---
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.*