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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 project.
- More advanced backends, using:
- 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.
- App package for OS X, probably using PyInstaller or py2app.
- 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.