Fully Convolutional Networks
Project description
fcn - Fully Convolutional Networks
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
fcn-6.4.12.tar.gz
(21.2 kB
view details)
File details
Details for the file fcn-6.4.12.tar.gz.
File metadata
- Download URL: fcn-6.4.12.tar.gz
- Upload date:
- Size: 21.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
57805cb0414670e02fa6d34b573607158d0694bb3121981aa8771d3709a48871
|
|
| MD5 |
da2b398cdffac7d6cbf886437c5318fc
|
|
| BLAKE2b-256 |
250c43a108920d03f3d3f056c7ae61d04ebb28ee277f9bc3c0e134d2c48f13cf
|