v0.6.0

Breaking changes

• out: atomnet, atomstat
• in: models, trainers, predictors

To install and use the old code run pip install git+https://github.com/ziatdinovmax/atomai@legacy --upgrade

The new version provides an easy, Keras-like interface for training and applying models for semantic segmentation, image-to-spectrum conversion, as well as different forms of variational autoencoders. For example, to train a model for semantic segmentation of data, (for atom/defect finding) simply run:

model = Segmentor()
model.fit(X, y, X_test, y_test, training_cycles=300)

To make a prediction with a trained model, run:

output, coords = model.predict(expdata)

See the updated documentation for more details.

New functionalities:

• ImSpec models for converting 2D images to 1D spectra and vice versa
• Graph analysis for identifying topologcial defects in the lattices
• Class-conditioned VAE and rVAE

Imrovements:

• AtomAI's trainers and predictors can now work with custom Pytorch models