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# first, make sure that your conda is setup properly with the right environment# for that, check that `which conda`, `which pip` and `which python` points to the# right path. From a clean conda env, this is what you need to do
conda create --name maskrcnn_benchmark
source activate maskrcnn_benchmark
# this installs the right pip and dependencies for the fresh python
conda install ipython
# maskrnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib
# follow PyTorch installation in https://pytorch.org/get-started/locally/# we give the instructions for CUDA 9.0
conda install pytorch-nightly -c pytorch
# install torchvisioncd~/github
git clone https://github.com/pytorch/vision.git
cd vision
python setup.py install
# install pycocotoolscd~/github
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
# install PyTorch Detectioncd~/github
git clone https://github.com/facebookresearch/maskrcnn-benchmark.git
cd maskrcnn-benchmark
# the following will install the lib with# symbolic links, so that you can modify# the files if you want and won't need to# re-build it
python setup.py build develop
# or if you are on macOS# MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop