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.

2015/07/07 Visual Studio 2013 with CUDA 7.0 is now supported. A beta version 3rdparty library can be downloaded from http://pan.baidu.com/s/1sj3IvzZ. All the libraries have been updated to the latest version. Please help me try and report bugs.

WARNING: Due to the low compile speed of VS2012 with CUDA 6.5, VS2012 3rdparty library will not continue to be updated after September, 2015. If you are configuring a new platform, we strongly recommend you to use Visual Studio 2013 and CUDA 7.0.

1. Download third-party libraries from http://pan.baidu.com/s/1sjE5ER7 (for VS2012), and put the 3rdparty folder under the root of caffe-windows. Please don't forgert to add the ./3rdparty/bin folder to your environment variable PATH.

2. Run ./src/caffe/proto/extract_proto.bat to create caffe.pb.h, caffe.pb.cc and caffe_pb2.py.

3. Double click ./build/MSVC/MainBuilder.sln to open the solution in Visual Studio 2012. If you are using VS2013, please download 3rdparty libraries and solution files from http://pan.baidu.com/s/1sj3IvzZ.

4. Change the compile mode to Release and X64.

5. Change the CUDA include and library path to your own ones.

6. 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.