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README.md

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-license: cc-by-4.0
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+---
+tags:
+- computer-vision
+- object-detection
+- yolo
+- fruits
+library_name: ultralytics
+license: cc-by-4.0
+datasets:
+- Johnatanvq/fruitsdata
+---
+
+# 🍎🥕🍊 Fruits Detection Models (YOLOv11 + OAK Deployment)
+
+This repository provides two versions of a YOLO-based model trained to detect **apples, carrots, and oranges**.  
+The models were trained on the [Fruits Dataset](https://huggingface.co/datasets/johnatanvq/fruits-dataset), which contains **160 annotated images** with variations in **angles, distances, lighting, shadows, quantities, and surfaces**.
+
+---
+
+## 📂 Repository Structure
+
+fruits-yolo-model/ </br>
+├── my_model_PC/ </br>
+│ └── my_model.pt </br>
+└── my_model_CAMERA/ </br>
+├── my_model_openvino_2022.1_6shave.blob </br>
+├── my_model-simplified.onnx </br>
+├── my_model.bin </br>
+├── my_model.xml </br>
+└── my_model.json </br>
+---
+
+## 🧾 Training & Conversion
+Training: The model was trained with the Fruits Dataset.
+
+Conversion: The .pt weights were exported to ONNX and then converted via Luxonis tools into the .blob format for OAK deployment.
+
+Source Code: Training scripts and conversion pipeline are documented here:
+👉 GitHub: [fruit_detection_model](https://github.com/Johnatanvq/fruit_detection_model)  
+
+## 📑 License
+This model is released under the CC-BY 4.0 license.
+You are free to share, use, and adapt the models, including for commercial purposes, as long as you provide proper attribution.
+
+## Attribution
+If you use these models, please cite them as:
+
+*Fruits Detection Models (YOLOv11 + OAK Deployment), by **Johnatanvq**, trained on the Fruits Dataset, licensed under CC-BY 4.0.*
+
+## 📝 Notes
+The dataset is compact (160 images) but provides strong variation for robust training.
+
+my_model.pt is suitable for PyTorch inference and further training.
+
+my_model_openvino_2022.1_6shave.blob is optimized for real-time inference on OAK devices.
+
+Supporting files (.onnx, .bin, .xml, .json) are included for reproducibility.