The following list gives some ideas for improving Glimpse in the future. The list is in no particular order.
Integration into a general machine learning framework. Ideally, this
framework would provide a graphical interface for designing and running
experiments. A good candidate for such a framework is the Orange
More advanced backends, using:
GPUs: Push evaluation of layer-wise operations to a GPU. This could probably be written using PyCUDA or Theano.
Vector Intrinsics (SSE): Evaluate a layer-wise operation on several units in parallel. Some code for this exists in old versions of the project, and should be dusted off.
A graphical user interface (GUI) that allows the user to specify arbitrary
network topologies. This might be done by hacking an interface out of the
Orange project's workbench code.
Integrated GUI for running experiments and analyzing results. As
an example, this should integrate the plots shown in the user guide. A
start in this direction has been made using PySide.
Automated loader/downloader for image corpora, similar to the
mechanism provided by scikit-learn. For example, this should allow the
user to download and unpack the AnimalDB dataset with a single command.
Add a script to perform classification on many sub-windows of the same
image. Use the optimization we built for George's thesis.
Add an iterable interface for joblib.Parallel, with access to results as they arrive. This is necessary to support a progress meter for the MulticorePool.