Welcome to AtomAI’s documentation!

Notes

• ReadMe
  • What is AtomAI
    • Why AtomAI
  • How to use it
    • Semantic segmentation
    • ImSpec models
    • Deep ensembles
    • Variational autoencoders (VAE)
    • Custom models
    • Not just deep learning
  • Installation
• LICENSE
• Scientific output

Package Content

• AtomAI Models
  • Segmentor
  • ImSpec
  • Variational Autoencoder (VAE)
  • Rotational Variational Autoencoder (rVAE)
  • Joint Variational Autoencoder (jVAE)
  • Joint Rotational Variational Autoencoder (jrVAE)
  • Deep Kernel Learning
  • Load trained models
• Trainers and Predictors
  • Single Model Trainers
    • Base Trainer Class
    • Trainer for Semantic Segmentation
    • ImSpec Trainer
    • Variational Inference Trainer
  • Deep Ensemble Trainers
    • Ensemble Trainer
  • Single Model Predictors
    • BasePredictor
    • SegPredictor
    • ImSpecPredictor
  • Deep Ensemble Predictors
    • Ensemble Predictor
• Neural Nets
  • Fully convolutional neural networks
  • Neural Networks for VAE
  • Neural Networks for ImSpec
  • Building blocks
• Loss Functions and Metrics
  • Losses for imbalanced classes
  • Metics for imbalanced classes
• Other utilities
  • Statistics
  • Image transforms
  • Training data preparation
  • Image pre/post processing
  • Atomic Coordinates
  • Visualization
  • ASE utilities
  • Datasets

Examples

• Colab notebooks