An official implementation of "SFNet: Learning Object-aware Semantic Correspondence" (CVPR 2019, TPAMI 2020) in PyTorch.

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Deep Learning SFNet
Overview

PyTorch implementation of SFNet

no_image

This is the implementation of the paper "SFNet: Learning Object-aware Semantic Correspondence".

For more information, checkout the project site [website] and the paper [PDF].

Dependencies

  • Python 3.6
  • PyTorch >= 1.0.0
  • numpy
  • pandas

Datasets

  • Pascal VOC 2012 segmentation dataset (excluding images that overlap with the test split in the PF-PASCAL) for training
  • PF-Pascal & PF-WILLOW datasets for evaluation
  • All datasets are automatically downloaded into the data folder by running download_datasets.py
  • FYI: Without the need of downloading the entire data, the csv file of PF-PASCAL test split is available in the following link: [csv file]

Code

git clone https://github.com/cvlab-yonsei/SFNet.git
cd SFNet
python3 download_datasets.py # prepare the datasets for training/evaluation
python3 train.py # for training
python3 eval_pascal.py # evaluation on PF-Pascal dataset
python3 eval_willow.py # evaluation on PF-WILLOW dataset

Trained model

  • Download pre-trained weights into weights folder
    Link: [weights]

Bibtex

@inproceedings{lee2019sfnet,
  title={SFNet: Learning Object-aware Semantic Correspondence},
  author={Lee, Junghyup and Kim, Dohyung and Ponce, Jean and Ham, Bumsub},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2278--2287},
  year={2019}
}
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Comments
  • google drive link all invalid

    google drive link all invalid

    hello! I find your model weights download link and your datasets download links are all invalid! so could you provide new valid links? thank you very much!

    opened by lilyNight 0
  • matching probability m_p

    matching probability m_p

    I saw your code like below.

    def softmax_with_temperature(self, x, beta, d = 1): M, _ = x.max(dim=d, keepdim=True) x = x - M # subtract maximum value for stability exp_x = torch.exp(beta*x) exp_x_sum = exp_x.sum(dim=d, keepdim=True) return exp_x / exp_x_sum

    but, in your paper, m_p(q)= softmax(beta * kp * n_p(q)).

    According to your paper, It seems that x = x - M -> x = x * M more proper. Is it right?

    opened by sunwoo76 0
  • Hi, question about data preprocessing step.

    Hi, question about data preprocessing step. 

    mask = self.affine_transform(mask,affine1);
mask = self.affine_transform(mask, affine_inverse1) # convert truncated pixels to 0

    why do you convert warped mask to another mask by "grid sample". Can you use the initial originial mask variable? At the annotation, you said " convert truncated pixels to 0", but "grid sample" uses bilinear and it can't get binary output.

    opened by shoutOutYangJie 0
Owner
CV Lab @ Yonsei University
CV Lab @ Yonsei University
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