DeepMask
A Keras implementation of DeepMask based on NIPS 2015 paper Learning to Segment Object Candidates.
Requirements
ANACONDA、Keras、OpenCV3、mscoco
Here is the instructions to install them all:
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Download ANACONDA and then install it, I suggest you to install the Python 3.6 version.
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Install Keras by the following steps:
sudo pip install -U --pre pip setuptools wheel
sudo pip install -U --pre numpy scipy matplotlib scikit-learn scikit-image
sudo pip install -U --pre tensorflow
If your computer supports CUDA, you could install tensorflow-gpu by
sudo pip install -U --pre tensorflow-gpu
sudo pip install -U --pre keras
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Install OpenCV3 by the following steps:
brew tap homebrew/science
brew install opencv3 --with-python3 --without-python --without-numpy
cd ~/anaconda/lib/python3.6/site-packages/
ln -s /usr/local/Cellar/opencv3/3.2.0/lib/python3.6/site-packages/cv2.cpython-36m-darwin.so cv2.so
If your computer system aren't macOS Sierra, you should download OpenCV3.2.0 and then install it from source.
Make sure the compile setting 'with-python3' is on, you could do that by using cmake-gui.
When you have installed OpenCV3, make sure the cv2.so is in '~/anaconda/lib/python3.6/site-packages/'.
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Install MS COCO API by the following steps:
Download coco and unzip it.
cd coco-master/PythonAPI/
python setup.py build_ext install
Usage
Download the mscoco datasets first, you should only download '2014 Training images' and '2014 Train/Val object instances'.
Make a dir named 'coco', go inside and make two dir named 'images' and 'annotations'.
Unzip '2014 Training images' to dir 'images', '2014 Train/Val object instances' to dir 'annotations'.
cd DeepMask\
python main.py