---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': tree
            '1': flower
            '2': premature
            '3': unripe
            '4': ripe
            '5': spoiled
  splits:
  - name: train
    num_bytes: 436051831
    num_examples: 3098
  download_size: 447273427
  dataset_size: 436051831
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
---
# Cashew Detection

A dataset for object detection of cashew flowers and fruits. The dataset contains 3,098 images with 88,364 bounding box annotations across 6 categories.

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

## Citation

```bibtex
@article{sanya2024coffee,
  title={Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications},
  author={Sanya, Rahman and Nabiryo, Ann Lisa and Tusubira, Jeremy Francis and Murindanyi, Sudi and Katumba, Andrew and Nakatumba-Nabende, Joyce},
  journal={Data in Brief},
  volume={52},
  pages={109952},
  year={2024},
  publisher={Elsevier}
}
```

Nakatumba-Nabende, Joyce; Katumba, Andrew; Sanya, Rahman; Tusubira, Jeremy; Murindanyi, Sudi; Namanya, Gloria; Nabiryo, Ann (2023), “Coffee and Cashew Nut Dataset”, Mendeley Data, V1, doi: 10.17632/r46c6bpfpf.1

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