---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': bud
            '1': flower
            '2': early-fruit
            '3': mid-growth
            '4': mature
  splits:
  - name: train
    num_bytes: 887034006
    num_examples: 5857
  download_size: 832778925
  dataset_size: 887034006
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---

# Pomegranate Growth Detection

A dataset for object detection of Pomegranates as they grow. The dataset contains 5,857 images with 11,484 bounding box annotations across 5 categories.

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

## Citation

```bibtex
@article{zhao2023dataset,
  title={A dataset of pomegranate growth stages for machine learning-based monitoring and analysis},
  author={Zhao, Jifei and Almodfer, Rolla and Wu, Xiaoying and Wang, Xinfa},
  journal={Data in brief},
  volume={50},
  pages={109468},
  year={2023},
  publisher={Elsevier}
}
```

Zhao, Jifei; Almodfer, Rolla (2023), “Pomegranate Images Dataset”, Mendeley Data, V5, doi: 10.17632/kgwsthf2w6.5