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Datasets:
RobotIX-Lab
/
fruit_quality

Tasks:
Image Classification
Modalities:
Image
Formats:
arrow
Languages:
English
Size:
10K - 100K
Tags:
fruit
vegetable
quality
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
fruit_quality
File size: 2,229 Bytes
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---
license: mit
task_categories:
- image-classification
language:
- en
tags:
- fruit
- vegetable
- quality
pretty_name: Fruit and Vegetable Quality Dataset
size_categories:
- 10K<n<100K
dataset_info:
- config_name: default
  features:
  - name: image
    dtype: image
  - name: quality
    dtype:
      class_label:
        names:
          '0': fresh
          '1': rotten
  - name: category
    dtype:
      class_label:
        names:
          '0': apples
          '1': banana
          '2': cucumber
          '3': okra
          '4': oranges
          '5': potato
          '6': tomato
  splits:
  - name: train
    num_bytes: 3125926
    num_examples: 13355
  - name: validation
    num_bytes: 677903
    num_examples: 2857
  - name: test
    num_bytes: 668998
    num_examples: 2867
  download_size: 2258968777
  dataset_size: 4472827
configs:
- config_name: default
  data_files:
  - split: train
    path: default/train/data-*.arrow
  - split: validation
    path: default/validation/data-*.arrow
  - split: test
    path: default/test/data-*.arrow
---

# Intro
The Fruit and Vegetable Quality Dataset is a multi‑category image dataset designed for quality classification and produce recognition tasks. It contains over 19,000 images across seven fruit and vegetable types (apples, bananas, cucumbers, okra, oranges, potatoes, and tomatoes), each annotated with a binary quality label (fresh or rotten). The dataset is split into training (13,355 samples), validation (2,857), and test (2,867) sets, providing a standardized benchmark for developing and evaluating computer vision models in agricultural quality inspection. With an MIT license and a size range of 10K to 100K samples, the dataset supports academic and industrial research in tasks such as defect detection, quality grading, and species identification.

## Usage
```python
from datasets import load_dataset

ds = load_dataset(
    "RobotIX-Lab/fruit_quality",
    name="default",
    split="train",
    cache_dir="./__pycache__",
)
for i in ds:
    print(i)
```

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/RobotIX-Lab/fruit_quality
cd vtuber_emojis
```

## Mirror
<https://modelscope.cn/datasets/RobotIX/fruit_quality>