|
--- |
|
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: area |
|
dtype: int64 |
|
- 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. |