Metadata-Version: 2.1
Name: Robustness-Framework
Version: 0.0.2
Summary: An efficient framework for establishing a baseline for standard and adversarial machine learning training projects 
Home-page: https://github.com/khalooei/robustness-framework
Author: Mohammad KHalooei
Author-email: mkhalooei90@gmail.com
License: UNKNOWN
Description: # Robustness Framework
        
        <div align="center">
        
        <img alt="Robustness Framework" src="robustness-framework.jpg" width="800px" style="max-width: 100%;">
        
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        <br/>
        
        **A robustness framework for baseline of standard and adversarial machine learning research projects**
        
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pytorch-lightning)](https://pypi.org/project/pytorch-lightning/)
        [![PyPI Status](https://badge.fury.io/py/pytorch-lightning.svg)](https://badge.fury.io/py/pytorch-lightning)
        [![Conda](https://img.shields.io/conda/v/conda-forge/lightning?label=conda&color=success)](https://anaconda.org/conda-forge/lightning)
        [![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)  
        
        </div>
        
        The robustness framework is based on top-tier machine learning libraries based on Pytorch. Additionally, it allows for embedding different model architectures and training processes in addition to fixing all research issues.
        This framework integrates the following libraries:
         * Pytorch-Lightning
         * Hydra
         * torchattacks
        
        The following architectures are covered in this framework and other networks will be added as needed:
         * MKToyNet
         * LeNet
         * DenseNet
         * ResNet
         * WideResNet
        
        The logging system of this framework is also customizable and resolves all your ideas. As a result, we can take advantage of the efficiency of the following libraries:
         * TorchMetrics 
         * Loggings
         * Neptune
         * Comet
         * MLFlow
         * ...
        
        ## Installation
        To install this interesting framework for standard and adversarial machine learning, follow the steps below, and don't waste your time developing an efficient baseline.
        For installing robustness framework, we have two approaches:
        
        ### 1- Automatic installation
         `pip install robustness-framework'
        
        ### 1- Manual installation
         `git clone ....`
         `pip install -r requirements.txt'
        
        ## Usage
        You can just follow the `main.py` file as a main anchor of this framework. You can define your own configurations in `configs` directory as we defined `training_mnist.yaml` and `training_cifar10.yaml` configuration. 
        You can run this framework for running on CIFAR10 dataset as below:
         ```
           python main.py +configs=training_cifar10
         ```
        
        [TOBE COMPLETED]
        
        
        ## Acknowledgements
        Thanks to the people behind Pytorch, Lightning, torchattacks hydra, and MLOps libraries whose work inspired this repository. Furthermore, I would like to thank my supervisors Prof. Mohammad Mehdi Homayounpour and Dr. Maryam Amirmazlagnani for their efforts and guidance.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
