task_categories:object-detection language:en license:unknown size_categories:n<1K format:parquet modality:image library:datasets library:pandas library:mlcroissant library:polars region:us
---
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.