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How to install Caffe for Fast-RCNN

  1. Complete the pre-requisites

  2. Clone Fast-RCNN and its Caffe fork ( It's better to do it in your home dir. CMake files will be looking for it on user's home)

% git clone --recursive https://github.com/rbgirshick/fast-rcnn.git

  1. Go into fast-rcnn/caffe-fast-rcnn and run % cp Makefile.config.example Makefile.config

  2. Open Makefile.config with your favorite editor.

  • If you have cuDNN installed uncomment the line # USE_CUDNN := 1
  • Make sure to uncomment WITH_PYTHON_LAYER := 1
  1. Compile Caffe
make && make distribute
  1. Download pretrained models executing while in fast-rcnn folder.
% ./data/scripts/fetch_fast_rcnn_models.sh

or http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/voc12_submission.tgz and put it in fast-rcnn/data/fast_rcnn_models.

To take advantage of cuDNN, at least CUDA 7.0 and a GPU with 3.5 compute capability is required.

If you didn't install Fast-RCNN in your home, modify the librcnn and rcnn_node CMakeFiles and point them to your caffe directories.

Once compiled, if you are running from the terminal

% roslaunch cv_tracker rcnn.launch

Remember to modify the launch file located inside computing/perception/detection/packages/cv_tracker/launch/rcnn.launch and point the network and pretrained models to your path