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