| | --- |
| | 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. |
