metadata
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Raw_Banana
'1': Raw_Mango
'2': Ripe_Banana
'3': Ripe_Mango
splits:
- name: train
num_bytes: 279368762.236
num_examples: 3999
- name: test
num_bytes: 35482482
num_examples: 1000
download_size: 390936312
dataset_size: 314851244.236
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- biology
pretty_name: fruit_ripeness_img
Dataset Card for Dataset Name
This is a collection of ripe and unripe fruits (mangoes and bananas) in outside lighting and outside conditions.
- Train - 80% (4k images)
- Test - 20% (1k images)
Dimensions of image : 640 x 480
The dataset has been collected from Mendeley data: https://data.mendeley.com/datasets/y3649cmgg6/3 (Mango and Banana Dataset (Ripe Unripe) : Indian RGB image datasets for YOLO object detection)
Initially the data was for training YOLO models. I have reorganized the data for training using datasets library in python for deep neural networks and transformers.
Dataset Details
Uses
This dataset is intended for image classification purpose.
Dataset Card Authors
Subhajit Chatterjee