Create README.md

README.md

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+---
+configs:
+- config_name: default
+  default: true
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': bruised
+          '1': cracked
+          '2': rotten
+          '3': spotted
+          '4': unaffected
+          '5': unripe
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 1K<n<10K
+---
+
+# African Plum Grading Classification
+
+A dataset for grade classification of plums. The dataset contains 4,507 images across 6 classes: bruised, cracked, rotten, spotted, unaffected, unripe.
+Images per class:
+- bruised: 319
+- cracked: 162
+- rotten: 720
+- spotted: 759
+- unaffected: 1,721
+- unripe: 826
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{fadja2025dataset,
+  title={A dataset of annotated African plum images from Cameroon for AI-based quality assessment},
+  author={Fadja, Arnaud Nguembang and Tagni, Armel Gabin Fameni and Che, Sain Rigobert and Atemkeng, Marcellin},
+  journal={Data in Brief},
+  volume={59},
+  pages={111351},
+  year={2025},
+  publisher={Elsevier}
+}
+```
+
+Arnaud Nguembang Fadja, and Armel Gabin Fameni Tagni. (2024). African Plums Dataset [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/9694239