Commit 2e99211e authored by Ross Girshick's avatar Ross Girshick
Browse files

cleanup installation instructions after a walkthrough; remove external/caffe symlink from repo

parent ae33d791
......@@ -9,3 +9,4 @@ data/selective_search_data
data/window_files
rcnn_config_local.m
cachedir/*
external/caffe
......@@ -35,21 +35,22 @@ LICENSE file for details).
0. Download this [tagged release of Caffe](http://todo)
0. Follow the [Caffe installation instructions](http://caffe.berkeleyvision.org/installation.html)
0. **Important:** Make sure to compile the Caffe MATLAB wrapper, which is not built by default: `$ make matcaffe`
0. Let's call the place where you installed caffe `$CAFFE_HOME`
0. Let's call the place where you installed caffe `$CAFFE_HOME`: `$ export CAFFE_HOME=$(pwd)`
0. **Install R-CNN**
0. Let's assume you've placed the R-CNN source in a folder called `rcnn`
0. Change into that directory: `$ cd rcnn`
0. R-CNN expects to find Caffe in `external/caffe`, so create a symlink: `$ ln -sf $CAFFE_HOME external/caffe`
0. Start MATLAB (make sure you're in the `rcnn` folder): `$ matlab`
0. You'll be prompted to download the [Selective Search](http://disi.unitn.it/~uijlings/MyHomepage/index.php#page=projects1) code, which we cannot redistribute. You should see the message `R-CNN startup done` followed by the MATLAB prompt.
0. Run the build script: `>> rcnn_build()` (builds [liblinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/) and [Selective Search](http://www.science.uva.nl/research/publications/2013/UijlingsIJCV2013/))
0. Check that Caffe and MATLAB wrapper are setup correctly (this code should run without error):
`>> key = caffe('get_init_key')`
0. Run the build script: `>> rcnn_build()` (builds [liblinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/) and [Selective Search](http://www.science.uva.nl/research/publications/2013/UijlingsIJCV2013/)). Don't worry if you see compiler warnings while building liblinear, this is normal on my system.
0. Check that Caffe and MATLAB wrapper are set up correctly (this code should run without error): `>> key = caffe('get_init_key');` (expected output is key = -2)
0. Download the data package, which includes precompute models (see below).
**Common issues:** You may need to set an `LD_LIBRARY_PATH` before you start MATLAB. If you see a message like "Invalid MEX-file '/path/to/rcnn/external/caffe/matlab/caffe/caffe.mexa64': libmkl_rt.so: cannot open shared object file: No such file or directory" then make sure that CUDA and MKL are in your `LD_LIBRARY_PATH`. On my system, I use:
export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
### Downloading precomputed models (the data package)
The quickest way to get started is to download precomputed R-CNN detectors. Currently we have detectors trained on PASCAL VOC 2007 train+val and 2012 train. Unfortunately the download is large (1.5GB), so brew some coffee or take a walk while waiting.
......
./caffe should be a symlink to where you installed caffe
rcnn/external/caffe should be a symlink to where you installed caffe
liblinear downloaded from http://www.csie.ntu.edu.tw/~cjlin/liblinear/
../../caffe-rcnn
\ No newline at end of file
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