Datasets:

Project-AgML
/

riseholme_strawberry_classification_2021

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': anomalous
            '1': occluded
            '2': ripe
            '3': unripe
  splits:
    - name: train
      num_bytes: 28873401
      num_examples: 3520
  download_size: 27091521
  dataset_size: 28873401
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

Riseholme Strawberry Classification 2021

A dataset for image classification of Riseholme Strawberry Classification 2021. The dataset contains 3,520 images across 4 classes: anomalous, occluded, ripe, unripe.
Images per class:

anomalous: 153
occluded: 499
ripe: 462
unripe: 2,406

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

Citation

@inproceedings{CWSC21,
title={Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies},
author={Choi, Taeyeong and Would, Owen and Salazar-Gomez, Adrian and Cielniak, Grzegorz},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
pages={2266--2272},
year={2022},
organization={IEEE}
}

https://github.com/ctyeong/Riseholme-2021