Update README.md

README.md

@@ -37,15 +37,25 @@ This research investigates the application of computer vision for rapid, accurat
 https://arxiv.org/html/2505.08537v1
 
 ## Data Instances
-![Example 1](1.png)
-![Example 2](1.png)
+<div style="display: flex; flex-direction: row; align-items: center;">
+  <img src="1.png" width="500" style="margin-right: 10px;">
+  <img src="1.png" width="500">
+</div>
 
 ## Annotation Format
 Note that the annotations folow the YOLO instance segmentation format. Please refer to this page for more info: https://docs.ultralytics.com/datasets/segment/
 
+## Acknowledgement
+<img src="AGILEHAND.png" width="200" style="margin-right: 10px;">
+This work is supported by European Union’s Horizon Europe research and innovation programme under grant agreement No 101092043, project AGILEHAND (Smart Grading, Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines.
 
-## Citation
+## Partners
+<div style="display: flex; flex-direction: row; align-items: center;">
+  <img src="FBK.jpg" width="200" style="margin-right: 10px;">
+  <img src="Santorsola.jpeg" width="300">
+</div>
 
+## Citation
 ```bibtex
 @article{riz2024wild,
   title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning},