Acne Detection Using YOLOv8 🧠📸

This project detects acne on facial images using the YOLOv8 object detection model by Ultralytics. It is trained on a custom dataset formatted in YOLO style and can be run on CPU or GPU.

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🚀 Features

• Acne detection using YOLOv8
• Custom labeled dataset
• Simple installation & setup
• Jupyter Notebook based training
• Works on Windows / Linux / macOS

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📂 Project Structure

Acne-Detection-Using-YOLOv8/ ├── data-2/ │ ├── train/ │ │ ├── images/ │ │ └── labels/ │ ├── valid/ │ │ ├── images/ │ │ └── labels/ │ ├── test/ │ │ ├── images/ │ │ └── labels/ │ └── data.yaml ├── acne-detection-using-yolo-v8.ipynb ├── requirements.txt └── README.md

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🛠 Requirements

• Python 3.8 – 3.11
• pip
• Git
• (Optional) NVIDIA GPU + CUDA

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⬇️ Installation

1️⃣ Clone Repository

git clone https://github.com/sufiinamulhassan/Acne-Detection-Using-YOLOv8.git cd Acne-Detection-Using-YOLOv8

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2️⃣ Create Virtual Environment (Recommended)

Windows: python -m venv venv venv\Scripts\activate

Linux / macOS: python3 -m venv venv source venv/bin/activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

If YOLOv8 is not installed: pip install ultralytics

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📊 Dataset Configuration

data.yaml example:

train: ../train/images val: ../valid/images test: ../test/images

nc: 1 names: ['Acne']

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🚀 Training

Open Jupyter Notebook:

jupyter notebook

Open: acne-detection-using-yolo-v8.ipynb

Run all cells to train the model.

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🔍 Inference

from ultralytics import YOLO

model = YOLO("runs/detect/train/weights/best.pt") model.predict(source="image.jpg", save=True)

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📈 Output

• Bounding boxes on acne areas
• Confidence scores
• Saved prediction images

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🧠 Model

YOLOv8 (Ultralytics)

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👤 Author

Inam Ul Hassan
BSCS Student | AI/ML Engineer | Data Scientist
Lahore, Pakistan

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🌐 Connect with Me

⭐ Support

Star ⭐ the repository if you find it helpful!