Get the master branch of Caffe to compile on your machine. If you've never used Caffe before, it can take a bit of time to get all the required libraries in place. Fortunately, the installation process is well documented.
Note: You can set CPU_ONLY := 1 in your Makefile.config to skip all the Cuda/GPU stuff. The Deep Visualization Toolbox can run with Caffe in either CPU or GPU mode.
Instead of using the master branch of caffe, to use the demo you'll need a slightly modified branch (supporting deconv and a few extra Python bindings). Getting the branch and switching to it is easy. Starting from your caffe directory, run:
$ git remote add yosinski https://github.com/yosinski/caffe.git
$ git fetch --all
$ git checkout --track -b deconv-deep-vis-toolbox yosinski/deconv-deep-vis-toolbox
$ make clean
$ make -j
$ make -j pycaffe
As noted above, feel free to compile in CPU_ONLY mode if desired.
You can put it wherever you like:
$ git clone https://github.com/yosinski/deep-visualization-toolbox
$ cd deep_visualization_toolbox
Copy settings.py.template to settings.py and edit it so the caffevis_caffe_root variable points to the directory where you've compiled caffe in Step 1:
$ cp settings.py.template settings.py
$ < edit settings.py >
Download the example model weights and corresponding top-9 visualizations saved as jpg (downloads a 230MB model and 1.1GB of jpgs to show as visualization):
$ cd models/caffenet-yos/
$ ./fetch.sh
Simple:
$ ./run_toolbox.py
Once the toolbox is running, push 'h' to show a help screen. You can also have a look at bindings.py to see what the various keys do. If the window is too large or too small for your screen, set the global_scale variable in settings.py to a value smaller or larger than one.
If you have any problems getting the code running, please feel free to email me. I might have left out an important detail here or there :).