Update README.md

README.md

@@ -38,11 +38,13 @@ size_categories:
 
 
 ---
-# The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning
-This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color. The acquired and annotated RaspGrade dataset is accessible on Hugging Face at: https://huggingface.co/datasets/FBK-TeV/RaspGrade.
+# 🍓 The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning
+This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. 
+To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. 
 
-## ArXiv link
-https://arxiv.org/html/2505.08537v1
+Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color
+
+🤗 [Paper on Hugging Face](https://huggingface.co/papers/2505.08537)   |   📝 [Paper on ArXiv](https://arxiv.org/abs/2505.08537)
 
 ## Data Instances
 <div style="display: flex; flex-direction: row; align-items: center;">
@@ -51,7 +53,7 @@ https://arxiv.org/html/2505.08537v1
 </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/
+Note that the annotations follow 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;">
@@ -65,9 +67,13 @@ This work is supported by European Union’s Horizon Europe research and innovat
 
 ## Citation
 ```bibtex
-@article{riz2024wild,
-  title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning},
-  author={Mekhalfi, Mohamed Lamine and Chippendale, Paul and Poiesi, Fabio and Bonecher, Samuele and Osler, Gilberto and Zancanella, Nicola },
-  journal={arXiv preprint arXiv::2505.08537v1},
-  year={2025}
-}
+@misc{mekhalfi2025raspgradedatasetautomaticraspberry,
+      title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning}, 
+      author={Mohamed Lamine Mekhalfi and Paul Chippendale and Fabio Poiesi and Samuele Bonecher and Gilberto Osler and Nicola Zancanella},
+      year={2025},
+      eprint={2505.08537},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.08537}, 
+}
+```