Added descriptions

README.md

@@ -28,8 +28,10 @@ The **AUTOFISH** dataset comprises 1500 high-quality images of fish on a conveyo
 and a total of 18,160 instance segmentation masks.
 
 The fish are partitioned into 25 groups, with 14 to 24 fish in each group. Each fish only appears in one group, making it easy to create training splits. The
-number of fish and distribution of species in each group were pseudo-randomly selected to mimic real-world scenarios. In addition to all the labeled data, two high-overlap 
-unlabeled groups, as well as camera calibration images are included.
+number of fish and distribution of species in each group were pseudo-randomly selected to mimic real-world scenarios. Every group is partitioned into three subsets: *Set1*, *Set2*, and *All*. *Set1* and *Set2* contain half of the fish each, and none of the
+fish overlap or touch each other. *All* contains all the fish from the group, purposely placed in positions with high overlap. Every group directory contains 20 images for
+each set, where variation is introduced by changing the position and orientation of the fish. Exactly half of every set is with the fish on their one side, while the other
+half has the fish flipped. *See the dataset viewer for examples.*
 
 The available classes are:
 - Cod
@@ -46,14 +48,18 @@ Other information contained in the annotations:
 - Unique fish IDs
 - 'Side up' referring to the side of the fish that is visible
 
-Extra information: <br>
-Every group is partitioned into three subsets: *Set1*, *Set2*, and *All*. *Set1* and *Set2* contain half of the fish each, and none of the
-fish overlap or touch each other. *All* contains all the fish from the group, purposely placed in positions with high overlap. Every group directory contains 20 images for
-each set, where variation is introduced by changing the position and orientation of the fish. Exactly half of every set is with the fish on their one side, while the other
-half has the fish flipped.
+In addition to all the labeled data, two high-overlap 
+unlabeled groups, as well as camera calibration images are included.
 
-In cases of high overlap, where fish occlude each other, some annotations might have multiple segmentation masks in a list of lists format.
+You can load this dataset with a default split configuration using the datasets library
+```python
+dataset = datasets.load_dataset('vapaau/autofish', trust_remote_code=True)
+```
 
+If you use this dataset for your work, please cite:
+```yaml
+Citation coming soon
+```
 <!-- The annotations are in COCO format, with a structure as per the following example: -->
 <!-- ```yaml
 {

autofish.py

@@ -3,7 +3,10 @@ import json
 import datasets
 
 class Autofish(datasets.GeneratorBasedBuilder):
-    """Custom loading script for the Autofish dataset."""
+    """
+    Custom loading script for the Autofish dataset.
+    Specifically designed to be used with the Datasets package
+    """
     
     # Define the features of the dataset
     def _info(self):