Atomvision is a deep learning framework for atomistic image data.
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:
git clone https://github.com/usnistgov/atomvision.git
cd atomvision
python setup.py develop
As an alternate method, AtomVision can also be installed using pip command as follows:
pip install atomvision
stem_conv.py --file_path atomvision/tests/POSCAR --output_path STEM.png
This example shows how to classify 2D-lattice (5 Bravais classes) for 2D-materials STM/STEM images.
We will use imagessample_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
train_tsne.py --data_dir atomvision/sample_data/test_folder
train_gan.py --dataset_path atomvision/sample_data/test_folder/0 --epochs 2
train_autoencoder.py --train_folder atomvision/sample_data/test_folder --test_folder atomvision/sample_data/test_folder --epochs 10
Please see detailed publications list here.
For detailed instructions, please see Contribution instructions
Please report bugs as Github issues (https://github.com/usnistgov/atomvision/issues) or email to kamal.choudhary@nist.gov.
NIST-MGI (https://www.nist.gov/mgi).
Please see Code of conduct