Skip to content

PyTorch implementation of "Soft Proposal Networks for Weakly Supervised Object Localization", ICCV 2017.

License

Notifications You must be signed in to change notification settings

yeezhu/SPN.pytorch

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

PyTorch implementation of SPN

Soft Proposal Networks for Weakly Supervised Object Localization, ICCV 2017.

[Project Page] [Paper] [Supp]

[Torch code]

Requirements

Conda virtual environment is recommended: conda env create -f environment.yml

  • Python3.5
  • PyTorch: conda install pytorch torchvision -c soumith
  • Packages: torch, torchnet, numpy, tqdm

Usage

  1. Clone the SPN repository:

    git clone https://github.com/yeezhu/SPN.pytorch.git
  2. Download the backbone model VGG16 (exported from caffe model) and then the model path should be SPN.pytorch/demo/models/VGG16_ImageNet.pt.

  3. Install SPN:

    cd SPN.pytorch/spnlib
    bash make.sh
  4. Run the training demo:

    cd SPN.pytorch/demo
    bash runme.sh
  5. Run the testing demo: EvaluationDemo.ipynb Figure Note: To perform bbox localization on ImageNet, firstly download the SP_GoogleNet_ImageNet model and the annotations into imagenet_eval folder. Extraxt the annotations:

    cd SPN.pytorch/demo/evaluation/imagenet_eval
    tar zxvf ILSVRC2012_bbox_val_v3.tgz    

Citation

If you use the code in your research, please cite:

@INPROCEEDINGS{Zhu2017SPN,
    author = {Zhu, Yi and Zhou, Yanzhao and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin},
    title = {Soft Proposal Networks for Weakly Supervised Object Localization},
    booktitle = {ICCV},
    year = {2017}
}

Acknowledgement

In this project, we reimplemented SPN on PyTorch based on wildcat.pytorch. To keep consistency with the Torch version, we use the VGG16 model exported from caffe in fcn.pytorch.

About

PyTorch implementation of "Soft Proposal Networks for Weakly Supervised Object Localization", ICCV 2017.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published