🍏 Apple Detection with YOLOv8 🚀

This repository contains an Apple Detection project using YOLOv8 segmentation. The model is trained on a custom dataset and utilizes the Ultralytics YOLOv8 framework for instance segmentation.

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📌 Installation

Ensure you have Python 3.11 or later installed. Then, install the necessary dependencies:

pip install -r requirements.txt

Alternatively, install dependencies manually:

pip install opencv-python==4.8.0.76 ultralytics==8.0.20 google-colab==1.0.0

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🏋️‍♂️ Training the Model

1️⃣ Mount Google Drive

Store your dataset in Google Drive and mount it in Colab:

from google.colab import drive
drive.mount('/content/drive')

2️⃣ Extract the Dataset

import zipfile, os
zip_file_path = "/content/drive/MyDrive/Appel/Appledetect.v4i.yolov8.zip"
extracted_dir_path = "/content/extracted"
os.makedirs(extracted_dir_path, exist_ok=True)
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
    zip_ref.extractall(extracted_dir_path)

3️⃣ Train the YOLOv8 Model

yolo task=segment mode=train model=yolov8x-seg.pt data=/content/extracted/data.yaml epochs=50 imgsz=640 plots=True

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💾 Saving Training Results

After training, save the results to Google Drive:

import shutil
source_path = "/content/extracted/runs/segment/train"
destination_path = "/content/drive/MyDrive/Appel/train"
shutil.copytree(source_path, destination_path, dirs_exist_ok=True)
print("✅ Training results copied successfully.")

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🔧 Usage

• Modify data.yaml to specify the correct dataset paths.
• Train for more epochs or use different models (yolov8m-seg.pt, yolov8l-seg.pt) for better results.

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📜 License

This project is open-source and available under the MIT License.

🔹 Happy Coding & Happy Detecting! 🎯

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