Datasets:
File size: 1,729 Bytes
3b28fe4 5c7ae79 3b28fe4 5c7ae79 3b28fe4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | ---
configs:
- config_name: raw
default: true
data_dir: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Formalin-mixed
'1': Fresh
'2': Rotten
- name: fruit
dtype: string
- config_name: augmented
data_dir: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Formalin-mixed
'1': Fresh
'2': Rotten
- name: fruit
dtype: string
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# FruitVision Quality Classification
A dataset for quality classification of apples, bananas, mangoes, grapes, and oranges. The dataset contains raw and augmented versions.
The raw dataset contains 10,154 images.
Images per class:
- Formalin-mixed: 3,176
- Fresh: 3,800
- Rotten: 3,178
The augmented dataset contains 73,389 images.
Images per class:
- Formalin-mixed: 22,228
- Fresh: 30,400
- Rotten: 20,761
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{bijoy2025fruitvision,
title={FruitVision: A benchmark dataset for fresh, rotten, and formalin-mixed fruit detection},
author={Bijoy, Md Hasan Imam and Tasnim, Syeda Zarin and Awsaf, Syed Ali and Hasan, Md Zahid},
journal={Data in Brief},
volume={61},
pages={111752},
year={2025},
publisher={Elsevier}
}
```
Bijoy, Md Hasan Imam; Tasnim, Syeda Zarin; Awsaf, Syed Ali; Hasan, Md Zahid (2025), “FruitVision: A Benchmark Dataset for Fresh, Rotten, and Formalin-mixed Fruit Detection”, Mendeley Data, V2, doi: 10.17632/xkbjx8959c.2 |