fcn

by wkentaro

wkentaro / fcn

Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original res...

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fcn - Fully Convolutional Networks

PyPI Version Python Versions GitHub Actions

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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