---
configs:
- config_name: default
  default: true
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Bean
          '1': Bitter melon
          '2': Brinjal
          '3': Cucumber
          '4': Garlic
          '5': Green Chili
          '6': Ladies finger
          '7': Onion
          '8': Pointed gourd
          '9': Potato
          '10': Radish
          '11': Tomato
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# Vegetable Classification Banglades

A dataset for image classification of various types of vegetables. The dataset contains 4,319 images across 12 classes: Bean, Bitter melon, Brinjal, Cucumber, Garlic, Green Chili, Ladies finger, Onion, Pointed gourd, Potato, Radish, Tomato.
Images per class:
- Bean: 454
- Bitter melon: 306
- Brinjal: 373
- Cucumber: 342
- Garlic: 349
- Green Chili: 497
- Ladies finger: 308
- Onion: 357
- Pointed gourd: 329
- Potato: 365
- Radish: 310
- Tomato: 329

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

## Citation

```bibtex
@article{ahmed2025banglaveg,
  title={BanglaVeg: A curated vegetable image dataset from Bangladesh for precision agriculture},
  author={Ahmed, Md Jobayer and Saha, Ratu and Dutta, Arpon Kishore and Mojumdar, Mayen Uddin and Chakraborty, Narayan Ranjan},
  journal={Data in Brief},
  volume={59},
  pages={111441},
  year={2025},
  publisher={Elsevier}
}
```

Ahmed, Md Jobayer; Saha, Ratu; Dutta , Arpon Kishore ; Mojumdar, Mayen Uddin  (2025), “Vegetable Image Dataset for Classification Models: A Bangladeshi Perspective”, Mendeley Data, V4, doi: 10.17632/b9rvg4f2st.4