How to Use Extra Training Data for Better Edge Detection?

Introduction

This repository is an official implementation of paper How to Use Extra Training Data for Better Edge Detection?.

Abstract.

News

(15/03/2023) Upload codes and models.

Datasets

BSDS500

Download the augmented BSDS500 data from baidu(1234).

|-- data
    |-- BSDS
        |-- ImageSets
        |   |-- train_pair.txt
        |   |-- test.txt
        |   |-- pascal_train_pair.txt
        |-- train
        |   |-- aug_data
        |   |-- aug_data_scale_0.5
        |   |-- aug_data_scale_1.5
        |   |-- aug_gt
        |   |-- aug_gt_scale_0.5
        |   |-- aug_gt_scale_1.5
        |-- test
        |   |-- 2018.jpg
        ......

NYUD

Download the augmented NYUD data from baidu(1234).

|-- data
    |-- NYUD
        |-- ImageSets
        |   |-- train_pair.txt
        |   |-- test.txt
        |-- train
        |   |-- HHA
        |   |-- HHA_05
        |   |-- HHA_15
        |   |-- GT
        |   |-- GT_05
        |   |-- GT_15
        |   |-- Images
        |   |-- Images_05
        |   |-- Images_15
        |-- test
        |   |-- Images
        |   |-- GT
        ......

classify_BSDS、 BSDS+、Pascal-、Split_data、Pascal dataset

Download the classify_BSDS training data from baidu(1234).
Download the BSDS+ training data from baidu(mo8f).
Download the Pascal- training data from baidu(15d2).
Download the Split_data data from baidu(hsfj).
Download the Pascal dataset from baidu(1234).

Pre-trained model

If you are unable to download due to network reasons, you can download the pre-trained model from baidu(1234) and baidu(1234).

Training EDTER

The training of Stage I

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} 
# For example, train Stage I on BSDS500 dataset with 8 GPUs
./tools/dist_train.sh configs/bsds/EDTER_BIMLA_320x320_80k_bsds_bs_8.py 8

Testing

Single-scale testing

Change the '--config', '--checkpoint', and '--tmpdir' in test.py.

python test.py

Results

Method	Dataset	ODS	OIS	AP
Boosting-ED	BSDS	0.837	0.854	0.890
Boosting-ED	NYUD	0.778	0.793	0.801
Boosting-ED	Pascal	0.668	0.683	0.690

Requirements

• Windows, Python>=3.6, CUDA>=11.0, pytorch >= 1.7.1
• Windows, Python>=3.6, CUDA>=11.0, pytorch >= 1.7.1

Installation

• git clone https://github.com/wenya1994/Boosting-ED.git
• cd Boosting-ED

Performance

We provide some visualization results as follows to show our superiority.

Contacts

If you have any question about our work or this repository, please don't hesitate to contact us by emails.

• [yangwenya@hdu.edu.cn]
• [wuwen.hdu.cs@gmail.com]

Acknowledgments

• We thank the anonymous reviewers for valuable and inspiring comments and suggestions.
• Thanks to previous open-sourced repo:
  EDTER
  

Cite