Chainer implementation of Fully Convolutional Networks.
pip install fcnInference 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/
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 0The 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 | - |
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 | - |
Ours (using fcn32s_voc_iter00092000.npz) |
91.1009 | 77.2522 | 65.3628 | 84.3675 | - |
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 | - |
Ours (using fcn16s_voc_iter00100000.npz) |
91.2608 | 78.1484 | 65.8444 | 84.6447 | - |
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 | - |
Left to right, FCN32s, FCN16s and FCN8s, which are fully trained using this repo. See above tables to see the accuracy.
See LICENSE.
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}
}