🍎🥕🍊 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, which contains 160 annotated images with variations in angles, distances, lighting, shadows, quantities, and surfaces.

📂 Repository Structure

fruits-yolo-model/
├── my_model_PC/
│ └── my_model.pt
└── my_model_CAMERA/
├── my_model_openvino_2022.1_6shave.blob
├── my_model-simplified.onnx
├── my_model.bin
├── my_model.xml
└── my_model.json

🧾 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

📑 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.