Wheat Detection with YOLO11n 🌾

A project for detecting wheat heads in images using the YOLO11n model from Ultralytics. This repository includes dataset setup, model training, and inference with visualization for the GlobalWheat2020 dataset. 🚀

📋 Table of Contents

• Overview
• Features
• Installation
• Dataset
• Usage
• Training
• Inference
• Results
• Contributing
• License

🌟 Overview

This project leverages the power of the YOLO11n model to detect wheat heads in agricultural images. It uses the GlobalWheat2020 dataset, automates dataset organization, trains a model, and performs inference with visualized bounding boxes. Perfect for computer vision enthusiasts and agricultural tech researchers! 📸

✨ Features

• 🛠️ Installs Ultralytics for YOLO model support
• 📂 Downloads and organizes the GlobalWheat2020 dataset
• ⚙️ Trains YOLO11n for wheat head detection
• 🖼️ Visualizes detection results with bounding boxes
• 🔄 Supports further training and inference

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/shahin-ro/wheat-detection.git
   cd wheat-detection

2. Install dependencies:

   pip install ultralytics

Note: This project is designed to run in a Google Colab environment for easy access to GPU resources. Adjust paths if running locally.

📊 Dataset

The project uses the GlobalWheat2020 dataset. The script:

• Downloads images and annotations
• Organizes them into images, annotations, and labels directories
• Creates a GlobalWheat2020_subset.yaml file for training and validation subsets

🚀 Usage

1. Setup: Run the dataset setup code to download and organize the GlobalWheat2020 dataset.
2. Train: Train the YOLO11n model using the provided YAML configuration.
3. Infer: Upload an image and detect wheat heads with visualized results.

🏋️‍♂️ Training

Train the model with the following command in the script:

model = YOLO("yolo11n.pt")
model.train(data="GlobalWheat2020_subset.yaml", epochs=5, imgsz=640)

• Adjust epochs or imgsz for better results.
• Trained weights are saved in /content/runs/detect/train/weights/best.pt.

🔍 Inference

Perform inference on a new image:

model = YOLO("/content/runs/detect/train/weights/best.pt")
results = model("path/to/your/image.jpg")
for result in results:
    img = result.plot()
    plt.imshow(img)
    plt.axis('off')
    plt.show()

Upload an image in Colab to see bounding boxes around detected wheat heads.

📈 Results

The model outputs bounding boxes around wheat heads in the input images. Results are visualized using Matplotlib, showing detected wheat heads with confidence scores.

🤝 Contributing

Contributions are welcome! 🌟

1. Fork the repository
2. Create a feature branch (git checkout -b feature/YourFeature)
3. Commit changes (git commit -m 'Add YourFeature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a Pull Request

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🌾 Happy wheat detection! If you find this project useful, give it a ⭐ on GitHub!