zebara58/deeplab-public

Working copy of Deeplab repository

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}
}

Performance

At the time of its release, DeepLab is the state-of-art method on semantic image segmentation on the challenging PASCAL VOC-2012 image segmentation task, with the latest variant achieving 72.7% mean IoU on the test set -- see the leaderboard.

Pre-trained models

1. DeepLab and corresponding prototxt files at here. After DenseCRF, the model yields 66.4% performance on the PASCAL VOC 2012 test set.

2. DeepLab-MSc at here. After DenseCRF, the model yields 67.1% performance on the PASCAL VOC 2012 test set.

3. DeepLab-COCO (has fine-tuned on MS-COCO and then on PASCAL VOC 2012) at here. After DenseCRF, the model yields 70.4% performance on the PASCAL VOC 2012 test set.

4. DeepLab-Weak-EM-Adapt at here. Trained on PASCAL using only weak image-level labels. After DenseCRF, the model yields 39.0% performance on the PASCAL VOC 2012 test set.

Experimental set-up

1. The scripts we used for our experiments:
   1. run_pascal.sh: the script for training/testing on the PASCAL VOC 2012 dataset. Note You also need to download this file
   2. run_densecrf.sh and run_densecrf_grid_search.sh: the scripts we used for post-processing the DCNN computed results by DenseCRF.
2. The image list files used in our experiments:
   • The list folder stores the list files for the PASCAL VOC 2012 dataset. You can download the zipped file here (i.e., all the lists).
3. To use the mat_read_layer and mat_write_layer, please download and install matio.