Datasets:

Project-AgML
/

MangoClassify-12_variety_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Amrapali
            '1': Banana
            '2': Bari 4
            '3': Fazli
            '4': GobindoBhog
            '5': GopalBhog
            '6': Harivanga
            '7': Himsagar
            '8': Khrishapat
            '9': Langra
            '10': RaniBhog
            '11': Sundari
  splits:
    - name: train
      num_bytes: 17855503855
      num_examples: 3900
  download_size: 18516857667
  dataset_size: 17855503855
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

MangoClassify 12 Variety Classification

A dataset for variety classification of common mangoes. The dataset contains 3,900 images across 12 classes:
Images per class:

Amrapali: 600
Banana: 212
Bari 4: 240
Fazli: 120
GobindoBhog: 41
GopalBhog: 406
Harivanga: 575
Himsagar: 502
Khrishapat: 380
Langra: 506
RaniBhog: 92
Sundari: 226

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{rahman2025mangoclassify,
  title={MangoClassify-12: A high-resolution image dataset of twelve indigenous Bangladeshi mango cultivars},
  author={Rahman, Md Sajedur and Nahin, Md Mahfuz Ahmed and Rahman, Md Mahbubur and Rani, Mollika and Islam, Md Ashraful and Bashir, Al and Shafkat, Ahmad and Mallik, Bijon and Majeed, Yaqoob},
  journal={Data in Brief},
  pages={112037},
  year={2025},
  publisher={Elsevier}
}

Md. Sajedur Rahman, Md. Mahfuz Ahmed Nahin, Mollika Rani, and MD Ashraful Islam. (2025). MangoClassify-12: Native Mango Dataset from BD [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/12460544