assessment})},
year = {2019},
publisher = {The Cancer Imaging Archive},
doi = {10.7937/tcia.2019.bvhjhdas},
url = {https://www.cancerimagingarchive.net/collection/osteosarcoma-tumor-assessment/}
}
- Library
- jax
- Type
- siglip_lora_classifier
- License
- apache-2.0
- Created
- Jul 13, 2026
- Updated
- Jul 13, 2026