Click on a nut name for more details.
DplyToList: convert DplyDataframe to list.
ReadNumpy: load numpy array from file system.
ReadImage: load image as numpy array from file system.
ReadLabelDirs: read file paths from label directories.
ReadPandas: read data as Pandas table from file system.
WriteImage: write images to file system.
PrintColType: print type and other information for sample columns.
PrintType: print type and other information for structured data.
ViewImage: display image in window.
ViewImageAnnotation: display image and annotation in window.
ConvertLabel: convert between string labels and integer class ids.
CheckNaN: raise exception if data contains NaNs.
PartitionByCol: partition samples depending on column value.
SplitRandom: randomly split iterable into partitions, e.g. training, validation, test.
SplitLeaveOneOut: split iterable into leave-one-out train and test sets.
Stratify: stratifies samples by down-sampling or up-sampling.
Transforming & Augmenting
AugmentImage: augment images using random transformations, e.g. rotation.
ImageAnnotationToMask: return bit mask for geometric image annotation.
ImageChannelMean: compute per-channel means over images and subtract from images.
ImageMean: compute mean over images and subtract from images.
ImagePatchesByAnnotation: randomly sample patches from image based on geometric annotation.
ImagePatchesByMask: randomly sample patches from image based on annotation mask.
RandomImagePatches: extract patches at random locations from images.
RegularImagePatches: extract patches in a regular grid from images.
TransformImage: transform images, e.g. crop, translate, rotate.
Boost: boost samples with high confidence for incorrect class.
BuildBatch: build batches for GPU-based training.
PlotLines: plot lines for selected data columns, e.g. accuracy, loss.
LogToFile: log sample columns to file.