Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+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/
+├── 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
+
+yaml
+Copiar código
+
+---
+
+## 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.