---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': grade-1
          '1': grade-2
          '2': grade-3
  - name: quality
    dtype:
      class_label:
        names:
          '0': quality-1
          '1': quality-2
          '2': quality-3
          '3': quality-4
  splits:
  - name: train
    num_bytes: 129205249
    num_examples: 1080
  download_size: 127371587
  dataset_size: 129205249
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc0-1.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Pomegranate Quality Classification

A dataset for classification of Pomegranate quality. The dataset contains 1,080 images across 3 classes: G1_Q1, G2_Q1, G3_Q1.  
Images per class:
- G1_Q1: 360
- G2_Q1: 360
- G3_Q1: 360

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

## Citation

```bibtex
@article{kumar2021image,
  title={Image dataset of pomegranate fruits (Punica granatum) for various machine vision applications},
  author={Kumar, Arun and Rajpurohit, Vijay S and Gaikwad, Nilesh N},
  journal={data in Brief},
  volume={37},
  pages={107249},
  year={2021},
  publisher={Elsevier}
}
```

Dr. Vijay S Rajpurohit, and Kshitijarun Y Bidari. (2020). Pomegranate Fruit Dataset [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/551234

*This dataset was reformatted from its original format to match HuggingFace standards.*