Exploring deep learning models for 4D-STEM-DPC data processing

Prerequisites The scripts presented below require certain open-source Python packages to run. Library versions used to run the scripts are: hyperspy 1.7.1 pyxem 0.14.2 fpd 0.2.5 pytorch 1.12.1 (cudatoolkit 11.6.0) jupyterlab 4.0.7 Data files Three zipped folders are included. Two of them contain the training- and inference data for the neural networks, aptly named training_data.zip and inference_data.zip. PyTorch state dictionaries for trained models are included in the models.zip folder. Processing scripts All scripts are included in an IPython notebook format (.ipynb extension). The notebooks Segmentation.ipynb and Regression.ipynb contain the code for training and inference of the segmentation and regression models, respectively. The Training_data_creation.ipynb notebook contains the code to preprocess the training data for both neural network models. The Standard_algorithms.ipynb notebook has the code for doing center of mass and edge filtering/disc detection algorithms for STEM-DPC processing.