metadata
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
@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