---
configs:
- config_name: default
  default: true
  features:
  - name: image
    dtype: image
  - name: objects
    sequence:
    - name: bbox
      list: float32
    - name: categories
      class_label:
            names:
              '0': Early-Fruit
              '1': Mature
              '2': Premature
              '3': Ripe
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---

# Mango Growth Object Detection

A dataset for object detection of Mango Growth. The dataset contains 2,000 images with 3,174 bounding box annotations across 4 categories.

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

## Citation

```bibtex
@article{kabir2025smartphone,
  title={Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stages},
  author={Kabir, Sayem and Akon, Md Fokrul and Rashid, Mohammad Rifat Ahmmad and Islam, Maheen and Jabid, Taskeed and Islam, Mohammad Manzurul and Ali, Md Sawkat},
  journal={Data in Brief},
  volume={61},
  pages={111780},
  year={2025},
  publisher={Elsevier}
}```

Kabir, Sayem ; Rashid, Mohammad Rifat Ahmmad (2024), “Image Dataset for Mango Growth Stages  Analysis”, Mendeley Data, V1, doi: 10.17632/5snwpzdtzs.1