Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation

Created by Alexander Kolesnikov and Christoph Lampert at IST Austria.

Disclaimer: the code was tested on Ubuntu 14.04 LTS.

Dependencies

• Python packages:

      $ pip install -r python-dependencies.txt

• caffe: installation instructions are available at http://caffe.berkeleyvision.org/installation.html. Note, you need to compile caffe with python wrapper and support for python layers.

• Fully connected CRF wrapper (requires the Eigen3 package).

      $ pip install CRF/

Adding new loss layers to caffe

In order to add new loss layers to caffe install the pylayers package, provided in this repository:

      $ pip install pylayers/

How to train SEC model

• Go into the training directory:

      $ cd training

• Download the initial model (~80 MB) pretrained on Imagenet:

      $ wget http://ccvl.stat.ucla.edu/ccvl/init_models/vgg16_20M.caffemodel

• Decompress localization cues:

      $ gzip -kd localization_cues/localization_cues.pickle.gz

• Set root_folder parameter in train.prototxt to the directory with PASCAL VOC 2012 images

• Run caffe:

      $ caffe train --solver solver.prototxt --weights vgg16_20M.caffemodel --gpu <gpu_id>

The trained model will be created in training/models

How to deploy the SEC model

• Go into the deploy directory:

      $ cd deploy

• Download pretrained SEC model (~80 MB):

      $ wget http://pub.ist.ac.at/~akolesnikov/SEC-data/SEC.caffemodel

• Run the model on any image (smooth options switches on CRF postprocessing):

      $ python demo.py --model SEC.caffemodel --image <PATH TO IMAGE> --smooth