license: cc-by-nc-4.0
size_categories:
- 1K<n<10K
task_categories:
- image-segmentation
- image-classification
pretty_name: RaspGrade
dataset_info:
features:
- name: image
dtype: image
- name: labels
sequence:
sequence: float64
- name: image_id
dtype: string
splits:
- name: train
num_bytes: 208837995
num_examples: 160
- name: valid
num_bytes: 52068619
num_examples: 40
download_size: 242513653
dataset_size: 260906614
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
tags:
- food
- foodquality
🍓 The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning
This research investigates the application of computer vision for rapid, accurate, and non-invasive food quality assessment, focusing on the novel challenge of real-time raspberry grading into five distinct classes within an industrial environment as the fruits move along a conveyor belt. To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated.
Instance segmentation experiments revealed that accurate fruit-level masks can be obtained; however, the classification of certain raspberry grades presents challenges due to color similarities and occlusion, while others are more readily distinguishable based on color
🤗 Paper on Hugging Face | 📝 Paper on ArXiv
🗂️ Data Instances
🏷️ Annotation Format
Note that the annotations follow the YOLO instance segmentation format.
Please refer to this page for more info.
🙏 Acknowledgement
This work is supported by European Union’s Horizon Europe research and innovation programme under grant agreement No 101092043, project AGILEHAND (Smart Grading, Handling and Packaging Solutions for Soft and Deformable Products in Agile and Reconfigurable Lines.
🤝 Partners
📖 Citation
@misc{mekhalfi2025raspgradedatasetautomaticraspberry,
title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning},
author={Mohamed Lamine Mekhalfi and Paul Chippendale and Fabio Poiesi and Samuele Bonecher and Gilberto Osler and Nicola Zancanella},
year={2025},
eprint={2505.08537},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.08537},
}