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Added Environment config #4

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Update README.md
doem97 Aug 12, 2018
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environment.yaml
doem97 Aug 12, 2018
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3 changes: 3 additions & 0 deletions README.md
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Implementation of the segmentation task for ISIC 2017. https://arxiv.org/abs/1703.04819

View segment.py for parameter options and instructions on where to download the dataset.

## Environment
Conda environment `environment.yaml` file saves the usable conda packets. You can use `conda env create -n envname -f environment.yaml` in conda to create a same environment on your machine.
136 changes: 136 additions & 0 deletions environment.yaml
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name: purepiptensor
channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- defaults
dependencies:
- _nb_ext_conf=0.4.0=py27_1
- bleach=1.5.0=py27_0
- cudatoolkit=8.0=3
- freetype=2.5.5=2
- html5lib=0.9999999=py27_0
- jpeg=8d=2
- lcms=1.19=0
- nb_anacondacloud=1.4.0=py27_0
- nb_conda_kernels=2.1.0=py27_0
- pil=1.1.7=py27_2
- anaconda-client=1.6.14=py27_0
- asn1crypto=0.24.0=py27_0
- backports=1.0=py27h63c9359_1
- backports.shutil_get_terminal_size=1.0.0=py27h5bc021e_2
- backports_abc=0.5=py27h7b3c97b_0
- blas=1.0=mkl
- ca-certificates=2018.03.07=0
- certifi=2018.4.16=py27_0
- cffi=1.11.5=py27h9745a5d_0
- chardet=3.0.4=py27hfa10054_1
- clyent=1.2.2=py27h7276e6c_1
- configparser=3.5.0=py27h5117587_0
- cryptography=2.2.2=py27h14c3975_0
- cudnn=7.0.5=cuda8.0_0
- decorator=4.3.0=py27_0
- entrypoints=0.2.3=py27h502b47d_2
- enum34=1.1.6=py27h99a27e9_1
- funcsigs=1.0.2=py27h83f16ab_0
- functools32=3.2.3.2=py27h4ead58f_1
- futures=3.2.0=py27h7b459c0_0
- gmp=6.1.2=h6c8ec71_1
- idna=2.7=py27_0
- intel-openmp=2018.0.3=0
- ipaddress=1.0.22=py27_0
- ipykernel=4.8.2=py27_0
- ipython=5.7.0=py27_0
- ipython_genutils=0.2.0=py27h89fb69b_0
- ipywidgets=7.2.1=py27_0
- jinja2=2.10=py27h4114e70_0
- jsonschema=2.6.0=py27h7ed5aa4_0
- jupyter_client=5.2.3=py27_0
- jupyter_core=4.4.0=py27h345911c_0
- libedit=3.1.20170329=h6b74fdf_2
- libffi=3.2.1=hd88cf55_4
- libgcc-ng=7.2.0=hdf63c60_3
- libgfortran-ng=7.2.0=hdf63c60_3
- libpng=1.6.34=hb9fc6fc_0
- libprotobuf=3.5.2=h6f1eeef_0
- libsodium=1.0.16=h1bed415_0
- libstdcxx-ng=7.2.0=hdf63c60_3
- markdown=2.6.11=py27_0
- markupsafe=1.0=py27h97b2822_1
- mistune=0.8.3=py27h14c3975_1
- mkl=2018.0.3=1
- mkl_fft=1.0.1=py27h3010b51_0
- mkl_random=1.0.1=py27h629b387_0
- mock=2.0.0=py27h0c0c831_0
- nb_conda=2.2.1=py27hec22543_0
- nbconvert=5.3.1=py27he041f76_0
- nbformat=4.4.0=py27hed7f2b2_0
- nbpresent=3.0.2=py27hcce4ef4_1
- ncurses=6.1=hf484d3e_0
- notebook=5.5.0=py27_0
- numpy=1.14.3=py27hcd700cb_2
- numpy-base=1.14.3=py27hdbf6ddf_2
- openssl=1.0.2o=h14c3975_1
- pandoc=2.2.1=h629c226_0
- pandocfilters=1.4.2=py27h428e1e5_1
- pathlib2=2.3.2=py27_0
- pbr=4.0.3=py27_0
- pexpect=4.6.0=py27_0
- pickleshare=0.7.4=py27h09770e1_0
- pip=10.0.1=py27_0
- prompt_toolkit=1.0.15=py27h1b593e1_0
- protobuf=3.5.2=py27hf484d3e_0
- ptyprocess=0.6.0=py27_0
- pycparser=2.18=py27hefa08c5_1
- pygments=2.2.0=py27h4a8b6f5_0
- pyopenssl=18.0.0=py27_0
- pysocks=1.6.8=py27_0
- python=2.7.15=h1571d57_0
- python-dateutil=2.7.3=py27_0
- pytz=2018.4=py27_0
- pyyaml=3.12=py27h2d70dd7_1
- pyzmq=17.0.0=py27h14c3975_1
- readline=7.0=ha6073c6_4
- requests=2.19.1=py27_0
- scandir=1.7=py27h14c3975_0
- send2trash=1.5.0=py27_0
- setuptools=39.1.0=py27_0
- simplegeneric=0.8.1=py27_2
- singledispatch=3.4.0.3=py27h9bcb476_0
- six=1.11.0=py27h5f960f1_1
- sqlite=3.23.1=he433501_0
- tensorflow-gpu=1.4.1=0
- tensorflow-gpu-base=1.4.1=py27h01caf0a_0
- tensorflow-tensorboard=1.5.1=py27hf484d3e_1
- terminado=0.8.1=py27_1
- testpath=0.3.1=py27hc38d2c4_0
- tk=8.6.7=hc745277_3
- tornado=5.0.2=py27_0
- traitlets=4.3.2=py27hd6ce930_0
- urllib3=1.23=py27_0
- wcwidth=0.1.7=py27h9e3e1ab_0
- werkzeug=0.14.1=py27_0
- wheel=0.31.1=py27_0
- widgetsnbextension=3.2.1=py27_0
- yaml=0.1.7=had09818_2
- zeromq=4.2.5=h439df22_0
- zlib=1.2.11=ha838bed_2
- pip:
- backports.functools-lru-cache==1.5
- backports.weakref==1.0rc1
- cycler==0.10.0
- h5py==2.7.1
- keras==1.2.2
- kiwisolver==1.0.1
- matplotlib==2.2.2
- opencv-python==3.4.0.12
- pandas==0.22.0
- pyparsing==2.2.0
- scikit-learn==0.19.1
- scipy==1.1.0
- subprocess32==3.2.7
- tensorflow==1.4.1
- theano==1.0.1
prefix: /home/zichen/anaconda2/envs/purepiptensor