| --- |
| pretty_name: Fruits (Apples, Carrots, Oranges) – YOLO Annotations |
| tags: |
| - computer-vision |
| - object-detection |
| - yolo |
| - fruits |
| task_categories: |
| - object-detection |
| annotations_creators: |
| - expert-generated |
| language: |
| - en |
| license: cc-by-4.0 |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 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. |
|
|
| --- |
|
|
| ## Structure |
|
|
| The dataset is organized under the `fruitsdata/` folder: |
|
|
| fruitsdata/ </br> |
| ├── images/ # original fruit photos (.jpg) </br> |
| ├── labels/ # YOLO annotation files (.txt, one per image) </br> |
| ├── classes.txt # class list (apple, carrot, orange) </br> |
| └── notes.json # dataset metadata and notes </br> |
| --- |
|
|
| ## 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](https://github.com/Johnatanvq/fruit_detection_model) |
|
|
| ### 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`. |
|
|
| --- |
|
|
| ## Annotation Format (YOLO) |
|
|
| Each line in `labels/*.txt` follows: |
| class_id x_center y_center width height |
| |
| --- |
| |
| ## Classes |
| |
| 1. apple |
| 2. carrot |
| 3. orange |
| |
| --- |
| |
| ## License |
| |
| This dataset is released under the [CC-BY 4.0](https://creativecommons.org/licenses/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. |
| |
| --- |
| |
| ## 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. |
