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Fully Convolutional Networks

Project description

https://badge.fury.io/gh/wkentaro%2Ffcn.svg https://travis-ci.org/wkentaro/fcn.svg?branch=master

This is Chainer implementation of fcn.berkeleyvision.org.

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]

License

Copyright (C) 2016 Kentaro Wada
Released under the MIT license

Installation

# 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:

# 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
static/fcn8s_26471792680.jpg

Original Image: https://www.flickr.com/photos/faceme/26471792680/

Training

cd examples/pascal
./download_dataset.py

./train_fcn32s.py

Currently we support only training FCN32s. The learning curve looks like below:

examples/pascal/static/learning_scale0.8.gif

Project details


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fcn-5.8.0.tar.gz (1.2 MB view hashes)

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