Datasets:

Project-AgML
/

DeepWeeds_classification

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

@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.