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fcn - Fully Convolutional Networks

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Chainer implementation of Fully Convolutional Networks.

Installation

pip install fcn

Inference

Inference is done as below:

# forwaring of the networks
img_file=https://farm2.staticflickr.com/1522/26471792680_a485afb024_z_d.jpg
fcn_infer.py --img-files $img_file --gpu -1 -o /tmp  # cpu mode
fcn_infer.py --img-files $img_file --gpu 0 -o /tmp   # gpu mode

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

Training

cd examples/voc
./download_datasets.py
./download_models.py

./train_fcn32s.py --gpu 0
# ./train_fcn16s.py --gpu 0
# ./train_fcn8s.py --gpu 0
# ./train_fcn8s_atonce.py --gpu 0

The accuracy of original implementation is computed with (evaluate.py) after converting the caffe model to chainer one using convert_caffe_to_chainermodel.py.
You can download vgg16 model from here: vgg16_from_caffe.npz.

FCN32s

Implementation Accuracy Accuracy Class Mean IU FWAVACC Model File
Original 90.4810 76.4824 63.6261 83.4580 fcn32s_from_caffe.npz
Ours (using vgg16_from_caffe.npz) 90.5668 76.8740 63.8180 83.5067 fcn32s_voc_iter00092000.npz

FCN16s

Implementation Accuracy Accuracy Class Mean IU FWAVACC Model File
Original 90.9971 78.0710 65.0050 84.2614 fcn16s_from_caffe.npz
Ours (using fcn32s_from_caffe.npz) 90.9671 78.0617 65.0911 84.2604 fcn16s_voc_using_fcn32s_from_caffe_iter00032000.npz
Ours (using fcn32s_voc_iter00092000.npz) 91.1009 77.2522 65.3628 84.3675 fcn16s_voc_iter00100000.npz

FCN8s

Implementation Accuracy Accuracy Class Mean IU FWAVACC Model File
Original 91.2212 77.6146 65.5126 84.5445 fcn8s_from_caffe.npz
Ours (using fcn16s_from_caffe.npz) 91.2513 77.1490 65.4789 84.5460 fcn8s_voc_using_fcn16s_from_caffe_iter00016000.npz
Ours (using fcn16s_voc_iter00100000.npz) 91.2608 78.1484 65.8444 84.6447 fcn8s_voc_iter00072000.npz

FCN8sAtOnce

Implementation Accuracy Accuracy Class Mean IU FWAVACC Model File
Original 91.1288 78.4979 65.3998 84.4326 fcn8s-atonce_from_caffe.npz
Ours (using vgg16_from_caffe.npz) 91.0883 77.3528 65.3433 84.4276 fcn8s-atonce_voc_iter00056000.npz

Left to right, FCN32s, FCN16s and FCN8s, which are fully trained using this repo. See above tables to see the accuracy.

License

See LICENSE.

Cite This Project

If you use this project in your research or wish to refer to the baseline results published in the README, please use the following BibTeX entry.

@misc{chainer-fcn2016,
  author =       {Ketaro Wada},
  title =        {{fcn: Chainer Implementation of Fully Convolutional Networks}},
  howpublished = {\url{https://github.com/wkentaro/fcn}},
  year =         {2016}
}

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Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

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