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}
}
DeepLab currently achieves 73.9% on the challenging PASCAL VOC 2012 image segmentation task -- see the leaderboard.
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.
- Install wget library for python
sudo pip install wget
-
Change DATA_ROOT to point to the PASCAL images
-
To use the mat_read_layer and mat_write_layer, please download and install matio.
python run.py
Check FAQ if you have some problems while using the code.
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