Update README.md

README.md

@@ -12,11 +12,11 @@ dataset_info:
       list:
         class_label:
           names:
-            '0': '0'
-            '1': '1'
-            '2': '2'
-            '3': '3'
-            '4': '4'
+            '0': bud
+            '1': flower
+            '2': early-fruit
+            '3': mid-growth
+            '4': mature
   splits:
   - name: train
     num_bytes: 887034006
@@ -28,4 +28,31 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- object-detection
+size_categories:
+- 1K<n<10K
 ---
+
+# Pomegranate Growth Detection
+
+A dataset for object detection of Pomegranates as they grow. The dataset contains 5,857 images with 11,484 bounding box annotations across 5 categories.
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{zhao2023dataset,
+  title={A dataset of pomegranate growth stages for machine learning-based monitoring and analysis},
+  author={Zhao, Jifei and Almodfer, Rolla and Wu, Xiaoying and Wang, Xinfa},
+  journal={Data in brief},
+  volume={50},
+  pages={109468},
+  year={2023},
+  publisher={Elsevier}
+}
+```
+
+Zhao, Jifei; Almodfer, Rolla (2023), “Pomegranate Images Dataset”, Mendeley Data, V5, doi: 10.17632/kgwsthf2w6.5