Metadata-Version: 1.1
Name: diluvian
Version: 0.0.4
Summary: Flood filling networks for segmenting electron microscopy of neural tissue.
Home-page: https://github.com/aschampion/diluvian
Author: Andrew S. Champion
Author-email: andrew.champion@gmail.com
License: MIT license
Description: ===============================
        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
        
           pip install 'tensorflow-gpu==1.3.0'
        
        To test that everything works train diluvian on three volumes from the
        `CREMI challenge <https://cremi.org>`_:
        
        .. code-block:: console
        
           diluvian train
        
        This will automatically download the CREMI datasets to your Keras cache. Only
        two epochs will run with a small sample set, so the trained model is not useful
        but will verify Tensorflow is working correctly.
        
        To train for longer, generate a diluvian config file:
        
        .. code-block:: console
        
           diluvian check-config > myconfig.toml
        
        Now edit settings in the ``[training]`` section of ``myconfig.toml`` to your
        liking and begin the training again:
        
        .. code-block:: console
        
           diluvian train -c myconfig.toml
        
        For detailed command line instructions and usage from Python, see the
        `usage documentation <https://diluvian.readthedocs.io/page/usage.html>`_.
        
        
        Limitations, Differences, and Caveats
        -------------------------------------
        
        Diluvian may differ from the original FFN algorithm or make implementation
        choices in ways pertinent to your use:
        
        * By default diluvian uses a U-Net architecture rather than stacked convolution
          modules with skip links. The authors of the original FFN paper also now use
          both architectures (personal communication). To use a different architecture,
          change the ``factory`` setting in the ``[network]`` section of your config
          file.
        * Rather than resampling training data based on the filling fraction
          :math:`f_a`, sample loss is (optionally) weighted based on the filling
          fraction.
        * A FOV center's priority in the move queue is determined by the checking
          plane mask probability of the first move to queue it, rather than the
          highest mask probability with which it is added to the queue.
        * Currently only processing of each FOV is done on the GPU, with movement
          being processed on the CPU and requiring copying of FOV data to host and
          back for each move.
        
        .. [Januszewski2016]
           Michał Januszewski, Jeremy Maitin-Shepard, Peter Li, Jorgen Kornfeld,
           Winfried Denk, and Viren Jain.
           Flood-filling networks. *arXiv preprint*
           *arXiv:1611.00421*, 2016.
        
        .. |pypi_badge|
                image:: https://img.shields.io/pypi/v/diluvian.svg
                :target: https://pypi.python.org/pypi/diluvian
                :alt: PyPI Package Version
        
        .. |travis_badge|
                image:: https://img.shields.io/travis/aschampion/diluvian.svg
                :target: https://travis-ci.org/aschampion/diluvian
                :alt: Continuous Integration Status
        
        .. |docs_badge|
                image:: https://readthedocs.org/projects/diluvian/badge/?version=latest
                :target: https://diluvian.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        .. |license_badge|
                image:: https://img.shields.io/badge/License-MIT-blue.svg
                :target: https://opensource.org/licenses/MIT
                :alt: License: MIT
        
        
        =======
        History
        =======
        
        0.0.4 (2017-10-02)
        ------------------
        
        * Much faster, more reliable training and validation.
        * U-net supports valid padding mode and other features from original
          specification.
        * Add artifact augmentation.
        * More efficient subvolume sampling.
        * Many other changes.
        
        
        0.0.3 (2017-06-04)
        ------------------
        
        * Training now works in Python 3.
        * Multi-GPU filling: filling will now use the same number of processes and
          GPUs specified by ``training.num_gpus``.
        
        
        0.0.2 (2017-05-22)
        ------------------
        
        * Attempt to fix PyPI configuration file packaging.
        
        
        0.0.1 (2017-05-22)
        ------------------
        
        * First release on PyPI.
        
Keywords: diluvian
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
