/WSL

Weakly Supervised Object Localization with Progressive Domain Adaptation (CVPR 2016)

Primary LanguageC++OtherNOASSERTION

Weakly Supervised Object Localization with Progressive Domain Adaptation (CVPR 2016)

This is the research code for the paper:

Dong Li, Jia-Bin Huang, Yali Li, Shengjin Wang, and Ming-Hsuan Yang. "Weakly Supervised Object Localization with Progressive Domain Adaptation" In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

Project page

Citation

If you find the code and pre-trained models useful in your research, please consider citing:

@inproceedings{Huang-CVPR-2016,
  author  = {Dong, Li and Huang, Jia-Bin and Li, Yali and Wang, Shengjin and Yang, Ming-Hsuan},
  title   = {Weakly Supervised Object Localization with Progressive Domain Adaptation},
  booktitle = {Proceedings of the IEEE  Conference on Computer Vision and Pattern Recognition)},
  year    = {2015},
  volume  = {},
  number  = {},
  pages   = {}  
  }

System Requirements

  • MATLAB (tested with R2014a on 64-bit Linux)
  • Caffe

Installation

  1. Download and unzip the project code.

  2. Install caffe. We call the root directory of the project code WSL_ROOT.

    cd $WSL_ROOT/caffe-wsl
# Now follow the Caffe installation instructions here:
# http://caffe.berkeleyvision.org/installation.html
# If you're experienced with Caffe and have all of the requirements installed
# and your Makefile.config is in place, then simply do:
make all -j8
make pycaffe
make matcaffe

  3. Download the PASCAL VOC 2007 dataset. Extract all the tars into one directory named VOCdevkit. It should have this basic structure:

    $VOCdevkit/                           # development kit
$VOCdevkit/VOCcode/                   # VOC utility code
$VOCdevkit/VOC2007                    # image sets, annotations, etc.
# ... and several other directories ...
# Then create symlinks for the dataset:
cd $WSL_ROOT/data
ln -s $VOCdevkit VOCdevkit2007

  4. Download the pre-trained ImageNet model and put it into $WSL_ROOT/data/imagenet_models.

  5. Download the pre-computed EdgeBox proposals and put them into $WSL_ROOT/data/edgebox_data.

  1. Install the project.

    cd $WSL_ROOT
# Start MATLAB
matlab
>> startup

Usage

You will need about 150GB of disk space free for the feature cache (which is stored in $WSL_ROOT/cache by default. The final adapted model will be stored in $WSL_ROOT/output/default/voc_2007_trainval.

  1. Classification adaptation.

    >> prepare_for_cls_adapt
cd $WSL_ROOT
sh cls_adapt.sh

  2. Class-specific proposal mining.

    >> maskout

  3. MIL for confident proposal mining.

    >> mil

  4. Detection adaptation.

    >> prepare_for_det_adapt
cd $WSL_ROOT
sh det_adapt.sh

  5. Evaluation.

    cd $WSL_ROOT
sh test.sh