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.
- 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.
- Compile the
Caffe
,matcaffe
andpycaffe
in thecaffe-cedn
package.
- 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
- Download the pre-trained model by running the script
./models/PASCAL/get_pretrained_pascal_net.sh
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.