machine-learningneural-networktheanodeep-learningshogun

How does the SHOGUN Toolbox convolutional neural network compare to Caffe and Theano?


I'm interested in implementing a convolutional neural network in my C++ program where I'm tracking tagged insects (I'm also using OpenCV). I see people mention Caffe, Torch and Theano a lot but I haven't heard the CNN in the SHOGUN Toolbox discussed. Does this CNN work well and would anyone recommend it if you're working in C++? I've used Theano via scikit-neuralnetwork in Python to test out some images and that worked really well, except unfortunately Theano is Python-only.


Solution

  • The difference lies in the speed. cnn is computationally expensive, so a GPU implementation is at least 10 times faster than CPU. caffe and theano provide seamless integration of calling either CPU or GPU, which may not be easy for you to implement without much GPU programming experience.

    Other factors may exist including a unified interface for multiplayer, stochastic gradient descent, and etc. but I think speed issue is most crucial among all these factors.