xdep

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Screenshots

Accuracy & Loss Graphs	Explainability (LIME)

Overview

xdep is an integrated pipeline designed to simplify the development and evaluation of trustworthy computer vision models.
It provides an end-to-end framework for training, interpreting, and visualizing deep learning models with a focus on transparency and reliability.

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Features

• Easy setup and modular pipeline to build trustworthy computer vision models
• Integrated support for popular explainability techniques (e.g., LIME)
• Automated generation of evaluation reports and visualizations (CSV, figures)
• Lightweight and easy to extend for custom workflows
• Command-line interface (CLI) for quick experimentation and testing

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Installation

You can install the latest stable version from TestPyPI (for testing) or PyPI (for production):

pip install xdep

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Dependencies

xdep requires the following Python packages (automatically installed with pip):

• lime >= 0.2.0.1
• matplotlib
• pillow
• rich
• scikit-learn
• seaborn
• tensorflow

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Quick Start

Using as a Python Package

Here’s a minimal example of how to use xdep in your Python code:

from xdep import main

# Call the main pipeline function or your preferred API
main.main()

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Using the CLI

After installation, you can run the xdep command directly in your terminal:

xdep

This will show available commands and options.

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Directory & Output Handling

xdep saves generated CSV files and figures to a results directory in the current working directory by default.

• If the results directory does not exist, it will be created automatically.
• You can configure output directories and filenames via the API or CLI arguments.

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Development & Contribution

Contributions are welcome! Feel free to:

• Report issues or bugs
• Request features or improvements
• Submit pull requests with enhancements or fixes

Setup for Development

Clone the repo and install in editable mode:

git clone https://github.com/SAGAR-TAMANG/X-DEP
cd X-DEP
pip install -e .

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For questions, suggestions, or collaboration, reach out to:

Sagar Tamang Email: sagar.bdr0000@gmail.com GitHub: https://github.com/SAGAR-TAMANG

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Acknowledgements

• Thanks to the developers of LIME and other open-source libraries integrated with this package.
• Inspired by the need for trustworthy and interpretable AI models in computer vision.

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Happy modeling! 🚀