Update README.md

README.md

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----
-tags:
-- image-classification
-- timm
-- transformers
-library_name: timm
-license: apache-2.0
----
-# Model card for RedDino-base
+---
+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-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
+      - task:
+          type: image-classification
+          name: RBC Shape Classification
+        dataset:
+          name: Chula
+          type: Classification
+        metrics:
+          - type: Weighted F1
+            value: 83.8
+          - type: Balanced Accuracy
+            value: 78.6
+          - type: Accuracy
+            value: 83.8
+      - task:
+          type: image-classification
+          name: RBC Shape Classification
+        dataset:
+          name: DSE
+          type: Classification
+        metrics:
+          - 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)
+
+---
+
+## Model Details
+
+- **Architecture:** ViT-base, patch size 16 (`b16`)
+- **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
+
+# 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
+
+```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}, 
+}
+```