Forked from https://www.github.com/BVLC/caffe master branch in 2015/6/5
Added Batch Normalization, Parametric ReLU, Locally Connected Layer, Normalize Layer, Randomized ReLU.
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Download third-party libraries from http://pan.baidu.com/s/1sjE5ER7 , and put the 3rdparty folder under the root of caffe-windows. If your VS version is not 2012, please refer to this project to create 3rdparty libraries.
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Run ./src/caffe/proto/extract_proto.bat to create caffe.pb.h, caffe.pb.cc and caffe_pb2.py.
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Double click ./build/MSVC/MainBuilder.sln to open the solution in Visual Studio 2012. Higher version of VS can also work, but you must create your own 3rdparty libraries.
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Change the compile mode to Release and X64.
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Change the CUDA include and library path to your own ones.
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Compile.
TIPS: If you have MKL library, please add the preprocess macro "USE_MKL" defined in the setting of the project.
中文安装说明:http://blog.csdn.net/happynear/article/details/45372231
Just change the Matlab include and library path defined in the settings and compile. Don't forget to add ./matlab to your Matlab path.
Similar with Matlab, just change the python include and library path defined in the settings and compile.
Please download the mnist leveldb database from http://pan.baidu.com/s/1mgl9ndu and extract it to ./examples/mnist. Then double click ./run_mnist.bat to run the MNIST demo.
We greatly thank Yangqing Jia and BVLC group for developing Caffe,
@niuzhiheng for his contribution on the first generation of caffe-windows,
@ChenglongChen for his implementation of Batch Normalization,
@jackculpepper for his implementation of locally-connected layer,
and all people who have contributed to the caffe user group.