---
pretty_name: RudolfV 2-S
license: other
license_name: other
library_name: transformers
pipeline_tag: image-feature-extraction
tags:
  - pathology
  - histopathology
  - computational-pathology
  - digital-pathology
  - vision-transformer
  - foundation-model
  - self-supervised
  - feature-extraction
  - biology
  - medical
extra_gated_prompt: Please complete the fields below to request access to RudolfV 2-S.
extra_gated_fields:
  I agree to the RudolfV 2-S terms of use: checkbox
  Can Aignostics contact you for feedback on RudolfV 2-S?:
    type: select
    options:
      - label: Yes, I consent to being contacted
        value: 'yes'
      - label: No, I do not wish to be contacted
        value: 'no'
---

**RudolfV 2-S**

RudolfV 2-S is a vision transformer based Foundation Model developed by [Aignostics](https://www.aignostics.com/) 
for computational pathology. It uses a ViT-S/8 backbone and was distilled from 
[RudolfV 2](https://huggingface.co/Aignostics/RudolfV-2), which was pretrained on 300k whole slide 
images (WSIs). 
RudolfV 2-S can serve as a general-purpose tile-level feature extractor for a broad range of downstream 
histopathology tasks, including tissue and tumor classification, grading, biomarker prediction, and 
slide-level modeling via multiple instance learning.

📄 **Paper:** *RudolfV 2: A Family of Robust and Efficient Open-Weights Pathology Foundation
Models* (Milbich, Eulig, Carpen-Amarie, Dippel, Muttenthaler, Tietz et al.).
Refer to the [paper](https://cdn.prod.website-files.com/67adb01f31489469b513304a/6a74e2f9f1d4aef06489a3f1_RudolfV_2_260806.pdf) for training details, full benchmark tables, and evaluation protocols.
<!-- TODO(maintainers): add the arXiv/paper link once available. -->

**Model family** — this repo hosts the smallest and fastest model RudolfV 2-S. The flagship model 
RudolfV 2 and the mid-sized RudolfV 2-B are available as well.

| Model | Backbone | Params | Relative speed |
|---|---|---|---|
| [RudolfV 2](https://huggingface.co/Aignostics/RudolfV-2) | ViT-g | 1.1 B | 1× |
| [RudolfV 2-B](https://huggingface.co/Aignostics/RudolfV-2-B) | ViT-B | 86 M | 2.5× faster |
| **RudolfV 2-S** (this repo) | ViT-S | 22 M | 6.5× faster |

---

## Usage

```bash
pip install torch transformers timm pillow
```

`trust_remote_code=True` is required (custom modeling code), and `timm` is a hard dependency.

```python
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel

REPO = "Aignostics/RudolfV-2-S"

processor = AutoImageProcessor.from_pretrained(REPO)
model = AutoModel.from_pretrained(REPO, trust_remote_code=True).eval()

image = Image.effect_noise((224, 224), 64).convert("RGB")  # replace with a real H&E tile
inputs = processor(images=image, return_tensors="pt")
with torch.inference_mode():
    out = model(**inputs)

out.pooler_output      # [1, 384]      CLS token
out.last_hidden_state  # [1, 793, 384] CLS + 8 register + 784 patch tokens
```

Always use `.eval()` for inference. Embeddings are deterministic — repeated passes over the
same tile are bit-identical.

## Access

This model is gated. To request access, you must be logged into a Hugging Face account. You will
be asked to provide your name and must use an academic or non-profit email address (e.g., .edu,
.org). All requests are reviewed and granted at Aignostics' discretion.

## License Terms

**License:** CC BY-NC-ND 4.0, plus the following supplementary terms, which control in case of conflict:

**Field of use.** Academic research use only. No diagnostic, preventive, therapeutic, clinical,
or commercial use.

**AI/ML training restriction.** RudolfV 2-S may not be used to train models designed to replicate
or approximate the capabilities of RudolfV 2-S or to generate training labels, pseudo-labels, or
any form of supervision signal for such models, direct or indirect. RudolfV 2-S may not be used to
provide training signals for any foundation model or general-purpose pathology model, direct or
indirect.

**No warranty.** RudolfV 2-S is provided as-is, without warranty of any kind. Aignostics accepts no liability for results obtained through its use.

**Attribution requirement.** Publications and presentations using RudolfV 2-S are encouraged, but must follow the Attribution Requirement below.

## Attribution Requirement

<!-- TODO(maintainers): add volume/arXiv ID once available. -->

```bibtex
@misc{rudolfv2,
  title  = {RudolfV 2: A Family of Robust and Efficient Open-Weights
            Pathology Foundation Models},
  author = {Milbich, Timo and Eulig, Elias and Carpen-Amarie, Alexandra and
            Dippel, Jonas and Muttenthaler, Lukas and Tietz, Stephan and
            Perez Cancer, Beatriz and L{\"u}scher, J{\'e}r{\^o}me and
            Benetti, Alessandro and Hashimi, Sayed Abid and Shah, Neelay and
            Kr{\"u}gener, Moritz and Jurmeister, Philipp and Horst, David and
            Norgan, Andrew and Schallenberg, Simon and Ruff, Lukas and
            M{\"u}ller, Klaus-Robert and Klauschen, Frederick and Alber, Maximilian},
  year   = {2026},
}
```