Datasets:
File size: 1,301 Bytes
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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 |