caffe 学习笔记

介绍

caffe 可用于c++、python和matlab

Install

Caffe 使用

已训练好的caffemodel

已训练好的模型在 “models/bvlc_对应模型名称”文件夹下的 readme.md 文件中提供下载链接。

使用caffe编译自己的c++ 代码

  1. At the top of your .cpp program which is linking to the Caffe library, you will need to make the following definition #define CPU_ONLY

  2. Now if we try to compile anything now, Caffe will make this complaint: caffe/proto/caffe.pb.h: No such file or directory Some of the header files are missing from the Caffe include directory. Thus, you’ll need to generate them with these commands from within the Caffe root directory :

$ protoc src/caffe/proto/caffe.proto --cpp_out=.
$ mkdir include/caffe/proto
$ mv src/caffe/proto/caffe.pb.h include/caffe/proto

  1. Finally, I copied libcaffe.so into /usr/lib and the caffe directory containing the header libraries (caffe_root/include/caffe) into the /usr/include directory. To compile this on a Mac (after installing OpenBLAS with Homebrew), I just had to run:

$ g++ classification.cpp -lcaffe -lglog -lopencv_core -lopencv_highgui -lopencv_imgproc -I /usr/local/Cellar/openblas/0.2.14_1/include -L /usr/local/Cellar/openblas/0.2.14_1/lib -o classifier

Alternatively, you could do what I did on my Linux machine and instead of copying header files, I just linked directly to those directories when I compiled:

$ g++ classification.cpp -lcaffe -lglog -lopencv_core -lopencv_highgui -lopencv_imgproc -I ~/caffe/include -L ~/caffe/build/lib -I /usr/local/Cellar/openblas/0.2.14_1/include -L /usr/local/Cellar/openblas/0.2.14_1/lib -o classifier``