bermanmaxim / jaccardSegment

Deeplab-resnet-101 in Pytorch with Jaccard loss

Deeplab-resnet-101 Pytorch with Lovász hinge loss

Train deeplab-resnet-101 with binary Jaccard loss surrogate, the Lovász hinge, as described in http://arxiv.org/abs/1705.08790.

Parts of the code is adapted from tensorflow-deeplab-resnet (in particular the conversion from caffe to tensorflow with kaffe).

The code has not been tested for full training of Deeplab-Resnet yet. Refer to tensorflow-deeplab-resnet and possibly extract the weights after training with that framework.

Code status

The code is in early stage. Pull requests welcome.

Citation

Please cite

@ARTICLE{2017arXiv170508790B,
   author = {{Berman}, M. and {Blaschko}, M.~B.},
    title = "{Optimization of the Jaccard index for image segmentation with the Lov\'asz hinge}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1705.08790},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2017,
    month = may,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170508790B},
}

if you use the code.

Dependencies and weights

Relies notably on Pytorch and the standalone tensorboard package

Using anaconda, install the full requirements using the provided conda environment file:

conda env create --f environemnt.yml
source activate jaccard-segment

Convert the Deeplab Caffe weights to tensorflow ckpt using caffe-tensorflow, then convert them to hdf5 using ckpt_to_dd.py and use our wrapper to load in Pytorch.

Important switches in the settings

By default, finetunes with cross-entropy loss. Use --binary class switch for selecting a particular class in the binary case, --jaccard for training with the Jaccard hinge loss described in the arxiv paper, --hinge to use the Hinge loss, and --proximal to use the prox. operator optimization variant for the Jaccard loss as described in the arxiv paper.

For the prox. operator, use a learning rate of 1. and set an equivalent regularization of 1/lr instead.