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SCNet Code

Region matching code is contributed by Kai Han (

Dense matching code is contributed by Rafael S. Rezende (

This code is written in MATLAB, and implements the SCNet[1]. For the dataset, see our project page:

Install Dependencies



Additional Matconvnet modules implemented for SCNet. These code should be copied into matconvnet/matlab/ folder.


This is the primary net work training and testing code.

  • SCNet_A_init.m, SCNet_AG_init.m, SCNet_AGplus_init.m: initialize the SCNet_A, SCNet_AG, SCNet_AG+.

  • SCNet_A.m, SCNet_AG.m, SCNet_AGplus.m: train SCNet_A, SCNet_AG, SCNet_AG+.

  • eva_PCR_mIoU_SCNet_A.m, eva_PCR_mIoU_SCNet_AG.m, eva_PCR_mIoU_SCNet_AGplus.m: evaluate the trained nets.

  • eva_PCR_mIoU_ImageNet_SCNet_A.m, eva_PCR_mIoU_ImageNet_SCNet_AG.m, eva_PCR_mIoU_ImageNet_SCNet_AGplus.m: evaluate SCNets with ImageNet pretrained parameters, i.e., SCNets without training.


Comparison code for our SCNet features and HOG features with NAM, PHM and LOM in Proposal Flow [2, 3].

  • NAM_HOG_eva.m, PHM_HOG_eva.m, LOM_HOG_eva.m: evaluate NAM, PHM, and LOM with HOG features.

  • NAM_SCNet_eva.m, PHM_SCNet_eva.m, LOM_SCNet_eva.m: evaluate NAM, PHM, and LOM with learned SCNet features.

  • HOG_SCNet_AG_eva.m: replace the learned SCNet feature by HOG feature in SCNet_AG model.


We used PF-PASCAL, PF-WILLOW, PASCAL Parts and CUB data sets and follows Proposal Flow[2, 3] to generate our trainging data.

Triaining data preparation code is put in PF-PASCAL-code folder.


  • The code is provided for academic use only. Use of the code in any commercial or industrial related activities is prohibited.
  • If you use our code or dataset, please cite the paper.
author = {Kai Han, Rafael S. Rezende, Bumsub Ham, Kwan-Yee K. Wong, Minsu Cho, Cordelia Schmid, and Jean Ponce},
title = {SCNet: Learning Semantic Correspondence},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2017}


[1] Kai Han, Rafael S. Rezende, Bumsub Ham, Kwan-Yee K. Wong, Minsu Cho, Cordelia Schmid, Jean Ponce, "SCNet: Learning Semantic Correspondence", International Conference on Computer Vision (ICCV), 2017.

[2] Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce, "Proposal Flow: Semantic Correspondences from Object Proposals", IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2017

[3] Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce, "Proposal Flow", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

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