Skip to main content

An integrated pipeline for developing and evaluating trustworthy computer vision models.

Project description

xdep

PyPI version Python Version License


Screenshots

screenshot of cmd terminal
Accuracy & Loss Graphs Explainability (LIME)
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.


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

Installation

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

pip install xdep

Dependencies

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

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

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()

Using the CLI

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

xdep

This will show available commands and options.


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.

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 .

License

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


Contact

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

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


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.

Happy modeling! 🚀

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xdep-0.1.4.tar.gz (12.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

xdep-0.1.4-py3-none-any.whl (10.7 kB view details)

Uploaded Python 3

File details

Details for the file xdep-0.1.4.tar.gz.

File metadata

  • Download URL: xdep-0.1.4.tar.gz
  • Upload date:
  • Size: 12.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.3

File hashes

Hashes for xdep-0.1.4.tar.gz
Algorithm Hash digest
SHA256 ddd74b450c508853d16d750d9902efa73fc2c8e5387869d333979c87ab4b8a7c
MD5 f1a4789e43e03445d11e75c8dfadf376
BLAKE2b-256 150073dd3206f84c1d9fbde78c7c1bd40505a785fb85d96fdca2f3b11a9905f8

See more details on using hashes here.

File details

Details for the file xdep-0.1.4-py3-none-any.whl.

File metadata

  • Download URL: xdep-0.1.4-py3-none-any.whl
  • Upload date:
  • Size: 10.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.3

File hashes

Hashes for xdep-0.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 31d8269204abc38a09e0273d1b85855555a1565d2991c9c9419ffd1017ae779c
MD5 702cdbd2a12f6f7e4f6dbe9094e30a2f
BLAKE2b-256 913d900c97e4522617c3cf48f980362f591396e086fa1253ef01ef45163ebc6e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page