Create README.md

README.md

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+---
+configs:
+- config_name: raw
+  default: true
+  data_dir: raw
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': Defect Dragon Fruit
+          '1': Fresh Dragon Fruit
+- config_name: augmented
+  data_dir: augmented
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': Defect Dragon Fruit
+          '1': Fresh Dragon Fruit
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 1K<n<10K
+---
+
+# Dragonfruit Quality Classification
+
+A dataset for quality classification of dragonfruit. The dataset contains raw and augmented versions.  
+The raw dataset contains 1,652 images.  
+Images per class:
+- Defect Dragon Fruit: 754
+- Fresh Dragon Fruit: 898
+
+The augmented dataset contains 5,000 images.  
+Images per class:
+- Defect Dragon Fruit: 3,000
+- Fresh Dragon Fruit: 2,000
+
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{khatun2024comprehensive,
+  title={A comprehensive dragon fruit image dataset for detecting the maturity and quality grading of dragon fruit},
+  author={Khatun, Tania and Nirob, Md Asraful Sharker and Bishshash, Prayma and Akter, Morium and Uddin, Mohammad Shorif},
+  journal={Data in Brief},
+  volume={52},
+  pages={109936},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+Khatun, Tania; Nirob, Md. Asraful Sharker ; Bishshash, Prayma  ; Uddin, Mohammad Shorif  (2023), “Dragon Fruit Maturity Detection and Quality Grading Dataset”, Mendeley Data, V1, doi: 10.17632/2jpzbx8tm6.1