Felix-ML

A machine learning approach to diffraction patterns.
Check out the webpage to see documentation and research!

Description

Felix-ML is able to predict and analyse LACBED diffraction patterns for defining crystal structures.

Installation

Pip

# clone project
git clone https://github.com/wephy/felix-ml
cd felix-ml

# [OPTIONAL] create conda environment
conda create -n myenv python=3.9
conda activate myenv

# install pytorch according to instructions
# https://pytorch.org/get-started/

# install requirements
pip install -r requirements.txt

Conda

# clone project
git clone https://github.com/wephy/felix-ml
cd felix-ml

# create conda environment and install dependencies
conda env create -f environment.yaml -n myenv

# activate conda environment
conda activate myenv

How to run

Train model with default configuration

# train on CPU
python src/train.py trainer=cpu

# train on GPU
python src/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

python src/train.py experiment=experiment_name.yaml

You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 data.batch_size=64

Create a sweep over hyperparameters

python train.py -m data.batch_size=32,64,128 model.lr=0.001,0.0005