satoshirobatofujimoto / rcf-pytorch

Richer Convolutional Features for Edge Detection model in pytorch CVPR2017

Richer Convolutional Features for Edge Detection

Thanks to yun-liu's help. Created by XuanyiLi, if you have any problem in using it, please contact:[email protected]. 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}, }

For you:😋

this is the edge version of movie Titanic， for my love:

Introduction

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

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

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.
• Add pami version of RCF.

source：

• To download the pretrained model, please click https://drive.google.com/open?id=1TupHeoBKawrniDka0Hc64m3BG4OKG8nM (This pretrained model is not the best model, just for communicating)
• To download the vgg16 pretrained model which is used for the backbone. please click https://drive.google.com/file/d/1lUhPKKj-BSOH7yQL0mOIavvrUbjydPp5/view?usp=sharing.

Related Projects

[1] Richer Convolutional Features for Edge Detection

[2] HED

[3] HED created by zeakey's

[4] ContourNet