Datasets:

Project-AgML
/

coffee_detection

dataset_info:
  features:
    - name: image
      dtype: image
    - name: batch
      dtype: string
    - name: objects
      struct:
        - name: bbox
          list:
            list: float64
        - name: categories
          list:
            class_label:
              names:
                '0': unripe
                '1': ripening
                '2': ripe
                '3': spoiled
                '4': coffee_tree
  splits:
    - name: train
      num_bytes: 740794993
      num_examples: 3254
  download_size: 1632957689
  dataset_size: 740794993
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

Coffee Detection

A dataset for object detection of coffee beans. The dataset contains 3,254 images with 126,840 bounding box annotations across 5 categories.

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

Citation

@article{sanya2024coffee,
  title={Coffee and cashew nut dataset: A dataset for detection, classification, and yield estimation for machine learning applications},
  author={Sanya, Rahman and Nabiryo, Ann Lisa and Tusubira, Jeremy Francis and Murindanyi, Sudi and Katumba, Andrew and Nakatumba-Nabende, Joyce},
  journal={Data in Brief},
  volume={52},
  pages={109952},
  year={2024},
  publisher={Elsevier}
}

Nakatumba-Nabende, Joyce; Katumba, Andrew; Sanya, Rahman; Tusubira, Jeremy; Murindanyi, Sudi; Namanya, Gloria; Nabiryo, Ann (2023), “Coffee and Cashew Nut Dataset”, Mendeley Data, V1, doi: 10.17632/r46c6bpfpf.1

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