v0.6.2

New functionalities:

• ResHedNet model for advanced edge detection. This model is based on the holistically-nested edge detection paper. We improved the original model by replacing vanilla convolutional layers with ResNet-like blocks in each segment and by reducing the number of max-pooling operations to 2 (we found that 3 different scales are enough for learning the relevant features in typical microscopy images)
• SegResNet model for general semantic segmentation as an alternative to default UNet model. It has ResNet-like connections in each segment in addition to UNet-like skip connections between encoding and decoding paths.

Bug fixes:

• Fix bug that was preventing from saving/loading custom models
• Fix bug that was performing a zoom-in operation even when set to False during data augmentation
• Fix bug in the output_shape in BasePredictor, which required the output shape to be identical to the input shape

Improvements:

• Add option to pass a custom loss function to trainers for semantic segmentation and im2spec/spec2im
• Add option to store all training data on CPU when the size of the training data exceeds a certain limit (default limit is 4GB). In this case, only the individual batches are moved to a GPU device at training/test steps.
• Make computation of coordinates optional for SegPredictor
• Automatically save VAE models after each training cycle ("epoch") and not just at the end of training

New examples:

• New notebook on constructing and using (training+predicting) a custom image denoiser with AtomAI
• New notebook on applications of rotationally invariant VAE (rVAE) and class-conditioned rVAE to arbitrary rotated handwritten digits