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README.md

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+---
+license: apache-2.0
+tags:
+  - pathology
+  - histopathology
+  - foundation-model
+  - self-supervised
+  - dinov2
+  - vision-transformer
+  - digital-pathology
+library_name: pytorch
+pipeline_tag: image-feature-extraction
+extra_gated_prompt: >-
+  Access is granted for research use. By requesting access you agree to cite the OpenPath paper.
+extra_gated_fields:
+  Name: text
+  Affiliation: text
+  Intended use: text
+---
+
+# OpenPath — Checkpoints
+
+Teacher checkpoints of **[OpenPath](https://huggingface.co/taejoon89/openpath)**, a **ViT-g/14**
+pathology foundation model pre-trained with self-supervision (**DINOv2** + gram anchoring) on
+**public-only** whole-slide histopathology tiles ([OpenPath corpus](https://huggingface.co/datasets/taejoon89/openpath-corpus)).
+
+> **Headline result.** On **AMC-HCC-ST** — a contamination-free in-house Asan Medical Center
+> hepatocellular-carcinoma spatial-transcriptomics cohort, the least leakage-prone benchmark since no
+> public foundation model was trained on it — OpenPath **ranks #1 among seven foundation models**.
+
+## Checkpoints
+
+- **61 teacher checkpoints**: `training_0` … `training_345000`, every **5,750 iters** (≈ 1 native epoch total).
+- Each is `training_<iter>/teacher_checkpoint.pth` (ViT-g/14 reg4, 1536-dim CLS embedding).
+- **Released model = `training_316250`** — selected by the clean AMC-HCC-ST benchmark. (OpenPath's
+  HEST-1K peaks earlier, ~`training_23000` at ~0.38; pick a checkpoint to match your task.)
+
+## Load & extract embeddings
+
+Requires the OpenPath / DINOv2 code (`taejoon89/openpath`).
+
+```python
+import torch, dinov2.models.vision_transformer as vits
+ck = torch.load("training_316250/teacher_checkpoint.pth", map_location="cpu", weights_only=False)
+sd = {k[len("backbone."):]: v for k, v in ck["teacher"].items() if k.startswith("backbone.")}
+m = vits.vit_giant2(patch_size=14, img_size=224, block_chunks=4, num_register_tokens=4,
+                    ffn_layer="swiglufused", init_values=1e-5,
+                    interpolate_antialias=True, interpolate_offset=0.0)
+m.load_state_dict(sd, strict=True); m.eval()
+cls = m.forward_features(x)["x_norm_clstoken"]   # (B, 1536), ImageNet-normalized 224x224 input
+```
+
+## Evaluation
+
+Frozen-encoder linear/ridge probing, all models under one protocol (sorted by the clean AMC-HCC-ST
+cohort). AMC-HCC-ST is our headline because public benchmarks (HEST-1K, CRC, BACH) derive from
+repositories these FMs were pre-trained on and are confounded by **train/test leakage**.
+
+| Model | AMC-HCC-ST (clean) ↓ | HEST-1K (public) | NCT-CRC-HE (9-cls acc) | BACH (4-cls acc) |
+|---|---|---|---|---|
+| **OpenPath (`training_316250`)** | **0.323** | 0.372 | 0.954 | 0.761 |
+| UNI2-h | 0.301 | 0.414 | 0.966 | 0.908 |
+| OpenMidnight | 0.300 | 0.390 | 0.967 | 0.906 |
+| Virchow2 | 0.292 | 0.398 | 0.964 | 0.875 |
+| prov-gigapath | 0.286 | 0.393 | 0.953 | 0.752 |
+| Phikon-v2 | 0.274 | 0.375 | 0.937 | 0.708 |
+| UNI | 0.257 | 0.386 | 0.946 | 0.777 |
+
+## Intended use & limitations
+
+Frozen feature extractor for **H&E** histopathology tiles (native ~40× / 0.5 µm-per-pixel, ImageNet
+normalization) → 1536-dim CLS embedding for linear/ridge probing, k-NN, MIL, retrieval. **Not a
+medical device**; not for diagnosis. Public-benchmark numbers are leakage-confounded; it is a
+patch-level encoder (slide-level context needs a separate aggregator). See the
+[model / code card](https://huggingface.co/taejoon89/openpath) for details.
+
+## Related artifacts
+
+| Artifact | Hugging Face repo | Notes |
+|---|---|---|
+| **Corpus** | `taejoon89/openpath-corpus` | Native 40× pathology tiles, 33,991 WebDataset shards / ~17 TB |
+| **Checkpoints** | `taejoon89/openpath-checkpoints` | This repository |
+| **Code** | `taejoon89/openpath` | training & evaluation code (also on [GitHub](https://github.com/taejoon89/openpath)) |
+
+## Citation
+
+```bibtex
+@misc{openpath2026,
+  title  = {OpenPath: Public-Data Pathology Foundation Models and Leakage-Free Evaluation},
+  author = {OpenPath authors},
+  year   = {2026},
+  note   = {https://huggingface.co/taejoon89/openpath}
+}
+```
+
+## Acknowledgements
+
+This research was supported by a grant of the Korea Health Technology R&D Project through the Korea
+Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of
+Korea (grant number: HR21C0198); the Advanced GPU Utilization Support Program funded by the Government
+of the Republic of Korea, Ministry of Science and ICT; and the National Research Foundation of Korea
+(NRF) grant funded by the Korean government (MSIT) (grant number: RS-2026-25522634).
+
+## License
+
+**Weights — Apache-2.0** (warm-started from Meta DINOv2 ViT-g/14-reg, itself Apache-2.0). Training
+data: public pathology datasets under CC-BY / CC0 / NIH-open terms.