first, make sure that your conda is setup properly with the right environment

Installation

Requirements:

• PyTorch 1.0 from a nightly release. It will not work with 1.0 nor 1.0.1. Installation instructions can be found in https://pytorch.org/get-started/locally/
• torchvision from master
• cocoapi
• yacs
• matplotlib
• GCC >= 4.9
• OpenCV (may need to build from source to run the video generation demo)
• CUDA >= 9.0

Step-by-step installation

# 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 python=3.6
conda activate maskrcnn_benchmark

# this installs the right pip and dependencies for the fresh python
conda install ipython pip -n maskrcnn

# install dependencies (remove pytorch and/or opencv from requirements.txt if you wish to install manually)
pip install -r requirements.txt

# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
conda install -c pytorch -n maskrcnn pytorch-nightly torchvision cudatoolkit=9.0 

export INSTALL_DIR=$PWD

# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# install cityscapesScripts
cd $INSTALL_DIR
git clone https://github.com/mcordts/cityscapesScripts.git
cd cityscapesScripts/
python setup.py build_ext install

# install apex
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

# install this repo
cd $INSTALL_DIR
git clone <URL to this repo>
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


unset INSTALL_DIR