johnatanvq/fruits-dataset

Fruits Dataset (Apples / Carrots / Oranges)

This dataset contains 160 original images of apples, carrots, and oranges, captured in different scenarios.
The pictures include variations in angles, distances, lighting conditions, shadows, quantities, and surfaces, providing dynamic and diverse samples for training.

Annotations were created using Label Studio and are formatted for direct use with YOLO object detection models.

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Structure

The dataset is organized under the fruitsdata/ folder:

fruitsdata/
├── images/ # original fruit photos (.jpg)
├── labels/ # YOLO annotation files (.txt, one per image)
├── classes.txt # class list (apple, carrot, orange)
└── notes.json # dataset metadata and notes

How to Use

Option A — Use my notebook (recommended)

1. Download this dataset.
2. Run the Jupyter Notebook available on GitHub, which performs train/val splitting and training:
   👉 Fruit Detection Model with YOLO

Option B — Manual usage

If you want to manually prepare a YOLO-compatible dataset, split images/ and labels/ into train/ and val/, then create a dataset.yaml.

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Annotation Format (YOLO)

Each line in labels/*.txt follows: class_id x_center y_center width height

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Classes

1. apple
2. carrot
3. orange

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License

This dataset is released under the CC-BY 4.0 license.
You are free to share, use, and adapt the dataset, including for commercial purposes, as long as you provide appropriate attribution.

Copyright & Attribution

The images and annotations are original work created by the author.

If you use this dataset, please cite it as:

Fruits (Apples/Carrots/Oranges) – YOLO Annotations, by Johnatanvq, licensed under CC-BY 4.0.

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Notes

• The dataset is intentionally compact (160 images) but highly varied.
• Designed for quick prototyping and benchmarking object detection models.
• Optimized for YOLO but can be adapted to other frameworks.