DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.

DeepLab

Introduction

DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.

It combines densely-computed deep convolutional neural network (CNN) responses with densely connected conditional random fields (CRF).

This distribution provides a publicly available implementation for the key model ingredients first reported in an arXiv paper, accepted in revised form as conference publication to the ICLR-2015 conference. It also contains implementations for methods supporting model learning using only weakly labeled examples, described in a second follow-up arXiv paper. Please consult and consider citing the following papers:

@inproceedings{chen14semantic,
  title={Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs},
  author={Liang-Chieh Chen and George Papandreou and Iasonas Kokkinos and Kevin Murphy and Alan L Yuille},
  booktitle={ICLR},
  url={http://arxiv.org/abs/1412.7062},
  year={2015}
}

@article{papandreou15weak,
  title={Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation},
  author={George Papandreou and Liang-Chieh Chen and Kevin Murphy and Alan L Yuille},
  journal={arxiv:1502.02734},
  year={2015}
}

Note that if you use the densecrf implementation, please consult and cite the following paper:

@inproceedings{KrahenbuhlK11,
  title={Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials},
  author={Philipp Kr{\"{a}}henb{\"{u}}hl and Vladlen Koltun},
  booktitle={NIPS},      
  year={2011}
}

Performance

DeepLab currently achieves 73.9% on the challenging PASCAL VOC 2012 image segmentation task -- see the leaderboard.

Pre-trained models

We have released several trained models and corresponding prototxt files at here. Please check it for more model details.

The best model among the released ones yields 73.6% on PASCAL VOC 2012 test set.

Python wrapper requirements

1. Install wget library for python

sudo pip install wget

2. Change DATA_ROOT to point to the PASCAL images

3. To use the mat_read_layer and mat_write_layer, please download and install matio.

Running the code

python run.py

FAQ

Check FAQ if you have some problems while using the code.