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.
the following are the side outputs and the prediction example
If you find our work useful in your research, please consider citing:
@inproceedings{liu2017richer,
title={Richer Convolutional Features for Edge Detection},
author={Liu, Yun and Cheng, Ming-Ming and Hu, Xiaowei and Wang, Kai and Bai, Xiang},
journal={Proceedings of the IEEE conference on computer vision and pattern recognition},
year={2017}
}
this is the edge version of movie Titanic， for my love:
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 | *** |
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.
To train a RCF model on BSDS500:
python train_RCF.py
If you have multiple GPUs on your machine, you can also run the multi-GPU version training:
CUDA_VISIBLE_DEVICES=0,1 python train_multi_gpu.py --num_gpus 2
After training, to evaluate:
python evaluate.py (for further work)
Side Note: Hello mingyang, I love you
Our code is released under MIT License (see LICENSE file for details).
- 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..
[1] Richer Convolutional Features for Edge Detection
[2] HED