| --- |
| 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 |
|
|
| ```bibtex |
| @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 |
