Datasets:
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
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
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/RobotIX-Lab/fruit_quality
cd vtuber_emojis