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}
}
@inproceedings{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},
url={http://arxiv.org/abs/1502.02734},
year={2015}
}
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.