Other deep learning libraries that are worth looking at, if you are going further with deep learning in Python, are Keras and Pylearn2. They are both based on Theano and have different usages and features.

Keras can be found here: https://github.com/fchollet/keras/.

Pylearn2 can be found here: http://deeplearning.net/software/pylearn2/.

Both are not stable platforms at the time of writing, although Pylearn2 is the more stable of the two. That said, they both do what they do very well and are worth investigating for future projects.

Another library called Torch is very popular but, at the time of writing, it doesn't have python bindings (see http://torch.ch/).

Another package for image processing is Mahotas, including better and more complex image processing techniques that can help achieve better accuracy, although they may come at a high computational cost. However, many image processing tasks are good candidates for parallelization. More techniques on image classification can be found in the research literature, with this survey paper as a good start: http://luispedro.org/software/mahotas/.

http://ijarcce.com/upload/january/22-A%20Survey%20on%20Image%20Classification.pdf

Other image datasets are available at:

http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html

There are many datasets of images available from a number of academic and industry-based sources. The linked website lists a bunch of datasets and some of the best algorithms to use on them. Implementing some of the better algorithms will require significant amounts of custom code, but the payoff can be well worth the pain.