Update README.md

README.md

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----
-license: cc-by-nc-4.0
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: annotations
-    list:
-    - name: class_id
-      dtype: int64
-    - name: segmentation
-      sequence:
-        sequence:
-          sequence: float64
-  splits:
-  - name: train
-    num_bytes: 103638330.0
-    num_examples: 82
-  - name: valid
-    num_bytes: 26074864.0
-    num_examples: 21
-  download_size: 124824112
-  dataset_size: 129713194.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: valid
-    path: data/valid-*
----
+---
+license: cc-by-nc-4.0
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: annotations
+    list:
+    - name: class_id
+      dtype: int64
+    - name: segmentation
+      sequence:
+        sequence:
+          sequence: float64
+  splits:
+  - name: train
+    num_bytes: 103638330.0
+    num_examples: 82
+  - name: valid
+    num_bytes: 26074864.0
+    num_examples: 21
+  download_size: 124824112
+  dataset_size: 129713194.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: valid
+    path: data/valid-*
+---
+
+
+
+---
+# Vision-Guided Robotic System for Automatic Fish Quality Grading and Packaging 
+This dataset contains images and corresponding (yolo format) instance segmentation annotations of (hake) fish steaks as they move along a conveyor belt in an industrial setting. 
+Moreover, it also contains the BAG files (recorded using Realsense D456) of two fish steak grades (A and B). The A steaks are typically larger than B steaks.
+In our paper (link below). We applied instance segmentation to isolate the fish steaks based on YOLOv8 (Check [here](https://docs.ultralytics.com/models/yolov8/) how to train and validate the model). 
+Once the fish steaks are segmented, we simply measure their size by leveraging the depth data contained in the BAG files.
+
+🤗 [Paper on Hugging Face]Coming soon ...   |   📝 [Paper on ArXiv] Coming soon ...
+
+## 🗂️ BAG files
+The BAG files of A and B grades, as well as the weights of the trained segmentation model (best.pt and last.pt) can be found here [here](https://fbk-my.sharepoint.com/:f:/g/personal/mmekhalfi_fbk_eu/ElmBGeHUIwpPveSRrfd7qu4BQpAiWsOo70m8__V875yggw?e=1L0iTT)
+
+## 🗂️ Data Instances
+Coming soon ...
+
+## 🏷️ Annotation Format
+Note that the annotations follow the YOLO instance segmentation format.
+
+Please refer to [this page](https://docs.ultralytics.com/datasets/segment/) for more info.
+
+## 🧪 How to use
+Coming soon ...
+
+
+## 🙏 Acknowledgement
+<style>
+  .list_view{
+      display:flex;
+      align-items:center;
+  }
+  .list_view p{
+      padding:10px;
+  }
+</style>
+<div class="list_view">
+  <a href="https://agilehand.eu/" target="_blank">
+    <img src="AGILEHAND.png" alt="AGILEHAND logo" style="max-width:200px">
+  </a>
+  <p style="line-height: 1.6;">
+  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.
+  </p>
+</div>
+
+## 🤝 Partners
+<div style="display: flex; flex-wrap: wrap; justify-content: center; gap: 40px; align-items: center;">
+  <a href="https://www.fbk.eu/en" target="_blank"><img src="FBK.jpg" width="180" alt="FBK logo"></a>
+  <a href="https://https://produmar.pai.pt/" target="_blank"><img src="produmar.png" width="250" alt="Produmar logo"></a>
+</div>
+
+
+## 📖 Citation
+Coming soon ...
+```