EEDNet

EEDNet: A Bio-Inspired Efficient Edge Detection Network

All results is evaluated on Python 3.8 with PyTorch 1.11.0+cuda113 and MATLAB R2018b.
We only publish our test results on the BSDS500, NYUDv2 and Multicue datasets for now.
The implementation details of code will be updated after the paper is officially published.

Datasets

We use the links in RCF Repository (really thanks for that).
The augmented BSDS500, PASCAL VOC, and NYUD datasets can be downloaded with:

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

Multicue Dataset is Here

    https://drive.google.com/file/d/1-tyt_KyzlYc9APafdh5mHJzh2K_F2hM8/view?usp=sharing

Tools

The evaluation program of ODS OIS is here:

    https://github.com/pdollar/edges

The PR curve tool is here:

    https://github.com/MCG-NKU/plot-edge-pr-curves

The code for the visualization parameter L.

    # We will upload it later.

All the efficiency indicators are obtained on the T4 provided by the Colab platform:

    # We will prepare a notebook for training and reasoning about EEDNet 
    # You can run on the Colab platform with one click.
    
    https://colab.research.google.com/

Reference

When building our codeWe referenced the repositories as follow:

1. Pidinet
2. RCF
3. HED Implementation