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Model card for H-optimus-1

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H-optimus-1 foundation model for histology, developed by Bioptimus.

The model is a 1.1B parameter vision transformer trained with self-supervised learning on an extensive proprietary dataset of billions of histology images sampled from over 1 million slides of more than 800,000 patients.

H-optimus-1 can be used to extract powerful features from histology images for various downstream applications, such as mutation prediction, survival analysis, or tissue classification/segmentation.

How to use it to extract features

The code below can be used to run inference. H-optimus-1 expects images of size 224x224 that were extracted at 0.5 microns per pixel.

from huggingface_hub import login
import torch
import timm 
from torchvision import transforms

# Login to the Hugging Face hub, using your user access token that can be found here:
# https://huggingface.co/settings/tokens.
login()

model = timm.create_model(
    "hf-hub:bioptimus/H-optimus-1", pretrained=True, init_values=1e-5, dynamic_img_size=False
)
model.to("cuda")
model.eval()

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=(0.707223, 0.578729, 0.703617), 
        std=(0.211883, 0.230117, 0.177517)
    ),
])

input = torch.rand(3, 224, 224)
input = transforms.ToPILImage()(input)

# We recommend using mixed precision for faster inference.
with torch.autocast(device_type="cuda", dtype=torch.float16):
    with torch.inference_mode():
        features = model(transform(input).unsqueeze(0).to("cuda"))

assert features.shape == (1, 1536)

Acknowledgments

This project was provided with computing HPC and storage resources by GENCI at IDRIS thanks to the grant 2024-GC011015442 on the supercomputer Jean Zay's H100 partition.

BibTeX entry and citation info

If you find this repository useful, please consider citing our work:

@inproceedings{scalbert2026hoptimus1,
  title={H-optimus-1: A foundation model for computational histopathology},
  author={Scalbert, Marin and Saillard, Charlie and Peeters, Thomas and Gonzalez, Liam and Valter, Dasha and Llinares-López, Felipe and Mariet, Zelda E. and Jenatton, Rodolphe},
  booktitle={Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts)},
  volume={86},
  number={8\_Suppl},
  pages={LB174},
  year={2026},
  publisher={AACR},
  journal={Cancer Research},
  doi={10.1158/1538-7445.AM2026-LB174}
}

Legal information

No commercial use: The H-optimus-1 model and associated code/weights may be reproduced and shared solely for non-commercial research, including academic research purposes, subject to the terms of the CC BY-NC-ND 4.0 license. Any commercial use, sale or other monetization of this model, or any part of it, is prohibited and requires a separate license. For commercial licensing options, please contact hello@bioptimus.com.
Attribution: Any sharing or permitted use of H-optimus-1 model must include (i) appropriate credits and attribution, (ii) a notice mentioning applicability of CC BY-NC-ND 4.0 license with an URL link, and (iii) our copyright notice and disclaimer of warranty notice (including on privacy) reproduced herein. This information must be provided by any reasonable means, without suggesting that we support user's organization or use of the model.
No derivatives: Derivative versions involving a modification to or from H-optimus-1 model, may not be shared nor made available. Derivative versions include, without limitation, models developed, based or trained through distillation of H-optimus-1 model.
No additional restrictions: No additional legal terms or technological restrictions may be imposed on the rights granted under the CC BY-NC-ND 4.0 license. This summary is provided for information only and does not amend, limit or otherwise affect the scope or terms of the CC BY-NC-ND 4.0 license, which shall prevail in the event of any inconsistency.
Disclaimer of warranties / Limitation of liability: H-optimus-1 model is provided "as-is" without warranties of any kind, express or implied. This includes, without limitation, no warranty of fitness for a particular purpose. This model has not been reviewed, certified, or approved by any regulatory body, including but not limited to the FDA (U.S.), EMA (Europe), MHRA (UK), or other medical device authorities. Any application of this model in healthcare or biomedical settings must comply with relevant regulatory requirements and undergo independent validation. Users assume full responsibility for how they use this model and any resulting consequences. The licensors, as well as authors, contributors and distributors disclaim any liability for damages, direct or indirect, resulting from model use.
Privacy: When using H-optimus-1 model, users undertake to and are solely responsible for ensuring their compliance with applicable local privacy regulations, including the GDPR in the EU and/or HIPAA in the US. Users shall not attempt to re-identify anonymized or pseudonymized data used to develop H-optimus-1 model.
Registration and download: Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. The primary email used to sign up for your Hugging Face account must match your institutional email to receive approval. By downloading the model, you attest that all information (affiliation, research use) is correct and up-to-date. If another user within your organization wishes to use the H-optimus-1 model, they must register as an individual user and agree to comply with the terms of use.