Project-AgML/african_plum_grading_classification

1---2configs:3- config_name: default4  data_files:5  - split: train6    path: data/train-*7license: cc-by-4.08task_categories:9- image-classification10size_categories:11- 1K<n<10K12dataset_info:13  features:14  - name: image15    dtype: image16  - name: label17    dtype:18      class_label:19        names:20          '0': bruised21          '1': cracked22          '2': rotten23          '3': spotted24          '4': unaffected25          '5': unripe26  splits:27  - name: train28    num_bytes: 35399207329    num_examples: 450730  download_size: 30756890731  dataset_size: 35399207332---33 34# African Plum Grading Classification35 36A dataset for grade classification of plums. The dataset contains 4,507 images across 6 classes: bruised, cracked, rotten, spotted, unaffected, unripe.37Images per class:38- bruised: 31939- cracked: 16240- rotten: 72041- spotted: 75942- unaffected: 1,72143- unripe: 82644 45This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.46 47## Citation48 49```bibtex50@article{fadja2025dataset,51  title={A dataset of annotated African plum images from Cameroon for AI-based quality assessment},52  author={Fadja, Arnaud Nguembang and Tagni, Armel Gabin Fameni and Che, Sain Rigobert and Atemkeng, Marcellin},53  journal={Data in Brief},54  volume={59},55  pages={111351},56  year={2025},57  publisher={Elsevier}58}59```60 61Arnaud Nguembang Fadja, and Armel Gabin Fameni Tagni. (2024). African Plums Dataset [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/9694239