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This is the Jittor implementation of our edge detection method, RCF.

If you are using the code/model/data provided here in a publication, please consider citing:

@article{liu2019richer,  title={Richer Convolutional Features for Edge Detection},  author={Liu, Yun and Cheng, Ming-Ming and Hu, Xiaowei and Bian, Jia-Wang and Zhang, Le and Bai, Xiang and Tang, Jinhui},  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},  volume={41},  number={8},  pages={1939--1946},  year={2019},  publisher={IEEE}}

• Jittor
• PyTorch (just for loading the pretrained model)
• numpy
• opencv-python
• scipy

1. Clone the RCF repository:

   git clone https://github.com/yun-liu/RCF-Jittor.git

2. Download the pretrained model (BSDS500+PASCAL), and put it into the$ROOT_DIR folder.

3. Download the BSDS500 dataset as below, and extract it to the$ROOT_DIR/data/ folder.

   wget http://mftp.mmcheng.net/liuyun/rcf/data/HED-BSDS.tar.gz

4. Run the following command to start the testing:

   python test.py --checkpoint bsds500_pascal_model.pth --save-dir /path/to/output/directory/

   This pretrained model should achieve an ODS F-measure of 0.812.

For more information about RCF and edge quality evaluation, please refer to this page:yun-liu/RCF

We have released the code and data for plotting the edge PR curves of many existing edge detectorshere.

PyTorch based RCF:yun-liu/RCF-PyTorch

Caffe based RCF:yun-liu/RCF