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--- |
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license: cc-by-nc-4.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- image-segmentation |
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- image-classification |
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pretty_name: RaspGrade |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: labels |
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sequence: |
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sequence: float64 |
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- name: image_id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 208837995 |
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num_examples: 160 |
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- name: valid |
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num_bytes: 52068619 |
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num_examples: 40 |
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download_size: 242513653 |
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dataset_size: 260906614 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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tags: |
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- food |
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- foodquality |
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--- |
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--- |
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# 🍓 The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning |
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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. |
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To address this, a dedicated dataset of raspberries, namely RaspGrade, was acquired and meticulously annotated. |
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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 |
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🤗 [Paper on Hugging Face](https://huggingface.co/papers/2505.08537) | 📝 [Paper on ArXiv](https://arxiv.org/abs/2505.08537) |
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## 🗂️ Data Instances |
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<figure style="display:flex; gap:10px; flex-wrap:wrap; justify-content:center;"> |
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<img src="1.png" width="45%" alt="Raspberry Example 1"> |
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<img src="3.png" width="45%" alt="Raspberry Example 2"> |
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</figure> |
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## 🏷️ Annotation Format |
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Note that the annotations follow the YOLO instance segmentation format. |
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Please refer to [this page](https://docs.ultralytics.com/datasets/segment/) for more info. |
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## 🙏 Acknowledgement |
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<style> |
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.list_view{ |
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display:flex; |
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align-items:center; |
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} |
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|
.list_view p{ |
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|
padding:10px; |
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} |
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</style> |
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<div class="list_view"> |
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<a href="https://agilehand.eu/" target="_blank"> |
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<img src="AGILEHAND.png" alt="AGILEHAND logo" style="max-width:200px"> |
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</a> |
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<p style="line-height: 1.6;"> |
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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. |
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</p> |
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</div> |
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## 🤝 Partners |
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<div style="display: flex; flex-wrap: wrap; justify-content: center; gap: 40px; align-items: center;"> |
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<a href="https://www.fbk.eu/en" target="_blank"><img src="FBK.jpg" width="180" alt="FBK logo"></a> |
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<a href="https://www.santorsola.com/" target="_blank"><img src="Santorsola.jpeg" width="250" alt="Santorsola logo"></a> |
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</div> |
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## 📖 Citation |
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```bibtex |
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@misc{mekhalfi2025raspgradedatasetautomaticraspberry, |
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title={The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning}, |
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author={Mohamed Lamine Mekhalfi and Paul Chippendale and Fabio Poiesi and Samuele Bonecher and Gilberto Osler and Nicola Zancanella}, |
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year={2025}, |
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eprint={2505.08537}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2505.08537}, |
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} |
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``` |
