Datasets:
File size: 1,850 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
- name: species
dtype: string
splits:
- name: train
num_bytes: 489520348
num_examples: 17509
download_size: 492631617
dataset_size: 489520348
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# Deepweeds Classification
This dataset comprises real-world RGB images capturing various weed species in agricultural field environments. Collected under natural outdoor conditions, the images provide a diverse visual representation of weeds for computer vision applications in precision agriculture. The dataset contains 17,509 images across 9 classes: 0, 1, 2, 3, 4, 5, 6, 7, 8.
Images per class:
- 0: 1,125
- 1: 1,064
- 2: 1,031
- 3: 1,022
- 4: 1,062
- 5: 1,009
- 6: 1,074
- 7: 1,016
- 8: 9,106
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{olsen2019deepweeds,
title={DeepWeeds: A multiclass weed species image dataset for deep learning},
author={Olsen, Alex and Konovalov, Dmitry A and Philippa, Bronson and Ridd, Peter and Wood, Jake C and Johns, Jamie and Banks, Wesley and Girgenti, Benjamin and Kenny, Owen and Whinney, James and others},
journal={Scientific reports},
volume={9},
number={1},
pages={2058},
year={2019},
publisher={Nature Publishing Group UK London}
}
```
https://github.com/AlexOlsen/DeepWeeds
*This dataset was reformatted from its original format to match HuggingFace standards.*
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