faster-rcnn-cpu | # Note This ref contains 2 files

We'll call the directory that you cloned Faster R-CNN into FRCN_ROOT.
Clone the Faster R-CNN repository
We'll call the directory that you cloned Faster R-CNN into FRCN_ROOT Ignore notes 1 and 2 if you followed step 1 above. Note 1: If you didn't clone Faster R-CNN with the --recursive flag, then you'll need to manually clone the caffe-fast-rcnn submodule: git submodule update --init --recursive Note 2: The caffe-fast-rcnn submodule needs to be on the faster-rcnn branch (or equivalent detached state). This will happen automatically if you followed step 1 instructions.
Build the Cython modules cd $FRCN_ROOT/lib make
Build Caffe and pycaffe cd $FRCN_ROOT/caffe-fast-rcnn # Now follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # If you're experienced with Caffe and have all of the requirements installed # and your Makefile.config in place, then simply do: make -j8 && make pycaffe
Download pre-computed Faster R-CNN detectors cd $FRCN_ROOT ./data/scripts/fetch_faster_rcnn_models.sh This will populate the $FRCN_ROOT/data folder with faster_rcnn_models. See data/README.md for details. These models were trained on VOC 2007 trainval.
Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16. VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience. ZF was trained at MSRA.