RedDino Base
RedDino is a self-supervised Vision Transformer foundation model specifically designed for red blood cell (RBC) image analysis. It leverages a tailored version of the DINOv2 framework, trained on a meticulously...
Model source
- Kind
- Other
- File size
- 327 MB
- Version
- vRevision 8f99b88
- Updated
- Sep 17, 2026
- Downloads
- 106
- Source
- Hugging Face
Source description
RedDino-base
RedDino is a self-supervised Vision Transformer foundation model specifically designed for red blood cell (RBC) image analysis.
It leverages a tailored version of the DINOv2 framework, trained on a meticulously curated dataset of 1.25 million RBC images from diverse acquisition modalities and sources.
This model excels at extracting robust, general-purpose features for downstream hematology tasks such as shape classification, morphological subtype recognition, and batch-effect–robust analysis.
Unlike general-purpose models pretrained on natural images, RedDino incorporates hematology-specific augmentations, architectural tweaks, and RBC-tailored data preprocessing, enabling state-of-the-art performance on multiple RBC benchmarks.
🧠 Developed by Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, and Carsten Marr
🏥 University of Cagliari & Helmholtz Munich
📄 Preprint: arXiv:2508.08180
💻 Code: https://github.com/Snarci/RedDino
Model Details
- Architecture: ViT-base, patch size 14
- SSL framework: DINOv2 (customized for RBC morphology)
- Pretraining dataset: 1.25M RBC images from 18 datasets
- Embedding size: 768
- Applications: RBC morphology classification, feature extraction, batch-effect–robust analysis
Example Usage
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-base", 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
@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},
}
Sources
1 source
Sources
1 sourceVerified Sep 2
Model artifacts
2 artifacts
Model artifacts
2 artifactsSource excerpts
2 excerpts
Source excerpts
2 excerpts--- datasets: - Elsafty - Chula - DSE library_name: timm license: cc-by-4.0 pipeline_tag: image-feature-extraction tags: - red-blood-cells - hematology - medical-imaging - vision-transformer - dino - dinov2 - feature-extraction - foundation-model model-index: - name: RedDino-base results: - task: type: image-classification name: RBC Shape Classification dataset: name: Elsafty type: Classification metrics: - type: Weighted F1 value: 88.1 - type: Balanced Accuracy value: 89.3 - type: Accuracy value: 88.2 - type: Weighted F1 value: 83.8 - type: Balanced Accuracy value: 78.6 - type: Accuracy value: 83.8 - type: Weighted F1 value: 85.9 - type: Balanced Accuracy value: 57.9 - type: Accuracy value: 86.0 --- # RedDino-base **RedDino** is a self-supervised Vision Transformer foundation model specifically designed for **red blood cell (RBC)** image analysis. It leverages a tailored version of the **DINOv2** framework, trained on a meticulously curated dataset of **1.25 million RBC images** from diverse acquisition modalities and sources. This model excels at extracting robust, general-purpose features for downstream hematology tasks such as **shape classification**, **morphological subtype recognition**, and **batch-effect–robust analysis**. Unlike general-purpose models pretrained on natural images, RedDino incorporates hematology-specific augmentations, architectural tweaks, and RBC-tailored data preprocessing, enabling **state-of-the-art performance** on multiple RBC benchmarks. > 🧠 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) > 💻 Code: [https://github.com/Snarci/RedDino](https://github.com/Snarci/RedDino) --- ## Model Details - **Architecture:** ViT-base, patch size 14 - **SSL framework:** DINOv2 (customized for RBC morphology) - **Pretraining dataset:** 1.25M RBC images from 18 datasets - **Embedding size:** 768 - **Applications:** RBC morphology classification, feature extraction, batch-effect–robust analysis ## Example Usage ```python from PIL import Image from torchvision import transforms import timm import torch #...
Source context: 106 downloads · 2 likes · Pipeline image-feature-extraction · Library timm · Repo Snarcy/RedDino-base
