faster-RCNN环境配置（Ｕbuntu14.04）

Requirements: hardware

1. For training smaller networks (ZF, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices
2. For training with VGG16, you'll need a K40 (~11G of memory)

Installation (sufficient for the demo)

1. Clone the Faster R-CNN repository

   # Make sure to clone with --recursive git clone --recursive https://github.com/rbgirshick/py-faster-rcnn.git

1. Build the Cython modules

   cd $FRCN_ROOT/lib make

2. sudo apt-get insatll python-pip  

3. sudo pip install cython
4. sudo apt-get install python-opencv
5. sudo pip install easydict
   
   

1. 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 

参考我的另一篇博客：

Ubuntu14.04(64位)+Ｃuda7.5+Ｃudnn7.0+Caffe+Ｍatlab(Linux版)

http://blog.csdn.net/qq_26898461/article/details/50586052

# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1
# 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

Demo

After successfully completing basic installation, you'll be ready to run the demo.

Python

To run the demo

cd $FRCN_ROOT ./tools/demo.py