Logo_watermark_detection

🛡️ VisionGuard AI: Logo & Watermark Detection Suite

VisionGuard is a production-ready system for the detection and removal of logos and watermarks from images, videos, and real-time feeds. Powered by YOLOv8 and built with a premium Streamlit UI.

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🚀 How to Run the Project

Follow these steps to get the system running perfectly on your local machine.

1. Prerequisites

Ensure you have Python 3.9+ installed. This project does not require Docker.

2. Setup Environment

Open your terminal (PowerShell on Windows or Bash on Mac/Linux) and run:

# Navigate to the project directory
cd watermarklogo

# Create a virtual environment
python -m venv .venv

# Activate the virtual environment
# On Windows:
.\.venv\Scripts\activate
# On Mac/Linux:
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

3. Generate Sample Data (Optional)

If you don't have a dataset yet, you can generate synthetic images to test the pipeline:

python scripts/generate_synthetic_dataset.py

4. Train the Model

To train the model on your dataset (or the synthetic one):

python src/train.py --data data.yaml --epochs 100 --imgsz 640 --batch 16

Note: A pre-trained model will be saved in models/best.pt.

5. Launch the Premium Dashboard

This is the main application interface.

streamlit run app/app.py

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🛠️ Advanced Usage

Run a Detection Demo

To quickly verify that the detection logic is working perfectly:

python scripts/run_demo.py

Check the output in the outputs/ folder.

Evaluate Performance

To see metrics like mAP, Precision, and Recall:

python src/evaluate.py --weights models/best.pt --data data.yaml

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📂 Project Structure

• app/: Premium Streamlit UI.
• src/: Core detection, training, and removal logic.
• scripts/: Helper scripts for data generation and demos.
• models/: Stores your trained .pt weights.
• dataset/: Your images and labels.

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© 2026 VisionGuard AI | Built with YOLOv8 & Streamlit