The eblearn tools help to create datasets, train models and run them. Their code is located in eblearn/tools/tools/src and binaries are built into eblearn/bin, and installed on the system with 'make install'. They can all be compiled by calling 'make tool', or simply 'make'. Most tools will show a brief help when called without arguments.

• dscompile: A data compilation tool.
• dssplit: split a dataset in two (e.g. validation and training).
• dsmerge: merge two datasets into one (e.g. positive and negative datasets).
• dsdisplay: show samples and labels contained in a dataset.
• dsfprop: runs a network on an entire dataset and saves its raw outputs as a new dataset.
• imfprop: runs a network on an image and saves the raw outputs.
• capture: capture images from camera for training.

• train: train a model given a configuration file.
• maketable: create a table matrix for convolutional connections.

• detect: find bounding boxes around objects in images given a trained model (multi-threaded version).
• classify: classifies inputs and prints out classes and confidences.
• track: find and track objects in video inputs given a trained model.
• stdetect: single-threaded version of detect.
• mpidetect: cluster multi-threaded version of detect, using MPI.

• metarun: run multiple jobs in parallel or in sequence, given all possible configurations in a configuration file with multi-values variables. metarun analyzes outputs and sends emails with reports and curves at different iterations. It can also be used with clusters (using MPI).
• metaparse: parse the output files and directories of metarun, analyze results and send reports and curves.

• matshow: display images of any type and .mat matrix types. matshow can show multiple images at the same time. It can also display weights of a stored model.
• ebl2matlab: converts eblearn/lush matrix format into Matlab matrix format.
• narrow: allows to narrow some dimension of a matrix file and save it.