Metadata-Version: 2.1
Name: atomvision
Version: 2022.10.23
Summary: atomvision
Home-page: https://github.com/usnistgov/atomvision
Author: Kamal Choudhary, Brian DeCost
Author-email: kamal.choudhary@nist.gov
License: UNKNOWN
Description: 
        [![name](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/AtomVisionExample.ipynb)
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        # Atomvision
        
        
        # Table of Contents
        * [Introduction](#intro)
        * [Installation](#install)
        * [Examples](#example)
        * [Reference](#reference)
        * [How to contribute](#contrib)
        * [Correspondence](#corres)
        * [Funding support](#fund)
        
        
        <a name="intro"></a>
        Introduction
        -------------------------
        Atomvision is a deep learning framework for atomistic image data
        
        
        <a name="install"></a>
        Installation
        -------------------------
        First create a conda environment:
        Install miniconda environment from https://conda.io/miniconda.html
        Based on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'.
        
        Now,
        
        ```
        bash Miniconda3-latest-Linux-x86_64.sh (for linux)
        bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac)
        ```
        Download 32/64 bit python 3.6 miniconda exe and install (for windows)
        Now, let's make a conda environment, say "version", choose other name as you like::
        ```
        conda create --name vision python=3.8
        source activate vision
        ```
        
        Now, let's install the package:
        
        #### Method 1 (using setup.py):
        
        ```
        git clone https://github.com/usnistgov/atomvision.git
        cd atomvision
        python setup.py develop
        ```
        
        #### Method 2 (using pypi):
        
        As an alternate method, AtomVision can also be installed using `pip` command as follows:
        ```
        pip install atomvision
        ```
        
        <a name="example"></a>
        Examples
        ---------
        
        #### 2D-Bravais lattice classification example
        This example shows how to classify 2D-lattice (5 Bravais classes) for 2D-materials STM/STEM images.
        
        We will use images``sample_data`` folder. It was generated with ``generate_stem.py`` script. There are  two folders ``train_folder``, ``test_folder`` with sub-folders ``0,1,2,3,4,...`` for individual classes and they contain images for these classes.
        
        ```
        train_classifier_cnn.py --model densenet --train_folder atomvision/sample_data/test_folder --test_folder atomvision/sample_data/test_folder --epochs 5 --batch_size 16
        ```
        
        
        #### Generating a t-SNE  plot
        
        ```
        train_tsne.py --data_dir atomvision/sample_data/test_folder
        ```
        
        #### Generative Adversarial Network
        
        ```
        train_gan.py --dataset_path atomvision/sample_data/test_folder/0 --epochs 2
        ```
        
        #### Autoencoder
        
        ```
        train_autoencoder.py --train_folder atomvision/sample_data/test_folder --test_folder atomvision/sample_data/test_folder --epochs 10
        ```
        
        
        <a name="reference"></a>
        Reference
        ---------
        
        1) [The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design](https://www.nature.com/articles/s41524-020-00440-1)
        
        2) [Computational scanning tunneling microscope image database](https://www.nature.com/articles/s41597-021-00824-y)
        
        Please see detailed publications list [here](https://jarvis-tools.readthedocs.io/en/master/publications.html).
        
        <a name="contrib"></a>
        How to contribute
        -----------------
        
        For detailed instructions, please see [Contribution instructions](https://github.com/usnistgov/jarvis/blob/master/Contribution.rst)
        
        <a name="corres"></a>
        Correspondence
        --------------------
        
        Please report bugs as Github issues (https://github.com/usnistgov/atomvision/issues) or email to kamal.choudhary@nist.gov.
        
        <a name="fund"></a>
        Funding support
        --------------------
        
        NIST-MGI (https://www.nist.gov/mgi).
        
        Code of conduct
        --------------------
        
        Please see [Code of conduct](https://github.com/usnistgov/jarvis/blob/master/CODE_OF_CONDUCT.md)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
