---
license: cc-by-4.0
tags:
  - red-blood-cells
  - hematology
  - medical-imaging
  - vision-transformer
  - dino
  - dinov2
  - feature-extraction
  - foundation-model
library_name: timm
datasets:
  - Elsafty
  - Chula
  - DSE
pipeline_tag: feature-extraction
model-index:
  - name: RedDino-small
    results:
      - task:
          type: image-classification
          name: RBC Shape Classification
        dataset:
          name: Elsafty
          type: Classification
        metrics:
          - type: Weighted F1
            value: 86.0
          - type: Balanced Accuracy
            value: 87.2
          - type: Accuracy
            value: 86.2
      - task:
          type: image-classification
          name: RBC Shape Classification
        dataset:
          name: Chula
          type: Classification
        metrics:
          - type: Weighted F1
            value: 84.3
          - type: Balanced Accuracy
            value: 78.5
          - type: Accuracy
            value: 84.4
      - task:
          type: image-classification
          name: RBC Shape Classification
        dataset:
          name: DSE
          type: Classification
        metrics:
          - type: Weighted F1
            value: 84.9
          - type: Balanced Accuracy
            value: 56.5
          - type: Accuracy
            value: 84.9
---

# RedDino-small

**RedDino** is a self-supervised Vision Transformer foundation model specifically designed for **red blood cell (RBC)** image analysis.  
This variant is the compact model in the family, delivering strong performance with lighter computational cost.

It leverages a tailored version of the **DINOv2** framework, trained on a meticulously curated dataset of RBC images from diverse acquisition modalities and sources.  
The model excels at extracting robust features for downstream hematology tasks such as **shape classification**, **morphological subtype recognition**, and **batch-effect–robust analysis**.

> 🧠 Developed by [Luca Zedda](https://orcid.org/0009-0001-8488-1612), [Andrea Loddo](https://orcid.org/0000-0002-6571-3816), [Cecilia Di Ruberto](https://orcid.org/0000-0003-4641-0307), and [Carsten Marr](https://orcid.org/0000-0003-2154-4552)  
> 🏥 University of Cagliari & Helmholtz Munich  
> 📄 Preprint: [arXiv:2508.08180](https://arxiv.org/abs/2508.08180)

---

## Model Details

- **Architecture:** ViT-small, patch size 16 (`s16`)
- **SSL framework:** DINOv2 (customized for RBC morphology)
- **Pretraining dataset:** Curated RBC images from 18 datasets (multiple modalities and sources)
- **Embedding size:** 384
- **Intended use:** RBC morphology classification, feature extraction, batch-effect–robust analysis

Notes:
- Trained with RBC-specific augmentations and DINOv2 customizations (e.g., removal of KoLeo regularizer; Sinkhorn-Knopp centering).
- Optimized using smear patches rather than only single-cell crops to improve generalization across sources.

## Example Usage

```python
from PIL import Image
from torchvision import transforms
import timm
import torch

# Load model from Hugging Face Hub
model = timm.create_model("hf_hub:Snarcy/RedDino-small", pretrained=True)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Load and preprocess image
image = Image.open("path/to/rbc_image.jpg").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225]),
])
input_tensor = transform(image).unsqueeze(0).to(device)

# Extract features
with torch.no_grad():
    embedding = model(input_tensor)
```
## 📝 Citation

If you use this model, please cite the following paper:

**RedDino: A foundation model for red blood cell analysis**  
Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Carsten Marr — 2025  
Preprint: arXiv:2508.08180. https://arxiv.org/abs/2508.08180

```bibtex
@misc{zedda2025reddinofoundationmodelred,
      title={RedDino: A foundation model for red blood cell analysis}, 
      author={Luca Zedda and Andrea Loddo and Cecilia Di Ruberto and Carsten Marr},
      year={2025},
      eprint={2508.08180},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.08180}, 
}
```