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---
license:
- unknown
task_categories:
- object-detection
language:
- en
pretty_name: FriutDetection
size_categories:
- n<1K
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': Apple
'1': Banana
'2': Orange
splits:
- name: train
num_examples: 240
- name: test
num_examples: 60
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# [Fruit Images for Object Detection](https://www.kaggle.com/datasets/mbkinaci/fruit-images-for-object-detection)
Download from Kaggle datasets.
## About Dataset
### Project
This dataset is the data used in this project.
### Context
A different dataset for object detection. 240 images in train folder. 60 images in test folder.
### Content
3 different fruits:
- Apple
- Banana
- Orange
### Acknowledgements
`.xml` files were created with LabelImg. It is super easy to label objects in images. |