A minimalist Deep Learning framework for embedded Computer Vision
MIT License
BCNN (Bare Convolutional Neural Networks) is a minimalist framework designed to prototype, train and deploy convolutional neural networks for embedded computer vision applications.
Download or clone the repository:
git clone https://github.com/jnbraun/bcnn.git
You need to have cmake installed in order to build the library.
Requires CUDA libraries (cudart, cublas, curand) and a GPU with compute capability 2.0 at least. CuDNN is optional but supported.
# User configuration settings
option(USE_AVX "Build with AVX instructions" ON)
option(USE_CUDA "Build with CUDA libraries" OFF)
option(USE_CUDNN "Build with CuDNN library" OFF)
option(USE_BLAS "Build with BLAS library" ON)
option(USE_NEON "Build with Neon instructions" OFF)
option(USE_OPENMP "Enable OpenMP multithreading" ON)
# Uncomment the proper line according to the system cuda arch
set(CUDA_ARCH
#"-gencode arch=compute_30,code=sm_30;"
#"-gencode arch=compute_35,code=sm_35;"
"-gencode arch=compute_50,code=sm_50;"
"-gencode arch=compute_50,code=compute_50;"
"-gencode arch=compute_52,code=sm_52;"
#"-gencode arch=compute_60,code=sm_60;"
#"-gencode arch=compute_61,code=sm_61;"
)
cd path/to/bcnn
mkdir build
cd build/
cmake ../
make
Use the command line tool bcnn-cl with configuration file: see an example here.
Or use the static library and write your own code: see an example there.
Released under MIT license.