---
tags:
- image-feature-extraction
- timm
- pathology
- histology
- medical imaging
- self-supervised learning
- vision transformer
- foundation model
library_name: timm
license: apache-2.0
extra_gated_prompt: >-
  - This model and associated code are released under the Apache-2.0 license. 

  - This model is provided “as-is” without warranties of any kind, express or implied. 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 authors, contributors, and distributors disclaim any liability for damages, direct or indirect, resulting from model use. Users are responsible for ensuring compliance with data protection regulations (e.g., GDPR, HIPAA) when using it in research that involves patient data.
extra_gated_fields:
  Full name (first and last): text
  Type of Affiliation:
    type: select
    options:
      - Academia
      - Industry
      - label: Other
        value: other
  Current affiliation: text
  Current and official institutional email: text
  Main use-case:
    type: select
    options:
    - Models benchmarking on various tasks
    - Biomarker Discovery
    - Diagnostics
    - Pathology workflows acceleration (cell & tissue segmentation etc)
    - label: Other
      value: other
  I am interested in receiving updates from Bioptimus: 
    type: checkbox
    optional: true
---

# Model card for H-optimus-0

<p align="center">
<img src="./logo.png" width="500" height="180" />
</p>

`H-optimus-0` is an open-source foundation model for histology, developed by [Bioptimus](https://www.bioptimus.com/).
The model is a 1.1B parameter vision transformer trained on a proprietary collection of more than 500,000 H&E stained whole slide histology images.
For more information, please refer to our GitHub repository [here](https://github.com/bioptimus/releases/tree/main/models/h-optimus/v0?utm_source=owkin&utm_medium=referral&utm_campaign=h-bioptimus-o).

`H-optimus-0` can be used to extract powerful features from histology images for various downstream applications, such as mutation prediction, survival analysis, or tissue classification.

## How to use it to extract features.

The code below can be used to run inference; `H-optimus-0` expects images of size 224x224 that were extracted at 0.5 microns per pixel.
```python
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-0", 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)
```

## BibTeX entry and citation info.

If you find this repository useful, please consider citing our work:
```
@software{hoptimus0,
  author = {Saillard, Charlie and Jenatton, Rodolphe and Llinares-López, Felipe and Mariet, Zelda and Cahané, David and Durand, Eric and Vert, Jean-Philippe},
  title = {H-optimus-0},
  url = {https://github.com/bioptimus/releases/tree/main/models/h-optimus/v0},
  year = {2024},
}
```