RudolfV 2-S
RudolfV 2-S is a vision transformer based Foundation Model developed by Aignostics for computational pathology. It uses a ViT-S/8 backbone and was distilled from 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 for training details, full benchmark tables, and evaluation protocols.
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 | ViT-g | 1.1 B | 1× |
| RudolfV 2-B | ViT-B | 86 M | 2.5× faster |
| RudolfV 2-S (this repo) | ViT-S | 22 M | 6.5× faster |
Usage
pip install torch transformers timm pillow
trust_remote_code=True is required (custom modeling code), and timm is a hard dependency.
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
@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},
}