---
configs:
- config_name: augmented
  data_dir: augmented
- config_name: raw
  data_dir: raw
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
dataset_info:
- config_name: augmented
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Bad
          '1': Good
- config_name: raw
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Bad
          '1': Good
  splits:
  - name: train
    num_bytes: 8433220
    num_examples: 4846
  download_size: 7168978
  dataset_size: 8433220
---

# Efficientmaize Classification

A dataset for quality classification of maize. The dataset contains raw and augmented versions.  
The raw dataset contains 4,846 images.  
Images per class:
- Bad: 2,211
- Good: 2,635

The augmented dataset contains 28,899 images.  
Images per class:
- Bad: 13,246
- Good: 15,653


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

## Citation

```bibtex
@article{asante2024efficientmaize,
  title={EfficientMaize: A lightweight dataset for maize classification on resource-constrained devices},
  author={Asante, Emmanuel and Appiah, Obed and Appiahene, Peter and Adu, Kwabena},
  journal={Data in Brief},
  volume={54},
  pages={110261},
  year={2024},
  publisher={Elsevier}
}
```

Asante, Emmanuel ; Appiah, Obed; APPIAHENE, PETER (2023), “Lightweight Dataset for Maize Classification on Resource-Constrained Devices”, Mendeley Data, V2, doi: 10.17632/r6vvm5jkh6.2