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]
Copyright (C) 2016 Kentaro Wada
Released under the MIT license
http://opensource.org/licenses/mit-license.php
Released under the MIT license
http://opensource.org/licenses/mit-license.php
pip install fcn
fcn_install_trained_data.pyForwarding is done as below, and computation graph is here.
# Download sample image
wget https://farm2.staticflickr.com/1522/26471792680_a485afb024_z_d.jpg -O sample.jpg
# forwaring of the networks
fcn_forward.py --img-files sample.jpg --gpu -1 # cpu mode
fcn_forward.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 install
fcn_install_trained_data.pyYou need to download pascal VOC2012 dataset from here, and install it as below construction:
- fcn - data - pascal - VOC2012 -- JPEGImages
- SegmentationClass
- ...Forwarding with trained model in caffe is done as below:
# This downloads caffemodel and convert it to chainermodel
./scripts/caffe_to_chainermodel.py
# forwarding of the networks
./scripts/fcn_forward.py --img-files data/pascal/VOC2012/JPEGImages/2007_000129.jpg
Original Image: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
./scripts/fcn_train.pyCurrently we support only training FCN32s. The learning curve looks like below:
Forwarding with fcn32s_60000.chainermodel ends with below result:
