Feature Catalogue
Plug-AI allows you to configure a complete pipeline using options from a predefined catalogue. You can independantly select and configure:
a dataset and dataloader
a model to train or use
an optimizer
a learning rate scheduler
a criterion (loss)
a metric for evaluation
a pipeline (training, evaluation, inference)
Datasets and dataloader
Models
DynUnet
unetr
ModSegNet
nnU-Net is also available but follows a special pipeline non-compatible with the rest of the options.
Optimizers
By default, the optimizer used is SGD but you can decide to use any of these following optimizers:
Adadelta,
Adagrad,
Adam,
AdamW,
Adamax,
ASGD,
NAdam,
RAdam,
RMSprop,
Rprop,
SGD
Learning rate schedulers
By default, the learning rate is static and no learning rate scheduler is used but you can decide to use any of the following schedulers:
StepLR
MultiStepLR
ConstantLR
LinearLR
ExponentialLR
CosineAnnealingLR
ReduceLROnPlateau
CyclicLR
OneCycleLR
CosineAnnealingWarmRestarts
Criterions (loss)
The available loss are:
DiceLoss
GeneralizedDiceLoss
DiceCELoss
DiceFocalLoss
GeneralizedDiceFocalLoss
FocalLoss
TverskyLoss
Metrics for evaluation
The available metrics are:
MeanDice
MeanIoU
Pipelines (training, evaluation, inference)
Training
Evaluation
Inference