---
configs:
- config_name: default
  default: true
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': agnishika
          '1': anantamul
          '2': apang
          '3': ayapan
          '4': bamonhati
          '5': basok
          '6': betal
          '7': chapalish
          '8': gainura
          '9': kalochitra
          '10': kalodhutura
          '11': kalomegh
          '12': punarnava
          '13': ramtulsi
          '14': sarpagandha
          '15': shotomuli
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---

# Remp Plant Classification

A dataset for image classification of various medicinal plants. The dataset contains 3,494 images across 16 classes: agnishika, anantamul, apang, ayapan, bamonhati, basok, betal, chapalish, gainura, kalochitra, kalodhutura, kalomegh, punarnava, ramtulsi, sarpagandha, shotomuli.  
Images per class:
- agnishika: 138
- anantamul: 158
- apang: 280
- ayapan: 294
- bamonhati: 134
- basok: 151
- betal: 194
- chapalish: 238
- gainura: 238
- kalochitra: 201
- kalodhutura: 276
- kalomegh: 294
- punarnava: 407
- ramtulsi: 154
- sarpagandha: 229
- shotomuli: 108

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

## Citation

```bibtex
@article{islam2024remp,
  title={REMP: A unique dataset of rare and endangered medicinal plants in Bangladesh for sustainable healing and biodiversity conservation},
  author={Islam, Mohammad Manzurul and Rahman, Sanjida and Hoque, Nahida and Al Mamun, Md and Moheuddin, Md Sultan and Ali, Md Sawkat and Rashid, Mohammad Rifat Ahmmad and Masum, Saleh and Ferdaus, Md Hasanul and Niloy, Nishat Tasnim and others},
  journal={Data in Brief},
  volume={57},
  pages={110895},
  year={2024},
  publisher={Elsevier}
}
```

Islam, Mohammad Manzurul; Rahman, Sanjida; Hoque, Nahida; Mamun, Md. Al; Moheuddin, Md. Sultan (2024), “REMP: A Unique Dataset of Rare and Endangered Medicinal Plants in Bangladesh”, Mendeley Data, V1, doi: 10.17632/hnwrxg8zm8.1