This is a personal reimplementation of Holistically-Nested Edge Detection [1] using PyTorch. Should you be making use of this work, please cite the paper accordingly. Also, make sure to adhere to the licensing terms of the authors. Should you be making use of this particular implementation, please acknowledge it appropriately [2].

For the original version of this work, please see: https://github.com/s9xie/hed
For another reimplementation based on Caffe, please see: https://github.com/zeakey/hed

To run it on your own image, use the following command. Please make sure to see their paper / the code for more details.

python run.py --model bsds500 --in ./images/sample.png --out ./out.png

I am afraid that this reimplementation is not entirely true to the original Caffe version, even though it utilizes the official weights. It achieves an ODS=0.774 versus the official ODS=0.780 on the BSDS500 dataset, evaluated using this code. Please feel free to contribute to this repository by submitting issues and pull requests.

[1]  @inproceedings{Xie_ICCV_2015,
         author = {Saining Xie and Zhuowen Tu},
         title = {Holistically-Nested Edge Detection},
         booktitle = {IEEE International Conference on Computer Vision},
         year = {2015}
     }

[2]  @misc{pytorch-hed,
         author = {Simon Niklaus},
         title = {A Reimplementation of {HED} Using {PyTorch}},
         year = {2018},
         howpublished = {\url{https://github.com/sniklaus/pytorch-hed}}
    }