SRN

This code is for the paper "Side-output Residual Network for Object Symmetry Detection in the Wild". pdf

SRN is build on Holistically-Nested Edge Detection (HED) [1] with Residual Unit (RU). RU is used to compute the residual between output image and side-output of SRN.

Installing

1. Install prerequisites for Caffe (http://caffe.berkeleyvision.org/installation.html#prequequisites).
2. Build HED (https://github.com/s9xie/hed). Supposing the root directory of HED is $HED.
3. Copy the folder SRN to $HED/example/.

Training

1. Download benchmark Sym-PASCAL trainning and testing set (OneDrive) or (BaiduYun). Our dataset Sym-PASCAL derived from PASCAL 2011 segmentation dataset [1]. The annotation and statistics are detailed in the Section 3 in our paper.
2. Download the Pre-trained VGG [3] model (VGG19). Copy it to $HED/example/SRN/
3. Change the dataset path in '$HED/example/SRN/train_val.prototxt'
4. Run solve.py in shell (or you could use IDE like Eclipse)

cd $HED/example/SRN/
python solver.py

Testing

1. Change the dataset path in $HED/example/SRNtest.py.
2. run SRNtest.py.

Evaluation

We use the evaluation code of [3] to draw the PR curve. The code can be download spb-mil.

NOTE: Before evaluation, the NMS is utilized. We use the NMS code in Piotr's edges-master.

Pre-trained SRN model on Sym-PASCAL

Pre-trained SRN model on Sym-PASCAL: (OneDrive) or (BaiduYun)

The PR curve data

Sym-PASCAL: (OneDrive) or (BaiduYun)

SYMMAX: (OneDrive) or (BaiduYun)

WH-SYMMAX: (OneDrive) or (BaiduYun) mostly taken from http://wei-shen.weebly.com/publications.html

SK506: (OneDrive) or (BaiduYun) mostly taken from http://wei-shen.weebly.com/publications.html

Ref

[1] S. Xie and Z. Tu. Holistically-nested edge detection. In International Conference on Computer Vision, 2015

[2] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results. http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html.

[3] S. Tsogkas and I. Kokkinos. Learning-based symmetry detection in natural images. In European Conference on Computer Vision