Create README.md

README.md

@@ -0,0 +1,63 @@
+---
+configs:
+- config_name: raw
+  default: true
+  data_dir: raw
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': Bad
+          '1': Good
+- config_name: augmented
+  data_dir: augmented
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': Bad
+          '1': Good
+license: cc-by-4.0
+task_categories:
+- image-classification
+size_categories:
+- 10K<n<100K
+---
+
+# Efficientmaize Classification
+
+A dataset for quality classification of maize. The dataset contains raw and augmented versions.  
+The raw dataset contains 4,846 images.  
+Images per class:
+- Bad: 2,211
+- Good: 2,635
+
+The augmented dataset contains 28,899 images.  
+Images per class:
+- Bad: 13,246
+- Good: 15,653
+
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{asante2024efficientmaize,
+  title={EfficientMaize: A lightweight dataset for maize classification on resource-constrained devices},
+  author={Asante, Emmanuel and Appiah, Obed and Appiahene, Peter and Adu, Kwabena},
+  journal={Data in Brief},
+  volume={54},
+  pages={110261},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+Asante, Emmanuel ; Appiah, Obed; APPIAHENE, PETER (2023), “Lightweight Dataset for Maize Classification on Resource-Constrained Devices”, Mendeley Data, V2, doi: 10.17632/r6vvm5jkh6.2