This is Chainer implementation of fcn.berkeleyvision.org.
- Provide FCN8s model for Chainer. [v1.0.0]
- 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]
Released under the MIT license
http://opensource.org/licenses/mit-license.php
# 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 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
Original Image: https://www.flickr.com/photos/faceme/26471792680/
git clone https://github.com/wkentaro/fcn.git
cd fcn
python setup.py develop
cd examples/pascal
./download_dataset.py
./train_fcn32s.py
Currently we support only training FCN32s. The learning curve looks like below:
Inference with FCN32s + 60000 iterations outputs below result: