Datasets:
File size: 1,990 Bytes
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dataset_info:
- config_name: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Fresh
'1': Rotten
splits:
- name: train
num_bytes: 5480864904
num_examples: 6000
download_size: 3014334523
dataset_size: 5480864904
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Fresh
'1': Rotten
splits:
- name: train
num_bytes: 156301454
num_examples: 1986
download_size: 149944018
dataset_size: 156301454
configs:
- config_name: augmented
data_files:
- split: train
path: augmented/train-*
- config_name: raw
data_files:
- split: train
path: raw/train-*
default: true
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Tomato Quality Classification
A dataset for quality classification of Tomatoes. The dataset contains raw and augmented versions.
The raw dataset contains 1,986 images.
Images per class:
- Fresh: 1,350
- Rotten: 636
The augmented dataset contains 6,000 images.
Images per class:
- Fresh: 3,000
- Rotten: 3,000
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{khatun2023extensive,
title={An extensive real-world in field tomato image dataset involving maturity classification and recognition of fresh and defect tomatoes},
author={Khatun, Tania and Razzak, Abdur and Islam, Md Shofiul and Uddin, Mohammad Shorif},
journal={Data in Brief},
volume={51},
pages={109688},
year={2023},
publisher={Elsevier}
}
```
Khatun, Tania; Razzak, Abdur ; Islam, Md. Shofiul ; Uddin, Prof. Dr. Mohammad Shorif (2023), “Tomato Maturity Detection and Quality Grading Dataset”, Mendeley Data, V1, doi: 10.17632/s42kpg8h37.1
*This dataset was reformatted from its original format to match HuggingFace standards.* |