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Thanks to yun-liu's help.
Created by XuanyiLi, if you have any problem in using it, please contact:xuanyili.edu@gmail.com.
The best result of my pytorch model is 0.808 ODS F-score now.
my model result
the following are the side outputs and the prediction example
Citation
If you find our work useful in your research, please consider citing:
@article{RcfEdgePami2019,
author = {Yun Liu and Ming-Ming Cheng and Xiaowei Hu and Jia-Wang Bian and Le Zhang and Xiang Bai and Jinhui Tang},
title = {Richer Convolutional Features for Edge Detection},
year = {2019},
journal= {IEEE Trans. Pattern Anal. Mach. Intell.},
volume={},
number={},
pages={},
doi = {},
}
@inproceedings{RCFEdgeCVPR2017,
title={Richer Convolutional Features for Edge Detection},
author={Yun Liu and Ming-Ming Cheng, Xiaowei Hu and K Wang and X Bai},
booktitle={IEEE CVPR},
year={2017},
}
I implement the edge detection model according to the RCF model in pytorch.
the result of my pytorch model will be released in the future
Method
ODS F-score on BSDS500 dataset
ODS F-score on NYU Depth dataset
ours
0.808
***
Reference[1]
0.811
***
Installation
Install pytorch. The code is tested under 0.4.1 GPU version and Python 3.6 on Ubuntu 16.04. There are also some dependencies for a few Python libraries for data processing and visualizations like cv2 etc. It's highly recommended that you have access to GPUs.
Usage
image edge detection
To train a RCF model on BSDS500:
python train_RCF.py
After training, to evaluate:
python evaluate.py (for further work)
Side Note: Hello mingyang, I love you
License
Our code is released under MIT License (see LICENSE file for details).
Updates
To do
Add support for multi-gpu training for the edge detetion task.
Improve the performance to 0.806/0.811 in the original paper.
Add a gpu version of edge-eval code to accelerate the evaluation process.