Metadata-Version: 1.1
Name: fcn
Version: 5.5.0
Summary: Fully Convolutional Networks
Home-page: http://github.com/wkentaro/fcn
Author: Kentaro Wada
Author-email: www.kentaro.wada@gmail.com
License: MIT
Description: fcn - Fully Convolutional Networks
        ==================================
        
        .. image:: https://badge.fury.io/gh/wkentaro%2Ffcn.svg
            :target: https://badge.fury.io/gh/wkentaro%2Ffcn
        .. image:: https://travis-ci.org/wkentaro/fcn.svg?branch=master
            :target: https://travis-ci.org/wkentaro/fcn
        
        
        This is Chainer_ implementation of fcn.berkeleyvision.org_.
        
        .. _fcn.berkeleyvision.org: https://github.com/shelhamer/fcn.berkeleyvision.org.git
        .. _Chainer: https://github.com/pfnet/chainer.git
        
        
        Features
        --------
        
        - Copy caffemodel to chainermodel. [v1.0.0_]
        - Forwarding with Chainer for pascal dataset. [v1.0.0_]
        - Training with Chainer for pascal dataset. [v2.0.0_]
        - Training for APC2015 dataset. [v3.0.0_]
        
        .. _v1.0.0: https://github.com/wkentaro/fcn/releases/tag/v1.0.0
        .. _v2.0.0: https://github.com/wkentaro/fcn/releases/tag/v2.0.0
        .. _v3.0.0: https://github.com/wkentaro/fcn/releases/tag/v3.0.0
        
        
        License
        -------
        | Copyright (C) 2016 Kentaro Wada
        | Released under the MIT license
        | http://opensource.org/licenses/mit-license.php
        
        
        
        Installation
        ------------
        
        .. code-block:: bash
        
          # Ubuntu: install required libraries via apt
          sudo apt-get install liblapack-dev   # for numpy
          sudo apt-get install libhdf5-dev     # for h5py
          # macOS: install required libraries via brew
          brew install gfortran  # for numpy
          brew install hdf5      # for h5py
        
          pip install fcn
        
        
        Inference
        ---------
        
        Inference is done as below:
        
        .. code-block:: bash
        
          # Download sample image
          wget https://farm2.staticflickr.com/1522/26471792680_a485afb024_z_d.jpg -O sample.jpg
        
          # forwaring of the networks
          fcn_infer.py --img-files sample.jpg --gpu -1  # cpu mode
          fcn_infer.py --img-files sample.jpg  # gpu mode
        
        .. image:: static/fcn8s_26471792680.jpg
        
        Original Image: https://www.flickr.com/photos/faceme/26471792680/
        
        
        Training
        --------
        
        .. code-block:: bash
        
          cd examples/pascal
          ./download_dataset.py
        
          ./train_fcn32s.py
        
        Currently we support only training FCN32s.
        The learning curve looks like below:
        
        .. image:: examples/pascal/static/learning_scale0.8.gif
        
Keywords: machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Topic :: Internet :: WWW/HTTP
