Improve model card: refine pipeline tag and add GitHub link (#1)

- Improve model card: refine pipeline tag and add GitHub link (f32843464db5926eda6fb7047e582665d8d73948)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,63 +1,50 @@
 ---
+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
-library_name: timm
-datasets:
-  - Elsafty
-  - Chula
-  - DSE
-pipeline_tag: feature-extraction
+- 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
-      - 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
+- 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.  
@@ -68,17 +55,18 @@ Unlike general-purpose models pretrained on natural images, RedDino incorporates
 
 > 🧠 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)
+> 📄 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
+-   **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