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objectContourDetector

This is the code for arXiv paper Object Contour Detection with a Fully Convolutional Encoder-Decoder Network by Jimei Yang, Brian Price, Scott Cohen, Honglak Lee and Ming-Hsuan Yang, 2016.

Contents

  • This code includes
  • the Caffe toolbox for Convolutional Encoder-Decoder Networks (caffe-cedn)
  • scripts for training and testing the PASCAL object contour detector, and
  • scripts to refine segmentation anntations based on dense CRF.
  • It is tested on Linux (Ubuntu 14.04) with NVIDIA TITAN X GPU.

Please follow the instructions below to run the code.

Compilation

  • Compile the Caffe, matcaffe and pycaffe in the caffe-cedn package.

Training on PASCAL

  • Download the pre-processed dataset by running the script
./data/PASCAL/get_pascal_training_data.sh
  • Download the VGG16 net for initialization by running the script
./models/get_vgg16_net.sh
  • Start training by running the script
./code/train.sh
  • Test the learned network by running the script
./code/test.sh

Testing the pre-trained model

  • Download the pre-trained model by running the script
./models/PASCAL/get_pretrained_pascal_net.sh

Citation

If you find this useful, please cite our work as follows:

@inproceedings{yang2016object,
  title={Object Contour Detection with a Fully Convolutional Encoder-Decoder Network},
  author={Yang, Jimei and Price, Brian and Cohen, Scott and Lee, Honglak and Yang, Ming-Hsuan},
  journal={arXiv preprint arXiv:1603.04530},
  year={2016}
}

Please contact "[email protected]" if any questions.

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