Update README.md

README.md

@@ -14,13 +14,13 @@ dataset_info:
           sequence: float64
   splits:
   - name: train
-    num_bytes: 103638330
+    num_bytes: 103638330.0
     num_examples: 82
   - name: valid
-    num_bytes: 26074864
+    num_bytes: 26074864.0
     num_examples: 21
   download_size: 124824112
-  dataset_size: 129713194
+  dataset_size: 129713194.0
 configs:
 - config_name: default
   data_files:
@@ -28,36 +28,25 @@ configs:
     path: data/train-*
   - split: valid
     path: data/valid-*
-task_categories:
-- image-segmentation
-tags:
-- seafood-processing
-- industrial-vision
-- fish
-- food-processing
-pretty_name: FishGrade
-size_categories:
-- 1K<n<10K
 ---
 
 
 
 ---
 # 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.
+This dataset (recorded with a Realsense D456 camera), associated with our work accepted in the 'IEEE/CAA Journal of Automatica Sinica', includes images and corresponding instance segmentation annotations (in YOLO format) of hake fish steaks on an industrial conveyor belt. It also provides BAG files for two quality grades of fish steaks (A and B), where A-grade steaks are generally larger. 
+The paper details our use of YOLOv8 instance segmentation (Check [here](https://docs.ultralytics.com/models/yolov8/) how to train and validate the model) to isolate the fish steaks and the subsequent measurement of their size using the depth data from the BAG files.
 
-🤗 [Paper on Hugging Face] Coming soon ...   |   📝 [Paper on ArXiv] Coming soon ...
+🤗 [Paper on Hugging Face]Coming soon ...   |   📝 [Paper on ArXiv] Coming soon ...
 
 ## 🗂️ BAG files & trained segmentation model:
-Please, first read the paper to comprehend the proposed pipeline.
+Please first read the associated paper to understand the proposed pipeline.
 
-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.](https://fbk-my.sharepoint.com/:f:/g/personal/mmekhalfi_fbk_eu/ElmBGeHUIwpPveSRrfd7qu4BQpAiWsOo70m8__V875yggw?e=1L0iTT)
+The BAG files for A and B grades, as well as the weights of the trained segmentation model (best.pt and last.pt), can be found [here.](https://fbk-my.sharepoint.com/:f:/g/personal/mmekhalfi_fbk_eu/ElmBGeHUIwpPveSRrfd7qu4BQpAiWsOo70m8__V875yggw?e=1L0iTT).
 
-Remember, the segmentation model has the purpose of segmenting the fish samples, and the BAG files serve for test purposes (for example, you could use the trained model weight to segment the RGB images in the BAG files, then measure their size based on the depth data).
-For the sake of clarity, a simplified code to measure steak metric perimiter is given below, ywhich can be repurposed for your endeavour:
+The segmentation model is designed to segment fish samples. The BAG files are intended for testing purposes. For example, you could use the provided model weights to segment the RGB images within the BAG files and then measure their size based on the depth data.
+
+For clarity, a simplified code snippet for measuring steaks' (metric) perimeter is provided below. You can repurpose this for your specific task:
 
 ```python
 import pyrealsense2 as rs
@@ -212,8 +201,8 @@ if __name__ == '__main__':
 
 ## 🗂️ Data Instances
 <figure style="display:flex; gap:10px; flex-wrap:wrap; justify-content:center;">
-  <img src="Figure_1.png" width="47%" alt="Raspberry Example 1">
-  <img src="Figure_2.png" width="47%" alt="Raspberry Example 2">
+  <img src="Figure_1.png" width="45%" alt="Raspberry Example 1">
+  <img src="Figure_2.png" width="45%" alt="Raspberry Example 2">
 </figure>
 
 ## 🏷️ Annotation Format