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Embedded Caffe

  Make the depth learning model running on Embedded Systems and multi-platform.

Features

  • Based on caffe
  • Compact，Only forward calculation
  • Support faster rcnn
  • Support ssd
  • Support ShuffleNet
  • Support MobileNet
  • Support GPU/CPU model
    • Window only support x86_64 archtecture in GPU mode.
    • CPU_ONLY mode support x86/x86_64 archtecture on Win platform.
  • Cross-platform
  • Small
  • Remove the gtest
  • Remove the test
  • Remove the python wrapper

Requirements

Linux

System              : Linux / Ubuntu 14.04
Cmake               : 3.4+
C++ compiler        : 4.8+
BLAS                : Atlas
Boost               : 1.54
glog                : Y
gflags              : Y
protobuf            : 2.6.1
CUDA                : 7.5
cuDNN               : 5.0.5
OpenCV              : N (for examples)

Windows

  • CPU_ONLY mode support x86/x86_64 archtecture.
  • GPU model only support x86_64 archtecture, because NVIDIA only support the x86_64 archtecture libraries since CUDA7.0.
System              : Windows
Cmake-gui           : 3.8+
C++ compiler        : vc140(VS2015)
embcaffe_3rdparty   : https://github.com/FreeApe/embcaffe_3rdparty

Build

Build EmbCaffe on Linux

  • Way 1
# Note: Test examples with this way
$ cd EmbCaffe
$ mkdir cmake_build
$ cd cmake_build
$ cmake ..
$ make all -j
  • Way 2
$ cd EmbCaffe
$ make all -j

Build EmbCaffe on Windows

1. with CMake-gui3.8+
2. git clone https://github.com/FreeApe/embcaffe_3rdparty(You can also compile these third-party libraries yourself)
2. Configure and Generate(You should configure CMAKE_INCLUDE_PATH and CMAKE_LIBRARY_PATH)
3. Open Caffe.sln with VS2015
4. build solutions

Build examples

  Linux:

$ cd examples
$ mkdir build
$ cd build
$ cmake ..
$ make -j

  Windows:

1. with CMake-gui3.8+
2. Configure and Generate
3. Open Caffe-example.sln with VS2015
4. build solutions
5. run demos

  Running the examples, the results show as :

ssd

faster-rcnn

Build Errors

1. error ‘type name’ declared as function returning an array escape

    Make sure the CUDA version is 7.5

TODO / Targets

  • Remove Backward calculation
  • Support GPU model on windows
  • Optimize the calculation
  • ......

Thanks