Datasets:

Project-AgML
/

ImageWeeds_weed_detection

dataset_info:
  features:
    - name: image
      dtype: image
    - name: objects
      struct:
        - name: bbox
          list:
            list: float64
        - name: categories
          list:
            class_label:
              names:
                '0': horseweed
                '1': kochia
                '2': corn
                '3': ragweed
                '4': redrootpigweed
  splits:
    - name: train
      num_bytes: 811723383
      num_examples: 3208
  download_size: 1334656681
  dataset_size: 811723383
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

ImageWeeds Weed Detection

A dataset for detection of weeds. The dataset contains 3,208 images with 6,932 bounding box annotations across 5 categories.

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

Citation

<!-- TODO: add BibTeX citation -->

Rai, Nitin; Villamil Mahecha, Maria; Christensen, Annika; Quanbeck, Jamison; Howatt, Kirk; Ostlie, Michael; Zhang, Yu; Sun, Xin (2023), “ImageWeeds: An Image dataset consisting of weeds in multiple formats to advance computer vision algorithms for real-time weed identification and spot spraying application”, Mendeley Data, V2, doi: 10.17632/8kjcztbjz2.2

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