as.list.platypus_spec   Turn a specification back into plain R data
augmentation_step       An augmentation step
available_components    What this package can do, asked of the package
available_transforms    Which augmentations are available
available_weights       The published weights, and what they are
callback_swa            Average the weights over the last part of the
                        run
callbacks               Callbacks
class_balance           How many images each class has, before anything
                        is trained
classes_to_colours      Paint a mask with its colormap
classification_data     Images and the label each of them carries,
                        which may be a set
classification_metrics
                        Metrics for one label per image
classifier              A contracting path, pooled, with one dense
                        layer on top
colormaps               Colormaps and their labels
colours_to_classes      Read a colour mask back to class indices
ct_windows              The named CT windows
decodings               How predicted channels become objects
detection_anchors       The anchors a detector used, and whether they
                        were fitted
detection_crops         Cut every detection out of the image it was
                        found in
detection_data          Where the images and their annotation files are
drawing_style           The decisions a drawing makes
ellipse_measure         The ellipse a segmented structure implies, in
                        millimetres
encoders                Pretrained encoders, and what they are worth
evaluate                Compare the trained models
evaluate_cases          Score every case separately
evaluate_classes        Average precision per class
evaluate_images         Score every image separately
export_weights          Save a trained model's weights
flow_to_colours         A vector field as a picture: hue is the
                        direction, value is the length
inspect_masks           What the colormap or labels match in your masks
instance_data           Data for instance segmentation: tell the
                        objects apart, not just find them
instance_shapes         What shape each object is, not just how big
instance_sizes          How big each object is, in units somebody can
                        use
loss_boundary           A loss that knows how far a wrong voxel is from
                        the truth
losses                  Loss functions
mask_coverage           How much of each class a mask covers
mask_volume             How much of a volume a class occupies, in
                        millilitres
metrics                 Metrics
models                  Segmentation models
optimizers              Optimisers
overlay_agreement       Where a prediction and the truth disagree
overlay_instance_agreement
                        Found, missed and invented objects
overlay_instances       Draw an instance map over its image
overlay_mask            Lay a mask over the image it belongs to
platypus_conditions     When something is wrong: the conditions this
                        package raises
platypus_device         Where the work will happen
platypus_fact           One fact about the platypus
platypus_fit            Train the models in a specification
platypus_spec           Build an experiment specification
platypus_status         Is the engine ready?
platypus_use_torch      Ask for a particular PyTorch build
plot.platypus_fit       Plot what happened during training
plot_anchors            Draw the anchors against the boxes they were
                        fitted to
plot_boxes              Draw boxes on images
plot_masks              Look at masks beside the images they came from
predict.platypus_fit    Predict masks, boxes, objects or labels
read_images             Read images as the model sees them
read_masks              Read mask files as class indices
read_record             Read what a run wrote down about itself
save_class_masks        Write overlapping classes, one file per class
save_class_volumes      Save overlapping classes as one NIfTI per class
save_masks              Write masks to files
save_spec               Write a specification back out as YAML
save_volumes            Save predicted volumes as NIfTI
segmentation_data       Where the images and masks are
separations             What the network predicts, so objects can be
                        told apart
series_report           Check folders of DICOM slices before training
                        on them
split_dataset           Split a dataset into training, validation and
                        test sets
split_path              The files in one part of a split
training_history        The epoch-by-epoch record
unite_masks             Combine several binary masks into one
volume_info             What is in a volume file
yolo3                   YOLOv3
