This is the code accompanying the ECCV 2018 publication on Superpixel Sampling Networks. Please visit the project website for more details about the paper and overall methodology.
Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
cd ssn_superpixels/lib/
git clone https://github.com/varunjampani/video_prop_networks.git
lib/include
and lib/src
folders) to the corresponding locations in the caffe
repository. In the ssn_superpixels/lib
directory:cp src/caffe/layers/* video_prop_networks/lib/caffe/src/caffe/layers/.
cp src/caffe/test/* video_prop_networks/lib/caffe/src/caffe/test/.
cp src/caffe/proto/caffe.proto video_prop_networks/lib/caffe/src/caffe/proto/caffe.proto
cp include/caffe/layers/* video_prop_networks/lib/caffe/include/caffe/layers/.
ssn_superpixels/lib
directory:cd video_prop_networks/lib/caffe/
mkdir build
cd build
cmake ..
make -j
cd ../../../..
Note: If you install Caffe in some other folder, update CAFFEDIR
in config.py
accordingly.
We use a cython script taken from 'scikit-image' for enforcing connectivity in superpixels. To compile this:
cd lib/cython/
python setup.py install --user
cd ../..
Download the BSDS dataset into data
folder:
cd data
sh get_bsds.sh
cd ..
get_models.sh
script in the models
folder:cd models
sh get_models.sh
cd ..
compute_ssn_spixels.py
to compute superpixels on BSDS dataset:python compute_ssn_spixels.py --datatype TEST --n_spixels 100 --num_steps 10 --caffemodel ./models/ssn_bsds_model.caffemodel --result_dir ./bsds_100/
You can change the number of superpixels by changing the n_spixels
argument above, and you can update the datatype
to TRAIN
or VAL
to compute superpixels on the corresponding data splits.
If you want to compute superpixels on other datasets, update config.py
accordingly.
For superpixel evaluation, we use scripts from here for computing ASA score and scripts from here for computing Precision-Recall and other evaluation metrics.
Use train_ssn.py
to train on BSDS training dataset:
python train_ssn.py --l_rate=0.0001 --num_steps=10
Please consider citing the below paper if you make use of this work and/or the corresponding code:
@inproceedings{jampani18ssn,
title = {Superpixel Samping Networks},
author={Jampani, Varun and Sun, Deqing and Liu, Ming-Yu and Yang, Ming-Hsuan and Kautz, Jan},
booktitle = {European Conference on Computer Vision (ECCV)},
month = September,
year = {2018}
}