<commit_before>===============================
diluvian
===============================


Flood filling networks for segmenting electron microscopy of neural tissue.

==============  ===============
PyPI Release    |pypi_badge|
Documentation   |docs_badge|
License         |license_badge|
Build Status    |travis_badge|
==============  ===============

Diluvian is an implementation and extension of the flood-filling network (FFN)
algorithm first described in [Januszewski2016]_. Flood-filling works by
starting at a seed location known to lie inside a region of interest, using a
convolutional network to predict the extent of the region within a small
field of view around that seed location, and queuing up new field of view
locations along the boundary of the current field of view that are confidently
inside the region. This process is repeated until the region has been fully
explored.


Quick Start
-----------

This assumes you already have CUDA installed and have created a fresh
virtualenv. See `installation documentation <https://diluvian.readthedocs.io/page/installation.html>`_
for detailed instructions.

Install diluvian and its dependencies into your virtualenv:

.. code-block:: console

   pip install diluvian

For compatibility diluvian only requires TensorFlow CPU by default, but you
will want to use TensorFlow GPU if you have installed CUDA:

.. code-block:: console
<commit_msg>Doc: note availability of Google's implementation<commit_after>===============================
diluvian
===============================


Flood filling networks for segmenting electron microscopy of neural tissue.

==============  ===============
PyPI Release    |pypi_badge|
Documentation   |docs_badge|
License         |license_badge|
Build Status    |travis_badge|
==============  ===============

Diluvian is an implementation and extension of the flood-filling network (FFN)
algorithm first described in [Januszewski2016]_. Flood-filling works by
starting at a seed location known to lie inside a region of interest, using a
convolutional network to predict the extent of the region within a small
field of view around that seed location, and queuing up new field of view
locations along the boundary of the current field of view that are confidently
inside the region. This process is repeated until the region has been fully
explored.

As of December 2017 the original paper's authors have released `their implementation <https://github.com/google/ffn>`_.


Quick Start
-----------

This assumes you already have CUDA installed and have created a fresh
virtualenv. See `installation documentation <https://diluvian.readthedocs.io/page/installation.html>`_
for detailed instructions.

Install diluvian and its dependencies into your virtualenv:

.. code-block:: console

   pip install diluvian

For compatibility diluvian only requires TensorFlow CPU by default, but you
will want to use TensorFlow GPU if you have installed CUDA:

.. code-block:: console

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