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, Triplet Loss, SmoothL1 Layer, ROI Layer.

Update

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.

Setup step:

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

Matlab Wrapper

Just change the Matlab include and library path defined in the settings and compile. Don't forget to add ./matlab to your Matlab path.

Python Wrapper

Similar with Matlab, just change the python include and library path defined in the settings and compile.

MNIST example

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.

Acknowlegement

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.